Nexus AI · Agent World

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_1904":1905,"_1906":1907},"routes/_app.x.$handle.$name",{"_101":100,"_32":46,"_109":91,"_27":110,"_13":14,"_111":-5,"_11":112,"_113":114,"_115":116,"_117":118,"_119":120,"_20":-5,"_121":-5,"_122":-5,"_123":25,"_124":125,"_126":60,"_127":-5,"_128":-5,"_129":130},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_44":45,"_32":46,"_39":47,"_48":-5,"_49":25,"_50":51,"_52":51,"_53":54,"_55":-5,"_18":56,"_57":58,"_59":60,"_61":-5,"_62":-5,"_63":64,"_65":66,"_67":68,"_69":70,"_71":72,"_73":-5,"_74":75,"_76":75,"_77":78,"_79":78,"_80":78,"_81":75,"_82":75,"_83":84,"_85":86,"_87":88,"_89":25},"version",1083,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":33,"_34":35,"_36":38,"_39":40,"_41":14,"_42":43,"_24":25},"previous",{"_30":31,"_32":33,"_34":35,"_36":37},"deleted",false,"deletedHere","path","api/llm.ts","latestPath","fileId","31190010-56ca-11f1-8403-ee650bb23af1","name","llm.ts","fileType","script","content","import { sqlite } from \"../db.ts\";\nimport { isSafePublicUrl } from \"./integrations/_shared.ts\";\nimport type { LLMConfig, ChatMessage } from \"../types.ts\";\n\n// --- Defaults -----------------------------------------------------------------\n\nconst DEFAULT_CONFIG: LLMConfig = {\n id: \"default\",\n provider: \"anthropic\",\n model: \"claude-sonnet-4-6\",\n api_key: \"\",\n endpoint: \"\",\n};\n\n// Provider - default model. Used to auto-correct on provider switch so users don't\n// accidentally send \"claude-sonnet-4-6\" to Gemini (which 400s).\nconst PROVIDER_DEFAULT_MODEL: Record = {\n anthropic: \"claude-sonnet-4-6\",\n openai: \"gpt-4o\",\n gemini: \"gemini-3-pro-preview\",\n custom: \"your-model\",\n};\nconst VALID_PROVIDERS = new Set([\"anthropic\", \"openai\", \"gemini\", \"custom\"]);\n\n// Map a provider name to its dedicated key column on llm_config.\nconst providerKeyCol = (p: string): string => {\n switch (p) {\n case \"anthropic\": return \"api_key_anthropic\";\n case \"openai\": return \"api_key_openai\";\n case \"gemini\": return \"api_key_gemini\";\n case \"custom\": return \"api_key_custom\";\n default: return \"api_key_anthropic\";\n }\n};\n\n// --- Helpers ------------------------------------------------------------------\n\nfunction rowToConfig(columns: string[], row: any[]): LLMConfig & { has_key?: Record } {\n const obj: Record = {};\n for (let i = 0; i < columns.length; i++) {\n obj[columns[i]] = row[i];\n }\n const provider = obj.provider ?? DEFAULT_CONFIG.provider;\n // Resolve api_key: prefer the per-provider column, fall back to legacy single api_key.\n const perProviderKey = obj[providerKeyCol(provider)];\n const api_key = (perProviderKey && String(perProviderKey)) || obj.api_key || \"\";\n const endpoint = provider === \"custom\" ? (obj.endpoint_custom || obj.endpoint || \"\") : (obj.endpoint || \"\");\n return {\n id: obj.id ?? \"default\",\n provider,\n model: obj.model ?? DEFAULT_CONFIG.model,\n api_key,\n endpoint,\n has_key: {\n anthropic: !!(obj.api_key_anthropic || (provider === \"anthropic\" && obj.api_key)),\n openai: !!(obj.api_key_openai || (provider === \"openai\" && obj.api_key)),\n gemini: !!(obj.api_key_gemini || (provider === \"gemini\" && obj.api_key)),\n custom: !!(obj.api_key_custom || (provider === \"custom\" && obj.api_key)),\n },\n } as any;\n}\n\n// --- DB operations ------------------------------------------------------------\n\nexport async function getLLMConfig(): Promise {\n const result = await sqlite.execute(\n `SELECT id, provider, model, api_key, endpoint,\n api_key_anthropic, api_key_openai, api_key_gemini, api_key_custom, endpoint_custom\n FROM llm_config WHERE id = 'default'`\n );\n\n if (!result.rows || result.rows.length === 0) {\n return { ...DEFAULT_CONFIG };\n }\n\n return rowToConfig(result.columns, result.rows[0]);\n}\n\nexport async function saveLLMConfig(\n config: Partial\n): Promise {\n const existing = await getLLMConfig();\n\n // Validate provider; fall back to existing if caller sent garbage.\n let provider = (config.provider ?? existing.provider) as string;\n if (!VALID_PROVIDERS.has(provider)) provider = existing.provider;\n\n // Auto-correct model when provider changes and caller didn't pick a sensible one.\n // Prevents \"model not found\" 400s when switching anthropic - gemini.\n let model = config.model ?? existing.model;\n if (provider !== existing.provider) {\n const userPickedNewModel = config.model && config.model !== existing.model;\n if (!userPickedNewModel) {\n model = PROVIDER_DEFAULT_MODEL[provider] ?? existing.model;\n }\n }\n\n // Per-provider key: if caller supplied api_key, route it to that provider's column.\n // Other providers' keys are preserved across switches.\n const incomingKey = config.api_key;\n const keyCol = providerKeyCol(provider);\n const endpoint = provider === \"custom\" ? (config.endpoint ?? existing.endpoint ?? \"\") : (existing.endpoint ?? \"\");\n\n const now = new Date().toISOString();\n // Upsert: keep existing per-provider columns, only overwrite the one(s) the user changed.\n await sqlite.execute(\n `INSERT INTO llm_config (id, provider, model, api_key, endpoint, ${keyCol}, endpoint_custom, updated_at)\n VALUES ('default', ?, ?, ?, ?, ?, ?, ?)\n ON CONFLICT(id) DO UPDATE SET\n provider = excluded.provider,\n model = excluded.model,\n api_key = excluded.api_key,\n endpoint = excluded.endpoint,\n ${keyCol} = COALESCE(NULLIF(excluded.${keyCol}, ''), llm_config.${keyCol}),\n endpoint_custom = COALESCE(NULLIF(excluded.endpoint_custom, ''), llm_config.endpoint_custom),\n updated_at = excluded.updated_at`,\n [\n provider,\n model,\n incomingKey ?? existing.api_key ?? \"\",\n endpoint,\n incomingKey ?? \"\",\n provider === \"custom\" ? endpoint : \"\",\n now,\n ]\n );\n\n return await getLLMConfig();\n}\n\n// --- LLM call -----------------------------------------------------------------\n\nexport async function callLLM(\n config: LLMConfig,\n messages: ChatMessage[],\n options?: {\n systemPrompt?: string;\n tools?: any[];\n maxTokens?: number;\n temperature?: number; // 0..1 - passed to provider\n frequencyPenalty?: number; // 0..2 - OpenAI/custom only (Anthropic ignores)\n grounded?: boolean; // AI-native grounding ON by default; false opts out (mechanical calls)\n groundingQuery?: string; // override the knowledge-retrieval topic (else derived from last user msg)\n }\n): Promise<{ content: string; tool_calls?: any[] }> {\n const maxTokens = options?.maxTokens ?? 4096;\n let systemPrompt = options?.systemPrompt;\n const tools = options?.tools;\n const temperature = options?.temperature;\n const frequencyPenalty = options?.frequencyPenalty;\n\n // AI-NATIVE GROUNDING: prepend the operating context (company + playbooks + knowledge base) to the\n // system prompt for EVERY call unless explicitly opted out. Resilient — never breaks the call.\n if (options?.grounded !== false) {\n try {\n const { getOperatingContext, lastUserText } = await import(\"./ai_context.ts\");\n const grounding = await getOperatingContext({ query: options?.groundingQuery || lastUserText(messages) });\n if (grounding) systemPrompt = grounding + (systemPrompt ? \"\\n\\n\" + systemPrompt : \"\");\n } catch { /* grounding must never break the LLM call */ }\n }\n\n if (config.provider === \"anthropic\") {\n return callAnthropic(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"openai\") {\n return callOpenAI(config, \"https://api.openai.com/v1\", messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else if (config.provider === \"gemini\") {\n return callGemini(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"custom\") {\n const endpoint = config.endpoint?.replace(/\\/+$/, \"\") ?? \"\";\n // SSRF guard: a custom LLM endpoint is user-supplied and fetched server-side with the API key.\n if (!isSafePublicUrl(endpoint)) {\n throw new Error(\"Custom LLM endpoint must be a public https:// URL\");\n }\n return callOpenAI(config, endpoint, messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else {\n throw new Error(`Unknown LLM provider: ${config.provider}`);\n }\n}\n\n// --- Streaming LLM call -------------------------------------------------------\n\nexport async function* callLLMStream(\n config: LLMConfig,\n messages: ChatMessage[],\n options?: {\n systemPrompt?: string;\n tools?: any[];\n maxTokens?: number;\n temperature?: number;\n frequencyPenalty?: number;\n grounded?: boolean;\n groundingQuery?: string;\n }\n): AsyncGenerator {\n const maxTokens = options?.maxTokens ?? 4096;\n let systemPrompt = options?.systemPrompt;\n const tools = options?.tools;\n const temperature = options?.temperature;\n const frequencyPenalty = options?.frequencyPenalty;\n\n // AI-NATIVE GROUNDING (streaming): same operating-context injection as callLLM. Resilient.\n if (options?.grounded !== false) {\n try {\n const { getOperatingContext, lastUserText } = await import(\"./ai_context.ts\");\n const grounding = await getOperatingContext({ query: options?.groundingQuery || lastUserText(messages) });\n if (grounding) systemPrompt = grounding + (systemPrompt ? \"\\n\\n\" + systemPrompt : \"\");\n } catch { /* grounding must never break the LLM call */ }\n }\n\n if (config.provider === \"anthropic\") {\n yield* callAnthropicStream(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"openai\") {\n yield* callOpenAIStream(config, \"https://api.openai.com/v1\", messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else if (config.provider === \"gemini\") {\n yield* callGeminiStream(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"custom\") {\n const endpoint = config.endpoint?.replace(/\\/+$/, \"\") ?? \"\";\n if (!isSafePublicUrl(endpoint)) {\n throw new Error(\"Custom LLM endpoint must be a public https:// URL\");\n }\n yield* callOpenAIStream(config, endpoint, messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else {\n throw new Error(`Unknown LLM provider: ${config.provider}`);\n }\n}\n\nasync function* parseSseStream(body: ReadableStream, onChunk: (parsed: any) => string | null): AsyncGenerator {\n const reader = body.getReader();\n const dec = new TextDecoder();\n let buf = \"\";\n const chunks: string[] = [];\n\n const flush = async function* () {\n while (chunks.length) {\n yield chunks.shift()!;\n }\n };\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n buf += dec.decode(value, { stream: true });\n const lines = buf.split(\"\\n\");\n buf = lines.pop()!;\n for (const line of lines) {\n if (!line.startsWith(\"data: \")) continue;\n const d = line.slice(6).trim();\n if (d === \"[DONE]\") return;\n try {\n const j = JSON.parse(d);\n const text = onChunk(j);\n if (text) yield text;\n } catch { /* skip malformed SSE lines */ }\n }\n }\n } finally {\n reader.cancel().catch(() => {});\n }\n}\n\nasync function* callAnthropicStream(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): AsyncGenerator {\n const body: Record = {\n model: config.model ?? \"claude-sonnet-4-6\",\n max_tokens: maxTokens,\n messages: toAnthropicMessages(messages),\n stream: true,\n };\n if (typeof temperature === \"number\") body.temperature = Math.max(0, Math.min(1, temperature));\n if (systemPrompt) body.system = systemPrompt;\n if (tools && tools.length > 0) {\n body.tools = tools.map((t) => {\n if (t.type === \"function\") {\n return { name: t.function.name, description: t.function.description ?? \"\", input_schema: t.function.parameters ?? { type: \"object\", properties: {} } };\n }\n return t;\n });\n }\n\n const res = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: { \"x-api-key\": config.api_key, \"anthropic-version\": \"2023-06-01\", \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n if (!res.ok) throw new Error(`Anthropic ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n if (j.type === \"content_block_delta\" && j.delta?.text) return j.delta.text;\n return null;\n });\n}\n\nasync function* callOpenAIStream(\n config: LLMConfig,\n baseUrl: string,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number,\n frequencyPenalty?: number\n): AsyncGenerator {\n const openAIMessages: any[] = [];\n if (systemPrompt) openAIMessages.push({ role: \"system\", content: systemPrompt });\n for (const m of messages as any[]) {\n if (m.role === \"assistant\" && m.tool_calls) {\n const msg: any = { role: \"assistant\", content: m.content ?? null, tool_calls: m.tool_calls };\n if (m.reasoning_content) msg.reasoning_content = m.reasoning_content;\n openAIMessages.push(msg);\n } else if (m.role === \"tool\") {\n openAIMessages.push({ role: \"tool\", tool_call_id: m.tool_call_id, content: String(m.content ?? \"\") });\n } else {\n openAIMessages.push({ role: m.role, content: m.content ?? \"\" });\n }\n }\n\n const body: Record = {\n model: config.model ?? \"gpt-4o\",\n messages: openAIMessages,\n max_tokens: maxTokens,\n temperature: typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 0.7,\n stream: true,\n };\n if (typeof frequencyPenalty === \"number\") body.frequency_penalty = Math.max(0, Math.min(2, frequencyPenalty));\n if (tools && tools.length > 0) { body.tools = tools; body.tool_choice = \"auto\"; }\n\n const res = await fetch(`${baseUrl}/chat/completions`, {\n method: \"POST\",\n headers: { Authorization: `Bearer ${config.api_key}`, \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n if (!res.ok) throw new Error(`OpenAI ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n const text = j.choices?.[0]?.delta?.content;\n if (text) return text;\n return null;\n });\n}\n\nasync function* callGeminiStream(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): AsyncGenerator {\n const model = config.model ?? \"gemini-3-pro-preview\";\n // Use ?alt=sse for server-sent events streaming\n const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:streamGenerateContent?alt=sse`;\n\n const tcNameMap: Record = {};\n for (const m of messages as any[]) {\n if (m.tool_calls) for (const tc of m.tool_calls) tcNameMap[tc.id] = tc.function.name;\n }\n\n const contents: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"system\") continue;\n if (m.role === \"tool\") {\n const fnName = tcNameMap[m.tool_call_id] ?? m.tool_call_id ?? \"tool\";\n const part = { functionResponse: { id: (m as any).tool_call_id, name: fnName, response: { content: m.content } } };\n const last = contents[contents.length - 1];\n if (last?.role === \"user\") last.parts.push(part);\n else contents.push({ role: \"user\", parts: [part] });\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const parts: any[] = [];\n if (m.content) parts.push({ text: m.content });\n for (const tc of m.tool_calls) {\n let args: any = {};\n try { args = typeof tc.function.arguments === \"string\" ? JSON.parse(tc.function.arguments) : (tc.function.arguments ?? {}); } catch { args = {}; }\n parts.push({ functionCall: { id: tc.id, name: tc.function.name, args } });\n }\n contents.push({ role: \"model\", parts });\n } else {\n const role = m.role === \"assistant\" ? \"model\" : \"user\";\n contents.push({ role, parts: [{ text: m.content }] });\n }\n }\n\n const genConfig: Record = { maxOutputTokens: maxTokens };\n genConfig.temperature = typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 1.0;\n genConfig.thinkingConfig = { thinkingLevel: \"medium\" };\n\n const reqBody: Record = { contents, generationConfig: genConfig };\n if (systemPrompt) reqBody.systemInstruction = { parts: [{ text: systemPrompt }] };\n if (tools && tools.length > 0) {\n reqBody.tools = [{ functionDeclarations: tools.map(t => { const fn = t.type === \"function\" ? t.function : t; return { name: fn.name, description: fn.description ?? \"\", parameters: stripUnsupported(fn.parameters ?? { type: \"object\", properties: {} }) }; }) }];\n reqBody.toolConfig = { functionCallingConfig: { mode: \"AUTO\" } };\n }\n\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", \"x-goog-api-key\": config.api_key },\n body: JSON.stringify(reqBody),\n });\n if (!res.ok) throw new Error(`Gemini ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n const text = j.candidates?.[0]?.content?.parts?.[0]?.text;\n if (text) return text;\n return null;\n });\n}\n\n// --- Anthropic ----------------------------------------------------------------\n\nfunction toAnthropicMessages(messages: ChatMessage[]): any[] {\n const result: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"tool\") {\n const block = { type: \"tool_result\", tool_use_id: m.tool_call_id, content: m.content };\n const prev = result[result.length - 1];\n if (prev && prev.role === \"user\" && Array.isArray(prev.content)) {\n prev.content.push(block);\n } else {\n result.push({ role: \"user\", content: [block] });\n }\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const content: any[] = [];\n if (m.content) content.push({ type: \"text\", text: m.content });\n for (const tc of m.tool_calls) {\n let input: any = {};\n try {\n input = typeof tc.function.arguments === \"string\"\n ? JSON.parse(tc.function.arguments) : tc.function.arguments ?? {};\n } catch { input = {}; }\n content.push({ type: \"tool_use\", id: tc.id, name: tc.function.name, input });\n }\n result.push({ role: \"assistant\", content });\n } else {\n result.push({ role: m.role, content: m.content });\n }\n }\n return result;\n}\n\nasync function callAnthropic(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n const body: Record = {\n model: config.model ?? \"claude-sonnet-4-6\",\n max_tokens: maxTokens,\n messages: toAnthropicMessages(messages),\n };\n if (typeof temperature === \"number\") body.temperature = Math.max(0, Math.min(1, temperature));\n\n if (systemPrompt) {\n body.system = systemPrompt;\n }\n\n if (tools && tools.length > 0) {\n // Convert OpenAI-style tools to Anthropic format if needed\n body.tools = tools.map((t) => {\n if (t.type === \"function\") {\n return {\n name: t.function.name,\n description: t.function.description ?? \"\",\n input_schema: t.function.parameters ?? { type: \"object\", properties: {} },\n };\n }\n // Already in Anthropic format\n return t;\n });\n }\n\n // 25s timeout per LLM call (see callOpenAI for rationale).\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: {\n \"x-api-key\": config.api_key,\n \"anthropic-version\": \"2023-06-01\",\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`Anthropic API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n\n // Extract text content and tool use blocks\n let textContent = \"\";\n const toolCalls: any[] = [];\n\n if (Array.isArray(data.content)) {\n for (const block of data.content) {\n if (block.type === \"text\") {\n textContent += block.text;\n } else if (block.type === \"tool_use\") {\n toolCalls.push({\n id: block.id,\n type: \"function\",\n function: {\n name: block.name,\n arguments: JSON.stringify(block.input),\n },\n });\n }\n }\n }\n\n return {\n content: textContent,\n tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n };\n}\n\n// --- OpenAI / OpenAI-compatible ----------------------------------------------\n\nasync function callOpenAI(\n config: LLMConfig,\n baseUrl: string,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number,\n frequencyPenalty?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n const openAIMessages: any[] = [];\n\n if (systemPrompt) {\n openAIMessages.push({ role: \"system\", content: systemPrompt });\n }\n\n for (const m of messages as any[]) {\n if (m.role === \"assistant\" && m.tool_calls) {\n const msg: any = { role: \"assistant\", content: m.content ?? null, tool_calls: m.tool_calls };\n // DeepSeek thinking mode: reasoning_content must be echoed back for multi-turn\n if (m.reasoning_content) msg.reasoning_content = m.reasoning_content;\n openAIMessages.push(msg);\n } else if (m.role === \"tool\") {\n openAIMessages.push({ role: \"tool\", tool_call_id: m.tool_call_id, content: String(m.content ?? \"\") });\n } else {\n openAIMessages.push({ role: m.role, content: m.content ?? \"\" });\n }\n }\n\n const body: Record = {\n model: config.model ?? \"gpt-4o\",\n messages: openAIMessages,\n max_tokens: maxTokens,\n temperature: typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 0.7,\n };\n if (typeof frequencyPenalty === \"number\") body.frequency_penalty = Math.max(0, Math.min(2, frequencyPenalty));\n\n if (tools && tools.length > 0) {\n body.tools = tools;\n body.tool_choice = \"auto\";\n }\n\n // Hard 25s timeout per LLM call. Without this, a slow/unreachable provider hangs\n // the whole chat handler past Val Town's 60s execution cap and triggers a pause.\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(`${baseUrl}/chat/completions`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${config.api_key}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`OpenAI API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n const choice = data.choices?.[0];\n const message = choice?.message;\n\n const content: string = message?.content ?? \"\";\n const tool_calls: any[] | undefined =\n message?.tool_calls && message.tool_calls.length > 0\n ? message.tool_calls\n : undefined;\n // DeepSeek thinking mode: preserve reasoning_content so it can be echoed back in multi-turn\n const reasoning_content: string | undefined = message?.reasoning_content ?? undefined;\n\n return { content, tool_calls, reasoning_content };\n}\n\n// --- Gemini (native REST API) -------------------------------------------------\n\n// Gemini rejects additionalProperties and other JSON Schema fields it doesn't know\nfunction stripUnsupported(schema: any): any {\n if (!schema || typeof schema !== \"object\") return schema;\n const { additionalProperties, $schema, ...rest } = schema;\n if (rest.properties) {\n rest.properties = Object.fromEntries(\n Object.entries(rest.properties).map(([k, v]) => [k, stripUnsupported(v)])\n );\n }\n if (rest.items) rest.items = stripUnsupported(rest.items);\n return rest;\n}\n\nasync function callGemini(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n // Gemini 3 migration: default model is now gemini-3-pro-preview (gemini-1.5-flash deprecated).\n // Auth moved from ?key= query to the x-goog-api-key header (canonical).\n const model = config.model ?? \"gemini-3-pro-preview\";\n const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent`;\n\n // Build tool_call_id -> function name map so functionResponse can use the name\n const tcNameMap: Record = {};\n for (const m of messages as any[]) {\n if (m.tool_calls) {\n for (const tc of m.tool_calls) {\n tcNameMap[tc.id] = tc.function.name;\n }\n }\n }\n\n const contents: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"system\") continue;\n\n if (m.role === \"tool\") {\n const fnName = tcNameMap[m.tool_call_id] ?? m.tool_call_id ?? \"tool\";\n // id must match the corresponding FunctionCall id (required in Gemini 3.x)\n const part = { functionResponse: { id: (m as any).tool_call_id, name: fnName, response: { content: m.content } } };\n const last = contents[contents.length - 1];\n if (last?.role === \"user\") {\n last.parts.push(part);\n } else {\n contents.push({ role: \"user\", parts: [part] });\n }\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const parts: any[] = [];\n if (m.content) parts.push({ text: m.content });\n for (const tc of m.tool_calls) {\n let args: any = {};\n try { args = typeof tc.function.arguments === \"string\" ? JSON.parse(tc.function.arguments) : (tc.function.arguments ?? {}); } catch { args = {}; }\n const fcPart: any = { functionCall: { id: tc.id, name: tc.function.name, args } };\n // Echo thoughtSignature exactly as received - required by Gemini 2.x thinking models\n if (tc._gemini_thought_signature) fcPart.thoughtSignature = tc._gemini_thought_signature;\n parts.push(fcPart);\n }\n contents.push({ role: \"model\", parts });\n } else {\n const role = m.role === \"assistant\" ? \"model\" : \"user\";\n contents.push({ role, parts: [{ text: m.content }] });\n }\n }\n\n // Gemini 3 defaults: temperature 1.0 (research confirms lowering it degrades reasoning).\n // We honor an explicit caller override only when in [0,2] (Gemini 3 expanded range).\n const genConfig: Record = { maxOutputTokens: maxTokens };\n genConfig.temperature = typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 1.0;\n // Gemini 3 introduced thinkingLevel (\"minimal\"|\"low\"|\"medium\"|\"high\") replacing the\n // numeric thinking_budget. We default to \"medium\" - Gemini 3's reasoning mode.\n genConfig.thinkingConfig = { thinkingLevel: \"medium\" };\n\n const body: Record = {\n contents,\n generationConfig: genConfig,\n };\n\n if (systemPrompt) {\n body.systemInstruction = { parts: [{ text: systemPrompt }] };\n }\n\n if (tools && tools.length > 0) {\n body.tools = [{\n functionDeclarations: tools.map(t => {\n const fn = t.type === \"function\" ? t.function : t;\n return { name: fn.name, description: fn.description ?? \"\", parameters: stripUnsupported(fn.parameters ?? { type: \"object\", properties: {} }) };\n }),\n }];\n body.toolConfig = { functionCallingConfig: { mode: \"AUTO\" } };\n }\n\n // 25s timeout per LLM call (see callOpenAI for rationale).\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n // Gemini 3 canonical auth - header replaces the legacy ?key= query param.\n \"x-goog-api-key\": config.api_key,\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`Gemini API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n const parts = data.candidates?.[0]?.content?.parts ?? [];\n\n let textContent = \"\";\n const toolCalls: any[] = [];\n\n for (const part of parts) {\n if (part.text) textContent += part.text;\n else if (part.functionCall) {\n // Use the model's own id - must be echoed exactly in the functionResponse\n const tc: any = {\n id: part.functionCall.id ?? `gemini_${Date.now()}_${toolCalls.length}`,\n type: \"function\",\n function: {\n name: part.functionCall.name,\n arguments: JSON.stringify(part.functionCall.args ?? {}),\n },\n };\n // Preserve thoughtSignature (Part-level field) - required by Gemini 2.x thinking models\n const sig = part.thoughtSignature ?? part.thought_signature;\n if (sig) tc._gemini_thought_signature = sig;\n toolCalls.push(tc);\n }\n }\n\n return { content: textContent, tool_calls: toolCalls.length > 0 ? toolCalls : undefined };\n}\n","import { sqlite, currentTenantId } from \"../db.ts\";\nimport { isSafePublicUrl } from \"./integrations/_shared.ts\";\nimport type { LLMConfig, ChatMessage } from \"../types.ts\";\n\n// --- Defaults -----------------------------------------------------------------\n\nconst DEFAULT_CONFIG: LLMConfig = {\n id: \"default\",\n provider: \"anthropic\",\n model: \"claude-sonnet-4-6\",\n api_key: \"\",\n endpoint: \"\",\n};\n\n// Provider - default model. Used to auto-correct on provider switch so users don't\n// accidentally send \"claude-sonnet-4-6\" to Gemini (which 400s).\nconst PROVIDER_DEFAULT_MODEL: Record = {\n anthropic: \"claude-sonnet-4-6\",\n openai: \"gpt-4o\",\n gemini: \"gemini-3-pro-preview\",\n custom: \"your-model\",\n};\nconst VALID_PROVIDERS = new Set([\"anthropic\", \"openai\", \"gemini\", \"custom\"]);\n\n// Map a provider name to its dedicated key column on llm_config.\nconst providerKeyCol = (p: string): string => {\n switch (p) {\n case \"anthropic\": return \"api_key_anthropic\";\n case \"openai\": return \"api_key_openai\";\n case \"gemini\": return \"api_key_gemini\";\n case \"custom\": return \"api_key_custom\";\n default: return \"api_key_anthropic\";\n }\n};\n\n// --- Helpers ------------------------------------------------------------------\n\nfunction rowToConfig(columns: string[], row: any[]): LLMConfig & { has_key?: Record } {\n const obj: Record = {};\n for (let i = 0; i < columns.length; i++) {\n obj[columns[i]] = row[i];\n }\n const provider = obj.provider ?? DEFAULT_CONFIG.provider;\n // Resolve api_key: prefer the per-provider column, fall back to legacy single api_key.\n const perProviderKey = obj[providerKeyCol(provider)];\n const api_key = (perProviderKey && String(perProviderKey)) || obj.api_key || \"\";\n const endpoint = provider === \"custom\" ? (obj.endpoint_custom || obj.endpoint || \"\") : (obj.endpoint || \"\");\n return {\n id: obj.id ?? \"default\",\n provider,\n model: obj.model ?? DEFAULT_CONFIG.model,\n api_key,\n endpoint,\n has_key: {\n anthropic: !!(obj.api_key_anthropic || (provider === \"anthropic\" && obj.api_key)),\n openai: !!(obj.api_key_openai || (provider === \"openai\" && obj.api_key)),\n gemini: !!(obj.api_key_gemini || (provider === \"gemini\" && obj.api_key)),\n custom: !!(obj.api_key_custom || (provider === \"custom\" && obj.api_key)),\n },\n } as any;\n}\n\n// --- DB operations ------------------------------------------------------------\n\nexport async function getLLMConfig(): Promise {\n // Per-tenant: one llm_config row per tenant, keyed on tenant_id.\n const result = await sqlite.execute(\n `SELECT id, provider, model, api_key, endpoint,\n api_key_anthropic, api_key_openai, api_key_gemini, api_key_custom, endpoint_custom\n FROM llm_config WHERE tenant_id = ?`,\n [currentTenantId()]\n );\n\n if (!result.rows || result.rows.length === 0) {\n return { ...DEFAULT_CONFIG };\n }\n\n return rowToConfig(result.columns, result.rows[0]);\n}\n\nexport async function saveLLMConfig(\n config: Partial\n): Promise {\n const existing = await getLLMConfig();\n\n // Validate provider; fall back to existing if caller sent garbage.\n let provider = (config.provider ?? existing.provider) as string;\n if (!VALID_PROVIDERS.has(provider)) provider = existing.provider;\n\n // Auto-correct model when provider changes and caller didn't pick a sensible one.\n // Prevents \"model not found\" 400s when switching anthropic - gemini.\n let model = config.model ?? existing.model;\n if (provider !== existing.provider) {\n const userPickedNewModel = config.model && config.model !== existing.model;\n if (!userPickedNewModel) {\n model = PROVIDER_DEFAULT_MODEL[provider] ?? existing.model;\n }\n }\n\n // Per-provider key: if caller supplied api_key, route it to that provider's column.\n // Other providers' keys are preserved across switches.\n const incomingKey = config.api_key;\n const keyCol = providerKeyCol(provider);\n const endpoint = provider === \"custom\" ? (config.endpoint ?? existing.endpoint ?? \"\") : (existing.endpoint ?? \"\");\n\n const now = new Date().toISOString();\n const tenantId = currentTenantId();\n // Per-tenant upsert: one row per tenant keyed on tenant_id. Keep existing\n // per-provider columns, only overwrite the one(s) the user changed.\n await sqlite.execute(\n `INSERT INTO llm_config (tenant_id, id, provider, model, api_key, endpoint, ${keyCol}, endpoint_custom, updated_at)\n VALUES (?, 'default', ?, ?, ?, ?, ?, ?, ?)\n ON CONFLICT(tenant_id) DO UPDATE SET\n provider = excluded.provider,\n model = excluded.model,\n api_key = excluded.api_key,\n endpoint = excluded.endpoint,\n ${keyCol} = COALESCE(NULLIF(excluded.${keyCol}, ''), llm_config.${keyCol}),\n endpoint_custom = COALESCE(NULLIF(excluded.endpoint_custom, ''), llm_config.endpoint_custom),\n updated_at = excluded.updated_at`,\n [\n tenantId,\n provider,\n model,\n incomingKey ?? existing.api_key ?? \"\",\n endpoint,\n incomingKey ?? \"\",\n provider === \"custom\" ? endpoint : \"\",\n now,\n ]\n );\n\n return await getLLMConfig();\n}\n\n// --- LLM call -----------------------------------------------------------------\n\nexport async function callLLM(\n config: LLMConfig,\n messages: ChatMessage[],\n options?: {\n systemPrompt?: string;\n tools?: any[];\n maxTokens?: number;\n temperature?: number; // 0..1 - passed to provider\n frequencyPenalty?: number; // 0..2 - OpenAI/custom only (Anthropic ignores)\n grounded?: boolean; // AI-native grounding ON by default; false opts out (mechanical calls)\n groundingQuery?: string; // override the knowledge-retrieval topic (else derived from last user msg)\n }\n): Promise<{ content: string; tool_calls?: any[] }> {\n const maxTokens = options?.maxTokens ?? 4096;\n let systemPrompt = options?.systemPrompt;\n const tools = options?.tools;\n const temperature = options?.temperature;\n const frequencyPenalty = options?.frequencyPenalty;\n\n // AI-NATIVE GROUNDING: prepend the operating context (company + playbooks + knowledge base) to the\n // system prompt for EVERY call unless explicitly opted out. Resilient — never breaks the call.\n if (options?.grounded !== false) {\n try {\n const { getOperatingContext, lastUserText } = await import(\"./ai_context.ts\");\n const grounding = await getOperatingContext({ query: options?.groundingQuery || lastUserText(messages) });\n if (grounding) systemPrompt = grounding + (systemPrompt ? \"\\n\\n\" + systemPrompt : \"\");\n } catch { /* grounding must never break the LLM call */ }\n }\n\n if (config.provider === \"anthropic\") {\n return callAnthropic(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"openai\") {\n return callOpenAI(config, \"https://api.openai.com/v1\", messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else if (config.provider === \"gemini\") {\n return callGemini(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"custom\") {\n const endpoint = config.endpoint?.replace(/\\/+$/, \"\") ?? \"\";\n // SSRF guard: a custom LLM endpoint is user-supplied and fetched server-side with the API key.\n if (!isSafePublicUrl(endpoint)) {\n throw new Error(\"Custom LLM endpoint must be a public https:// URL\");\n }\n return callOpenAI(config, endpoint, messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else {\n throw new Error(`Unknown LLM provider: ${config.provider}`);\n }\n}\n\n// --- Streaming LLM call -------------------------------------------------------\n\nexport async function* callLLMStream(\n config: LLMConfig,\n messages: ChatMessage[],\n options?: {\n systemPrompt?: string;\n tools?: any[];\n maxTokens?: number;\n temperature?: number;\n frequencyPenalty?: number;\n grounded?: boolean;\n groundingQuery?: string;\n }\n): AsyncGenerator {\n const maxTokens = options?.maxTokens ?? 4096;\n let systemPrompt = options?.systemPrompt;\n const tools = options?.tools;\n const temperature = options?.temperature;\n const frequencyPenalty = options?.frequencyPenalty;\n\n // AI-NATIVE GROUNDING (streaming): same operating-context injection as callLLM. Resilient.\n if (options?.grounded !== false) {\n try {\n const { getOperatingContext, lastUserText } = await import(\"./ai_context.ts\");\n const grounding = await getOperatingContext({ query: options?.groundingQuery || lastUserText(messages) });\n if (grounding) systemPrompt = grounding + (systemPrompt ? \"\\n\\n\" + systemPrompt : \"\");\n } catch { /* grounding must never break the LLM call */ }\n }\n\n if (config.provider === \"anthropic\") {\n yield* callAnthropicStream(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"openai\") {\n yield* callOpenAIStream(config, \"https://api.openai.com/v1\", messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else if (config.provider === \"gemini\") {\n yield* callGeminiStream(config, messages, systemPrompt, tools, maxTokens, temperature);\n } else if (config.provider === \"custom\") {\n const endpoint = config.endpoint?.replace(/\\/+$/, \"\") ?? \"\";\n if (!isSafePublicUrl(endpoint)) {\n throw new Error(\"Custom LLM endpoint must be a public https:// URL\");\n }\n yield* callOpenAIStream(config, endpoint, messages, systemPrompt, tools, maxTokens, temperature, frequencyPenalty);\n } else {\n throw new Error(`Unknown LLM provider: ${config.provider}`);\n }\n}\n\nasync function* parseSseStream(body: ReadableStream, onChunk: (parsed: any) => string | null): AsyncGenerator {\n const reader = body.getReader();\n const dec = new TextDecoder();\n let buf = \"\";\n const chunks: string[] = [];\n\n const flush = async function* () {\n while (chunks.length) {\n yield chunks.shift()!;\n }\n };\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n buf += dec.decode(value, { stream: true });\n const lines = buf.split(\"\\n\");\n buf = lines.pop()!;\n for (const line of lines) {\n if (!line.startsWith(\"data: \")) continue;\n const d = line.slice(6).trim();\n if (d === \"[DONE]\") return;\n try {\n const j = JSON.parse(d);\n const text = onChunk(j);\n if (text) yield text;\n } catch { /* skip malformed SSE lines */ }\n }\n }\n } finally {\n reader.cancel().catch(() => {});\n }\n}\n\nasync function* callAnthropicStream(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): AsyncGenerator {\n const body: Record = {\n model: config.model ?? \"claude-sonnet-4-6\",\n max_tokens: maxTokens,\n messages: toAnthropicMessages(messages),\n stream: true,\n };\n if (typeof temperature === \"number\") body.temperature = Math.max(0, Math.min(1, temperature));\n if (systemPrompt) body.system = systemPrompt;\n if (tools && tools.length > 0) {\n body.tools = tools.map((t) => {\n if (t.type === \"function\") {\n return { name: t.function.name, description: t.function.description ?? \"\", input_schema: t.function.parameters ?? { type: \"object\", properties: {} } };\n }\n return t;\n });\n }\n\n const res = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: { \"x-api-key\": config.api_key, \"anthropic-version\": \"2023-06-01\", \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n if (!res.ok) throw new Error(`Anthropic ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n if (j.type === \"content_block_delta\" && j.delta?.text) return j.delta.text;\n return null;\n });\n}\n\nasync function* callOpenAIStream(\n config: LLMConfig,\n baseUrl: string,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number,\n frequencyPenalty?: number\n): AsyncGenerator {\n const openAIMessages: any[] = [];\n if (systemPrompt) openAIMessages.push({ role: \"system\", content: systemPrompt });\n for (const m of messages as any[]) {\n if (m.role === \"assistant\" && m.tool_calls) {\n const msg: any = { role: \"assistant\", content: m.content ?? null, tool_calls: m.tool_calls };\n if (m.reasoning_content) msg.reasoning_content = m.reasoning_content;\n openAIMessages.push(msg);\n } else if (m.role === \"tool\") {\n openAIMessages.push({ role: \"tool\", tool_call_id: m.tool_call_id, content: String(m.content ?? \"\") });\n } else {\n openAIMessages.push({ role: m.role, content: m.content ?? \"\" });\n }\n }\n\n const body: Record = {\n model: config.model ?? \"gpt-4o\",\n messages: openAIMessages,\n max_tokens: maxTokens,\n temperature: typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 0.7,\n stream: true,\n };\n if (typeof frequencyPenalty === \"number\") body.frequency_penalty = Math.max(0, Math.min(2, frequencyPenalty));\n if (tools && tools.length > 0) { body.tools = tools; body.tool_choice = \"auto\"; }\n\n const res = await fetch(`${baseUrl}/chat/completions`, {\n method: \"POST\",\n headers: { Authorization: `Bearer ${config.api_key}`, \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n if (!res.ok) throw new Error(`OpenAI ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n const text = j.choices?.[0]?.delta?.content;\n if (text) return text;\n return null;\n });\n}\n\nasync function* callGeminiStream(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): AsyncGenerator {\n const model = config.model ?? \"gemini-3-pro-preview\";\n // Use ?alt=sse for server-sent events streaming\n const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:streamGenerateContent?alt=sse`;\n\n const tcNameMap: Record = {};\n for (const m of messages as any[]) {\n if (m.tool_calls) for (const tc of m.tool_calls) tcNameMap[tc.id] = tc.function.name;\n }\n\n const contents: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"system\") continue;\n if (m.role === \"tool\") {\n const fnName = tcNameMap[m.tool_call_id] ?? m.tool_call_id ?? \"tool\";\n const part = { functionResponse: { id: (m as any).tool_call_id, name: fnName, response: { content: m.content } } };\n const last = contents[contents.length - 1];\n if (last?.role === \"user\") last.parts.push(part);\n else contents.push({ role: \"user\", parts: [part] });\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const parts: any[] = [];\n if (m.content) parts.push({ text: m.content });\n for (const tc of m.tool_calls) {\n let args: any = {};\n try { args = typeof tc.function.arguments === \"string\" ? JSON.parse(tc.function.arguments) : (tc.function.arguments ?? {}); } catch { args = {}; }\n parts.push({ functionCall: { id: tc.id, name: tc.function.name, args } });\n }\n contents.push({ role: \"model\", parts });\n } else {\n const role = m.role === \"assistant\" ? \"model\" : \"user\";\n contents.push({ role, parts: [{ text: m.content }] });\n }\n }\n\n const genConfig: Record = { maxOutputTokens: maxTokens };\n genConfig.temperature = typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 1.0;\n genConfig.thinkingConfig = { thinkingLevel: \"medium\" };\n\n const reqBody: Record = { contents, generationConfig: genConfig };\n if (systemPrompt) reqBody.systemInstruction = { parts: [{ text: systemPrompt }] };\n if (tools && tools.length > 0) {\n reqBody.tools = [{ functionDeclarations: tools.map(t => { const fn = t.type === \"function\" ? t.function : t; return { name: fn.name, description: fn.description ?? \"\", parameters: stripUnsupported(fn.parameters ?? { type: \"object\", properties: {} }) }; }) }];\n reqBody.toolConfig = { functionCallingConfig: { mode: \"AUTO\" } };\n }\n\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", \"x-goog-api-key\": config.api_key },\n body: JSON.stringify(reqBody),\n });\n if (!res.ok) throw new Error(`Gemini ${res.status}: ${await res.text()}`);\n\n yield* parseSseStream(res.body!, (j) => {\n const text = j.candidates?.[0]?.content?.parts?.[0]?.text;\n if (text) return text;\n return null;\n });\n}\n\n// --- Anthropic ----------------------------------------------------------------\n\nfunction toAnthropicMessages(messages: ChatMessage[]): any[] {\n const result: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"tool\") {\n const block = { type: \"tool_result\", tool_use_id: m.tool_call_id, content: m.content };\n const prev = result[result.length - 1];\n if (prev && prev.role === \"user\" && Array.isArray(prev.content)) {\n prev.content.push(block);\n } else {\n result.push({ role: \"user\", content: [block] });\n }\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const content: any[] = [];\n if (m.content) content.push({ type: \"text\", text: m.content });\n for (const tc of m.tool_calls) {\n let input: any = {};\n try {\n input = typeof tc.function.arguments === \"string\"\n ? JSON.parse(tc.function.arguments) : tc.function.arguments ?? {};\n } catch { input = {}; }\n content.push({ type: \"tool_use\", id: tc.id, name: tc.function.name, input });\n }\n result.push({ role: \"assistant\", content });\n } else {\n result.push({ role: m.role, content: m.content });\n }\n }\n return result;\n}\n\nasync function callAnthropic(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n const body: Record = {\n model: config.model ?? \"claude-sonnet-4-6\",\n max_tokens: maxTokens,\n messages: toAnthropicMessages(messages),\n };\n if (typeof temperature === \"number\") body.temperature = Math.max(0, Math.min(1, temperature));\n\n if (systemPrompt) {\n body.system = systemPrompt;\n }\n\n if (tools && tools.length > 0) {\n // Convert OpenAI-style tools to Anthropic format if needed\n body.tools = tools.map((t) => {\n if (t.type === \"function\") {\n return {\n name: t.function.name,\n description: t.function.description ?? \"\",\n input_schema: t.function.parameters ?? { type: \"object\", properties: {} },\n };\n }\n // Already in Anthropic format\n return t;\n });\n }\n\n // 25s timeout per LLM call (see callOpenAI for rationale).\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: {\n \"x-api-key\": config.api_key,\n \"anthropic-version\": \"2023-06-01\",\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`Anthropic API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n\n // Extract text content and tool use blocks\n let textContent = \"\";\n const toolCalls: any[] = [];\n\n if (Array.isArray(data.content)) {\n for (const block of data.content) {\n if (block.type === \"text\") {\n textContent += block.text;\n } else if (block.type === \"tool_use\") {\n toolCalls.push({\n id: block.id,\n type: \"function\",\n function: {\n name: block.name,\n arguments: JSON.stringify(block.input),\n },\n });\n }\n }\n }\n\n return {\n content: textContent,\n tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n };\n}\n\n// --- OpenAI / OpenAI-compatible ----------------------------------------------\n\nasync function callOpenAI(\n config: LLMConfig,\n baseUrl: string,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number,\n frequencyPenalty?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n const openAIMessages: any[] = [];\n\n if (systemPrompt) {\n openAIMessages.push({ role: \"system\", content: systemPrompt });\n }\n\n for (const m of messages as any[]) {\n if (m.role === \"assistant\" && m.tool_calls) {\n const msg: any = { role: \"assistant\", content: m.content ?? null, tool_calls: m.tool_calls };\n // DeepSeek thinking mode: reasoning_content must be echoed back for multi-turn\n if (m.reasoning_content) msg.reasoning_content = m.reasoning_content;\n openAIMessages.push(msg);\n } else if (m.role === \"tool\") {\n openAIMessages.push({ role: \"tool\", tool_call_id: m.tool_call_id, content: String(m.content ?? \"\") });\n } else {\n openAIMessages.push({ role: m.role, content: m.content ?? \"\" });\n }\n }\n\n const body: Record = {\n model: config.model ?? \"gpt-4o\",\n messages: openAIMessages,\n max_tokens: maxTokens,\n temperature: typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 0.7,\n };\n if (typeof frequencyPenalty === \"number\") body.frequency_penalty = Math.max(0, Math.min(2, frequencyPenalty));\n\n if (tools && tools.length > 0) {\n body.tools = tools;\n body.tool_choice = \"auto\";\n }\n\n // Hard 25s timeout per LLM call. Without this, a slow/unreachable provider hangs\n // the whole chat handler past Val Town's 60s execution cap and triggers a pause.\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(`${baseUrl}/chat/completions`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${config.api_key}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`OpenAI API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n const choice = data.choices?.[0];\n const message = choice?.message;\n\n const content: string = message?.content ?? \"\";\n const tool_calls: any[] | undefined =\n message?.tool_calls && message.tool_calls.length > 0\n ? message.tool_calls\n : undefined;\n // DeepSeek thinking mode: preserve reasoning_content so it can be echoed back in multi-turn\n const reasoning_content: string | undefined = message?.reasoning_content ?? undefined;\n\n return { content, tool_calls, reasoning_content };\n}\n\n// --- Gemini (native REST API) -------------------------------------------------\n\n// Gemini rejects additionalProperties and other JSON Schema fields it doesn't know\nfunction stripUnsupported(schema: any): any {\n if (!schema || typeof schema !== \"object\") return schema;\n const { additionalProperties, $schema, ...rest } = schema;\n if (rest.properties) {\n rest.properties = Object.fromEntries(\n Object.entries(rest.properties).map(([k, v]) => [k, stripUnsupported(v)])\n );\n }\n if (rest.items) rest.items = stripUnsupported(rest.items);\n return rest;\n}\n\nasync function callGemini(\n config: LLMConfig,\n messages: ChatMessage[],\n systemPrompt?: string,\n tools?: any[],\n maxTokens = 4096,\n temperature?: number\n): Promise<{ content: string; tool_calls?: any[] }> {\n // Gemini 3 migration: default model is now gemini-3-pro-preview (gemini-1.5-flash deprecated).\n // Auth moved from ?key= query to the x-goog-api-key header (canonical).\n const model = config.model ?? \"gemini-3-pro-preview\";\n const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent`;\n\n // Build tool_call_id -> function name map so functionResponse can use the name\n const tcNameMap: Record = {};\n for (const m of messages as any[]) {\n if (m.tool_calls) {\n for (const tc of m.tool_calls) {\n tcNameMap[tc.id] = tc.function.name;\n }\n }\n }\n\n const contents: any[] = [];\n for (const m of messages as any[]) {\n if (m.role === \"system\") continue;\n\n if (m.role === \"tool\") {\n const fnName = tcNameMap[m.tool_call_id] ?? m.tool_call_id ?? \"tool\";\n // id must match the corresponding FunctionCall id (required in Gemini 3.x)\n const part = { functionResponse: { id: (m as any).tool_call_id, name: fnName, response: { content: m.content } } };\n const last = contents[contents.length - 1];\n if (last?.role === \"user\") {\n last.parts.push(part);\n } else {\n contents.push({ role: \"user\", parts: [part] });\n }\n } else if (m.role === \"assistant\" && m.tool_calls?.length) {\n const parts: any[] = [];\n if (m.content) parts.push({ text: m.content });\n for (const tc of m.tool_calls) {\n let args: any = {};\n try { args = typeof tc.function.arguments === \"string\" ? JSON.parse(tc.function.arguments) : (tc.function.arguments ?? {}); } catch { args = {}; }\n const fcPart: any = { functionCall: { id: tc.id, name: tc.function.name, args } };\n // Echo thoughtSignature exactly as received - required by Gemini 2.x thinking models\n if (tc._gemini_thought_signature) fcPart.thoughtSignature = tc._gemini_thought_signature;\n parts.push(fcPart);\n }\n contents.push({ role: \"model\", parts });\n } else {\n const role = m.role === \"assistant\" ? \"model\" : \"user\";\n contents.push({ role, parts: [{ text: m.content }] });\n }\n }\n\n // Gemini 3 defaults: temperature 1.0 (research confirms lowering it degrades reasoning).\n // We honor an explicit caller override only when in [0,2] (Gemini 3 expanded range).\n const genConfig: Record = { maxOutputTokens: maxTokens };\n genConfig.temperature = typeof temperature === \"number\" ? Math.max(0, Math.min(2, temperature)) : 1.0;\n // Gemini 3 introduced thinkingLevel (\"minimal\"|\"low\"|\"medium\"|\"high\") replacing the\n // numeric thinking_budget. We default to \"medium\" - Gemini 3's reasoning mode.\n genConfig.thinkingConfig = { thinkingLevel: \"medium\" };\n\n const body: Record = {\n contents,\n generationConfig: genConfig,\n };\n\n if (systemPrompt) {\n body.systemInstruction = { parts: [{ text: systemPrompt }] };\n }\n\n if (tools && tools.length > 0) {\n body.tools = [{\n functionDeclarations: tools.map(t => {\n const fn = t.type === \"function\" ? t.function : t;\n return { name: fn.name, description: fn.description ?? \"\", parameters: stripUnsupported(fn.parameters ?? { type: \"object\", properties: {} }) };\n }),\n }];\n body.toolConfig = { functionCallingConfig: { mode: \"AUTO\" } };\n }\n\n // 25s timeout per LLM call (see callOpenAI for rationale).\n const ac = new AbortController();\n const to = setTimeout(() => ac.abort(), 25_000);\n let res: Response;\n try {\n res = await fetch(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n // Gemini 3 canonical auth - header replaces the legacy ?key= query param.\n \"x-goog-api-key\": config.api_key,\n },\n body: JSON.stringify(body),\n signal: ac.signal,\n });\n } finally {\n clearTimeout(to);\n }\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`Gemini API error ${res.status}: ${errText}`);\n }\n\n const data = await res.json();\n const parts = data.candidates?.[0]?.content?.parts ?? [];\n\n let textContent = \"\";\n const toolCalls: any[] = [];\n\n for (const part of parts) {\n if (part.text) textContent += part.text;\n else if (part.functionCall) {\n // Use the model's own id - must be echoed exactly in the functionResponse\n const tc: any = {\n id: part.functionCall.id ?? `gemini_${Date.now()}_${toolCalls.length}`,\n type: \"function\",\n function: {\n name: part.functionCall.name,\n arguments: JSON.stringify(part.functionCall.args ?? {}),\n },\n };\n // Preserve thoughtSignature (Part-level field) - required by Gemini 2.x thinking models\n const sig = part.thoughtSignature ?? part.thought_signature;\n if (sig) tc._gemini_thought_signature = sig;\n toolCalls.push(tc);\n }\n }\n\n return { content: textContent, tool_calls: toolCalls.length > 0 ? toolCalls : undefined 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Rename a legacy table so the new CREATE below builds the right shape.\n try {\n const cols = await sqlite.execute(\"PRAGMA table_info(tasks)\");\n const have = new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n // Old schema is identifiable by `agent_id` + `input` + no `priority`. New schema has `priority` + `ice_score`.\n const isLegacy = have.has(\"agent_id\") && have.has(\"input\") && !have.has(\"ice_score\");\n if (isLegacy) {\n await sqlite.execute(\"ALTER TABLE tasks RENAME TO tasks_legacy\");\n }\n } catch { /* table doesn't exist yet — fine, new CREATE will run */ }\n\n // notes — shared team notes.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS notes (\n id TEXT PRIMARY KEY,\n author TEXT,\n content TEXT NOT NULL,\n workspace TEXT,\n pinned INTEGER DEFAULT 0,\n created_at TEXT NOT NULL,\n updated_at TEXT\n )\n `);\n\n // Intelligence layer: per-metric 30d baselines for health scoring. (Made per-tenant\n // UNIQUE(tenant_id, integration, metric_key) by mig_metric_baselines_tenant_v1 below.)\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS metric_baselines (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n integration TEXT NOT NULL,\n metric_key TEXT NOT NULL,\n baseline_value REAL,\n baseline_period TEXT DEFAULT '30d',\n last_updated TEXT DEFAULT (datetime('now')),\n UNIQUE(integration, metric_key)\n )\n `);\n\n // Intelligence cache — funnel summary, correlations, recommendations, LLM feed. (cache_key made\n // per-tenant UNIQUE(tenant_id, cache_key) by mig_intelligence_cache_tenant_v1 below.)\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS intelligence_cache (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n cache_key TEXT UNIQUE NOT NULL,\n payload TEXT NOT NULL,\n generated_at TEXT DEFAULT (datetime('now')),\n expires_at TEXT NOT NULL\n )\n `);\n\n // App-wide key/value config: ROI inputs + workspace preferences. (Made per-tenant by\n // mig_app_config_tenant_v1 below: composite PK (tenant_id, key).)\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS app_config (\n key TEXT PRIMARY KEY,\n value TEXT,\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // Metric history — rolling timeseries for delta_7d/delta_30d. Pruned to last 60 days on insert.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS metric_history (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n integration TEXT NOT NULL,\n metric_key TEXT NOT NULL,\n value REAL NOT NULL,\n recorded_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_metric_history_key ON metric_history(integration, metric_key, recorded_at DESC)`);\n\n // A5: rolling baseline columns on metric_baselines (guarded ALTER).\n try {\n const cols = await sqlite.execute(\"PRAGMA table_info(metric_baselines)\");\n const have = new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!have.has(\"rolling_avg\")) {\n await sqlite.execute(\"ALTER TABLE metric_baselines ADD COLUMN rolling_avg REAL\");\n }\n if (!have.has(\"sample_count\")) {\n await sqlite.execute(\"ALTER TABLE metric_baselines ADD COLUMN sample_count INTEGER DEFAULT 0\");\n }\n } catch { /* non-critical: warm-start baselines still work */ }\n\n // QA fix: per-provider key columns on llm_config so switching providers keeps prior keys. Guarded ALTER.\n try {\n const cols = await sqlite.execute(\"PRAGMA table_info(llm_config)\");\n const have = new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n for (const col of [\"api_key_anthropic\", \"api_key_openai\", \"api_key_gemini\", \"api_key_custom\"]) {\n if (!have.has(col)) await sqlite.execute(`ALTER TABLE llm_config ADD COLUMN ${col} TEXT DEFAULT ''`);\n }\n if (!have.has(\"endpoint_custom\")) {\n await sqlite.execute(\"ALTER TABLE llm_config ADD COLUMN endpoint_custom TEXT DEFAULT ''\");\n }\n } catch { /* non-critical: legacy single-key still works */ }\n\n // agent_budgets — monthly LLM-cost cap per agent. current_spend bumps per call, reset at period_start.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS agent_budgets (\n agent_id TEXT PRIMARY KEY,\n monthly_cap REAL DEFAULT 250,\n current_spend REAL DEFAULT 0,\n period_start TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // C3: agent_runs — one row per run (manual/scheduled); latest 50 power the AgentCard timeline.\n // status: queued → running → completed | failed | cancelled.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS agent_runs (\n id TEXT PRIMARY KEY,\n agent_id TEXT NOT NULL,\n status TEXT NOT NULL,\n trigger TEXT DEFAULT 'manual',\n started_at TEXT DEFAULT (datetime('now')),\n finished_at TEXT,\n duration_ms INTEGER,\n input TEXT,\n output TEXT,\n error TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_agent_runs_agent ON agent_runs(agent_id, started_at DESC)`);\n\n // D1: customer_cohorts — one row per cohort (e.g. signup month) for cohort-survival LTV.\n // retention_curve = JSON [m1,m2,...] retained counts; arpu = avg monthly revenue per user.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS customer_cohorts (\n id TEXT PRIMARY KEY,\n cohort_label TEXT NOT NULL,\n cohort_size INTEGER NOT NULL,\n arpu REAL NOT NULL,\n retention_curve TEXT NOT NULL,\n created_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // D3: lead_state_changes — one row per MQL→SQL transition (engagement velocity = avg days per stage).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS lead_state_changes (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n lead_email TEXT NOT NULL,\n from_state TEXT,\n to_state TEXT NOT NULL,\n changed_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_lead_state_email ON lead_state_changes(lead_email, changed_at)`);\n\n // D5: utm_conversions — channel attribution log (POST /api/attribution or future GA4 scrape).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS utm_conversions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n utm_source TEXT,\n utm_medium TEXT,\n utm_campaign TEXT,\n conversion_value REAL DEFAULT 0,\n converted_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_utm_source ON utm_conversions(utm_source, converted_at DESC)`);\n\n // ─── FRAMEWORK LAYER (F1-F4): NSM, OKRs, Experiments, Tasks ──\n\n // F1: North Star Metric (singleton — one active row; previous deleted on set)\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS north_star (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n metric_name TEXT NOT NULL,\n metric_source TEXT NOT NULL,\n metric_key TEXT,\n current_value REAL,\n target_value REAL NOT NULL,\n target_date TEXT NOT NULL,\n unit TEXT DEFAULT 'count',\n description TEXT,\n set_at TEXT DEFAULT (datetime('now')),\n set_by TEXT DEFAULT 'admin'\n )\n `);\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS nsm_history (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n nsm_id INTEGER REFERENCES north_star(id) ON DELETE CASCADE,\n value REAL NOT NULL,\n recorded_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_nsm_history_id ON nsm_history(nsm_id, recorded_at DESC)`);\n\n // F2: OKRs\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS objectives (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n description TEXT,\n quarter TEXT NOT NULL,\n workspace TEXT DEFAULT 'marketing',\n owner TEXT,\n status TEXT DEFAULT 'active',\n progress REAL DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS key_results (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n objective_id INTEGER NOT NULL REFERENCES objectives(id) ON DELETE CASCADE,\n title TEXT NOT NULL,\n metric_source TEXT,\n metric_key TEXT,\n start_value REAL DEFAULT 0,\n current_value REAL DEFAULT 0,\n target_value REAL NOT NULL,\n unit TEXT DEFAULT 'count',\n confidence REAL DEFAULT 0.5,\n progress REAL DEFAULT 0,\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_kr_objective ON key_results(objective_id)`);\n\n // F3: ICE Experiments (ice_score + uplift_pct computed in app code from impact/confidence/ease).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS experiments (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n hypothesis TEXT,\n objective_id INTEGER REFERENCES objectives(id) ON DELETE SET NULL,\n funnel_stage TEXT,\n aarrr_lever TEXT,\n status TEXT DEFAULT 'idea',\n impact REAL DEFAULT 5,\n confidence REAL DEFAULT 5,\n ease REAL DEFAULT 5,\n owner TEXT,\n start_date TEXT,\n end_date TEXT,\n channel TEXT,\n success_metric TEXT,\n baseline_value REAL,\n result_value REAL,\n outcome TEXT,\n learnings TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_exp_status ON experiments(status, updated_at DESC)`);\n\n // F4: Tasks (linked to OKRs/KRs/experiments). Replaces the older string-id tasks table (renamed\n // to tasks_legacy above); the two were never wired to a shared UI so the swap is safe.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS tasks (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n description TEXT,\n status TEXT DEFAULT 'todo',\n priority TEXT DEFAULT 'medium',\n workspace TEXT DEFAULT 'marketing',\n assignee TEXT,\n due_date TEXT,\n objective_id INTEGER REFERENCES objectives(id) ON DELETE SET NULL,\n key_result_id INTEGER REFERENCES key_results(id) ON DELETE SET NULL,\n experiment_id INTEGER REFERENCES experiments(id) ON DELETE SET NULL,\n ice_score REAL,\n impact REAL,\n confidence REAL,\n ease REAL,\n tags TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n completed_at TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_tasks_status ON tasks(status, due_date)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_tasks_links ON tasks(experiment_id, key_result_id, objective_id)`);\n\n // tasks.campaign_id — a task can belong to a campaign; completing it rolls progress up to the\n // objective. PRAGMA-guarded ALTER.\n try {\n const tc = await sqlite.execute(\"PRAGMA table_info(tasks)\");\n const tHave = new Set((tc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!tHave.has(\"campaign_id\")) await sqlite.execute(\"ALTER TABLE tasks ADD COLUMN campaign_id TEXT\");\n } catch { /* non-critical: tasks without a campaign link still work */ }\n\n // ─── INTELLIGENCE LAYER (I1, I4): Signals + Digests ──\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS intent_signals (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n lead_email TEXT NOT NULL,\n lead_name TEXT,\n company TEXT,\n signal_type TEXT NOT NULL,\n signal_source TEXT NOT NULL,\n signal_strength INTEGER DEFAULT 1,\n raw_data TEXT,\n detected_at TEXT DEFAULT (datetime('now')),\n actioned INTEGER DEFAULT 0,\n actioned_at TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_signals_email ON intent_signals(lead_email, detected_at DESC)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_signals_actioned ON intent_signals(actioned, detected_at DESC)`);\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS growth_digests (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n week_of TEXT NOT NULL,\n payload TEXT NOT NULL,\n nsm_value_at_generation REAL,\n generated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_digest_week ON growth_digests(week_of DESC)`);\n\n // ─── UNIFIED CALENDAR (CAL1): custom calendar entries ──\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS calendar_entries (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n description TEXT,\n event_date TEXT NOT NULL,\n event_time TEXT,\n end_date TEXT,\n end_time TEXT,\n event_type TEXT DEFAULT 'custom',\n workspace TEXT DEFAULT 'marketing',\n color TEXT DEFAULT 'blue',\n all_day INTEGER DEFAULT 0,\n recurrence TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_calendar_date ON calendar_entries(event_date)`);\n\n // knowledge_files — growth playbooks/frameworks/formulas injected into AI features (api/knowledge.ts).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS knowledge_files (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n kind TEXT DEFAULT 'playbook',\n content TEXT NOT NULL,\n summary TEXT,\n targets TEXT DEFAULT '[]',\n source TEXT DEFAULT 'user',\n tokens_estimate INTEGER DEFAULT 0,\n enabled INTEGER DEFAULT 1,\n uploaded_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_knowledge_enabled ON knowledge_files(enabled, uploaded_at DESC)`);\n\n // action_queue — human-in-the-loop. AI/agents PREPARE actions here; nothing sends until a human\n // approves, then the integration call runs. status: prepared → approved → executing → done|failed|rejected.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS action_queue (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n source_type TEXT DEFAULT 'chat',\n source_id TEXT,\n integration TEXT NOT NULL,\n endpoint TEXT NOT NULL,\n payload TEXT NOT NULL,\n preview TEXT,\n title TEXT,\n workspace TEXT DEFAULT 'marketing',\n status TEXT DEFAULT 'prepared',\n result TEXT,\n task_id TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n approved_at TEXT,\n executed_at TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_action_queue_status ON action_queue(status, created_at DESC)`);\n\n // deals: CRM sales pipeline. Auto-seeded from qualified leads (POST /api/deals/sync) AND user-editable.\n // stage ∈ lead|qualified|proposal|negotiation|won|lost. lead_email links back to the source lead.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS deals (\n id TEXT PRIMARY KEY,\n title TEXT,\n company TEXT,\n contact_name TEXT,\n contact_email TEXT,\n stage TEXT NOT NULL DEFAULT 'lead',\n amount REAL DEFAULT 0,\n currency TEXT DEFAULT 'USD',\n probability INTEGER DEFAULT 0,\n owner TEXT,\n close_date TEXT,\n source TEXT DEFAULT 'manual',\n lead_email TEXT,\n notes TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n last_activity_at TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deals_stage ON deals(stage, updated_at DESC)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deals_lead ON deals(lead_email)`);\n\n // ─── RELATIONAL CRM FOUNDATION (WAVE 1): Accounts + Contacts ───\n // First-class accounts + contacts; deals link via account_id + primary_contact_id + deal_contacts.\n // Backfilled non-destructively from legacy deal text columns by mig_crm_relational_v1. Tenant-scoped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS accounts (\n id TEXT PRIMARY KEY,\n name TEXT,\n domain TEXT,\n industry TEXT,\n size TEXT,\n website TEXT,\n owner TEXT,\n tags TEXT DEFAULT '[]',\n custom_fields TEXT DEFAULT '{}',\n notes TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_accounts_name ON accounts(tenant_id, name)`);\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS contacts (\n id TEXT PRIMARY KEY,\n account_id TEXT,\n first_name TEXT,\n last_name TEXT,\n email TEXT,\n phone TEXT,\n title TEXT,\n owner TEXT,\n tags TEXT DEFAULT '[]',\n custom_fields TEXT DEFAULT '{}',\n notes TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_contacts_email ON contacts(tenant_id, email)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_contacts_account ON contacts(tenant_id, account_id)`);\n\n // Many-to-many deal↔contact with a buying-committee role (migration uses \"primary\" for the deal's\n // primary_contact_id). Composite PK prevents duplicate links.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS deal_contacts (\n deal_id TEXT NOT NULL,\n contact_id TEXT NOT NULL,\n role TEXT DEFAULT 'contact',\n tenant_id TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n PRIMARY KEY (deal_id, contact_id)\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deal_contacts_contact ON deal_contacts(tenant_id, contact_id)`);\n\n // deals gains account_id + primary_contact_id (guarded ALTER). Legacy text columns KEPT.\n try {\n const dc = await sqlite.execute(\"PRAGMA table_info(deals)\");\n const dHave = new Set((dc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!dHave.has(\"account_id\")) await sqlite.execute(\"ALTER TABLE deals ADD COLUMN account_id TEXT\");\n if (!dHave.has(\"primary_contact_id\")) await sqlite.execute(\"ALTER TABLE deals ADD COLUMN primary_contact_id TEXT\");\n } catch { /* non-critical: relational links added next warm start */ }\n\n // ─── ENGAGEMENT + PIPELINE DEPTH (WAVE 2): Activities + Line Items + Stages ──\n // activities: unified timeline (calls/meetings/notes/emails/tasks) on contact/account/deal.\n // getTimeline() MERGES these with intent_signals (by email) + linked tasks. Tenant-scoped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS activities (\n id TEXT PRIMARY KEY,\n type TEXT,\n subject TEXT,\n body TEXT,\n contact_id TEXT,\n account_id TEXT,\n deal_id TEXT,\n owner TEXT,\n due_date TEXT,\n completed INTEGER DEFAULT 0,\n occurred_at TEXT,\n created_at TEXT,\n updated_at TEXT,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_activities_deal ON activities(tenant_id, deal_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_activities_contact ON activities(tenant_id, contact_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_activities_account ON activities(tenant_id, account_id)`);\n\n // deal_products — LINE ITEMS. Deal amount rolls up as SUM(amount) when present (else manual\n // deals.amount). amount = quantity * unit_price.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS deal_products (\n id TEXT PRIMARY KEY,\n deal_id TEXT,\n name TEXT,\n quantity REAL DEFAULT 1,\n unit_price REAL DEFAULT 0,\n amount REAL DEFAULT 0,\n tenant_id TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deal_products_deal ON deal_products(tenant_id, deal_id)`);\n\n // pipeline_stages — configurable label + win-probability per stage (seeded once below, per tenant).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS pipeline_stages (\n id TEXT PRIMARY KEY,\n stage_key TEXT,\n label TEXT,\n probability INTEGER,\n sort_order INTEGER,\n is_won INTEGER DEFAULT 0,\n is_lost INTEGER DEFAULT 0,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_pipeline_stages_tenant ON pipeline_stages(tenant_id, sort_order)`);\n\n // deals gains next_step + win/loss reasons + recurring/MRR (guarded ALTER).\n try {\n const d2 = await sqlite.execute(\"PRAGMA table_info(deals)\");\n const d2Have = new Set((d2.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n for (const [col, def] of [\n [\"next_step\", \"TEXT\"], [\"win_reason\", \"TEXT\"], [\"lost_reason\", \"TEXT\"],\n [\"recurring_type\", \"TEXT\"], [\"mrr\", \"REAL DEFAULT 0\"],\n ] as Array<[string, string]>) {\n if (!d2Have.has(col)) await sqlite.execute(`ALTER TABLE deals ADD COLUMN ${col} ${def}`);\n }\n } catch { /* non-critical: deal next-step/reasons/MRR added next warm start */ }\n\n // tasks gains CRM links (deal_id/contact_id/account_id); preserves all current task behavior.\n try {\n const t2 = await sqlite.execute(\"PRAGMA table_info(tasks)\");\n const t2Have = new Set((t2.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n for (const col of [\"deal_id\", \"contact_id\", \"account_id\"]) {\n if (!t2Have.has(col)) await sqlite.execute(`ALTER TABLE tasks ADD COLUMN ${col} TEXT`);\n }\n } catch { /* non-critical: CRM task links added next warm start */ }\n\n // ONE-TIME seed of the 6 default pipeline stages (flag mig_pipeline_stages_v1) for every tenant\n // (always incl. t_default). Idempotent: INSERT OR IGNORE on a deterministic id (tenant + stage_key).\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_pipeline_stages_v1'\");\n if (flag.rows.length === 0) {\n const STAGE_SEEDS: Array<[string, string, number, number, number, number]> = [\n // stage_key, label, probability, sort_order, is_won, is_lost\n [\"lead\", \"Lead\", 10, 0, 0, 0],\n [\"qualified\", \"Qualified\", 30, 1, 0, 0],\n [\"proposal\", \"Proposal\", 50, 2, 0, 0],\n [\"negotiation\", \"Negotiation\", 70, 3, 0, 0],\n [\"won\", \"Won\", 100, 4, 1, 0],\n [\"lost\", \"Lost\", 0, 5, 0, 1],\n ];\n // Tenants to seed: every row in the tenants table, always including t_default.\n const tenantIds = new Set([\"t_default\"]);\n try {\n const tns = await sqlite.execute(\"SELECT id FROM tenants\");\n for (const r of (tns.rows || [])) {\n const tid = String(Array.isArray(r) ? r[0] : (r as any).id ?? \"\").trim();\n if (tid) tenantIds.add(tid);\n }\n } catch { /* tenants table may not exist on a very fresh DB — default tenant still seeded */ }\n for (const tid of tenantIds) {\n for (const [key, label, prob, sort, isWon, isLost] of STAGE_SEEDS) {\n await sqlite.execute(\n `INSERT OR IGNORE INTO pipeline_stages (id, stage_key, label, probability, sort_order, is_won, is_lost, tenant_id)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?)`,\n [\"ps_\" + tid + \"_\" + key, key, label, prob, sort, isWon, isLost, tid]\n );\n }\n }\n await sqlite.execute(\n \"INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES ('mig_pipeline_stages_v1', 'done', 't_default', datetime('now'))\"\n );\n }\n } catch { /* non-critical: stage seed retried next warm start (flag only set on success) */ }\n\n // ─── SALES ANALYTICS + LEAD OPS + PRODUCTIVITY (WAVE 3) ───\n // Eight tenant-scoped tables over deals/contacts/intent_signals/users; in TENANT_SCOPED_TABLES.\n\n // A) SALES ANALYTICS ───\n // deal_stage_history: one row per stage change. Powers stage-conversion, velocity, funnel/leaderboard.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS deal_stage_history (\n id TEXT PRIMARY KEY,\n deal_id TEXT,\n from_stage TEXT,\n to_stage TEXT,\n owner TEXT,\n changed_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deal_stage_history_deal ON deal_stage_history(tenant_id, deal_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_deal_stage_history_to ON deal_stage_history(tenant_id, to_stage)`);\n\n // sales_quotas: per-owner revenue target per period. period_type ∈ month|quarter|year; period = label.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sales_quotas (\n id TEXT PRIMARY KEY,\n owner TEXT,\n period TEXT,\n period_type TEXT,\n target_amount REAL DEFAULT 0,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_sales_quotas_owner ON sales_quotas(tenant_id, owner, period)`);\n\n // pipeline_snapshots: one row PER STAGE per snapshot_date. weighted_amount = total × stage prob.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS pipeline_snapshots (\n id TEXT PRIMARY KEY,\n snapshot_date TEXT,\n stage TEXT,\n deal_count INTEGER DEFAULT 0,\n total_amount REAL DEFAULT 0,\n weighted_amount REAL DEFAULT 0,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_pipeline_snapshots_date ON pipeline_snapshots(tenant_id, snapshot_date)`);\n\n // B) LEAD OPS ───\n // lead_status: grade/owner/SLA overlay per (tenant, lead_email). grade ∈ A|B|C|D; status ∈\n // new|working|qualified|disqualified. sla_due_at = first-response deadline; first_response_at stops clock.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS lead_status (\n id TEXT PRIMARY KEY,\n lead_email TEXT,\n grade TEXT,\n owner TEXT,\n status TEXT DEFAULT 'new',\n first_response_at TEXT,\n sla_due_at TEXT,\n assigned_at TEXT,\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE UNIQUE INDEX IF NOT EXISTS idx_lead_status_email ON lead_status(tenant_id, lead_email)`);\n\n // C) PRODUCTIVITY ───\n // email_templates: reusable subject/body with {{merge_field}} placeholders → Gmail/Outlook draft path.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS email_templates (\n id TEXT PRIMARY KEY,\n name TEXT,\n subject TEXT,\n body TEXT,\n category TEXT,\n owner TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_email_templates_tenant ON email_templates(tenant_id, category)`);\n\n // sequences: named multi-step cadence (active=1 default).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sequences (\n id TEXT PRIMARY KEY,\n name TEXT,\n description TEXT,\n active INTEGER DEFAULT 1,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_sequences_tenant ON sequences(tenant_id, active)`);\n\n // sequence_steps: ordered steps. channel ∈ email|task|call; delay_days = wait after prior step;\n // template_id → email_templates (else inline subject/body).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sequence_steps (\n id TEXT PRIMARY KEY,\n sequence_id TEXT,\n step_order INTEGER DEFAULT 0,\n channel TEXT DEFAULT 'email',\n delay_days INTEGER DEFAULT 0,\n template_id TEXT,\n subject TEXT,\n body TEXT,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_sequence_steps_seq ON sequence_steps(tenant_id, sequence_id, step_order)`);\n\n // sequence_enrollments: contact (+ optional deal) in a sequence. current_step = 0-based pointer;\n // status ∈ active|paused|completed; next_action_at drives the runner.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sequence_enrollments (\n id TEXT PRIMARY KEY,\n sequence_id TEXT,\n contact_id TEXT,\n deal_id TEXT,\n current_step INTEGER DEFAULT 0,\n status TEXT DEFAULT 'active',\n enrolled_at TEXT DEFAULT (datetime('now')),\n next_action_at TEXT,\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_seq_enroll_seq ON sequence_enrollments(tenant_id, sequence_id, status)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_seq_enroll_contact ON sequence_enrollments(tenant_id, contact_id)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_seq_enroll_next ON sequence_enrollments(tenant_id, next_action_at)`);\n\n // ─── MARKETING WORKSPACE (WAVE 1) ───\n // marketing_personas: Persona/ICP/segment library. kind ∈ icp|persona|segment; attributes = JSON;\n // source ∈ enrichment|manual. Tenant-scoped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS marketing_personas (\n id TEXT PRIMARY KEY,\n name TEXT,\n kind TEXT,\n description TEXT,\n attributes TEXT,\n source TEXT DEFAULT 'manual',\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_marketing_personas_kind ON marketing_personas(tenant_id, kind)`);\n\n // utm_links: saved campaign-tagged URLs (UTM builder). full_url = base_url + utm params. Tenant-scoped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS utm_links (\n id TEXT PRIMARY KEY,\n name TEXT,\n base_url TEXT,\n utm_source TEXT,\n utm_medium TEXT,\n utm_campaign TEXT,\n utm_term TEXT,\n utm_content TEXT,\n full_url TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_utm_links_tenant ON utm_links(tenant_id)`);\n\n // brand_kit: one row per tenant — voice/tone/values/dos/donts/boilerplate + JSON palette + logo_url.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS brand_kit (\n id TEXT PRIMARY KEY,\n voice TEXT,\n tone TEXT,\n brand_values TEXT,\n dos TEXT,\n donts TEXT,\n boilerplate TEXT,\n palette TEXT,\n logo_url TEXT,\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_brand_kit_tenant ON brand_kit(tenant_id)`);\n\n // ─── MARKETING CAPTURE + AUDIENCES (WAVE 2) ───\n // Six tenant-scoped tables: CONVERT traffic (forms + landing pages) + TARGET people (segments +\n // lists). tenant_id on all EXCEPT two PUBLIC read paths (resolve owning tenant by id/slug). In\n // TENANT_SCOPED_TABLES for mig_tenant_v1. NEVER use a SQL reserved word as a column name.\n\n // marketing_forms: lead-capture form. fields = JSON; asset_url = gated download; redirect_url =\n // post-submit redirect; submit_count bumps on each public submit.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS marketing_forms (\n id TEXT PRIMARY KEY,\n name TEXT,\n fields TEXT,\n success_message TEXT,\n asset_url TEXT,\n redirect_url TEXT,\n submit_count INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_marketing_forms_tenant ON marketing_forms(tenant_id)`);\n\n // form_submissions: one row per public submit. data = JSON of posted fields. tenant_id = the OWNING\n // tenant of form_id (resolved by id at submit time, never the anonymous caller's session).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS form_submissions (\n id TEXT PRIMARY KEY,\n form_id TEXT,\n data TEXT,\n lead_email TEXT,\n lead_name TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_form_submissions_form ON form_submissions(tenant_id, form_id)`);\n\n // landing_pages: public page. slug GLOBALLY unique (public /api/marketing/p/:slug render across\n // tenants). blocks = JSON; form_id embeds a form; published=1 gates render; view_count bumps.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS landing_pages (\n id TEXT PRIMARY KEY,\n slug TEXT,\n title TEXT,\n subtitle TEXT,\n blocks TEXT,\n form_id TEXT,\n theme TEXT,\n published INTEGER DEFAULT 0,\n view_count INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_landing_pages_tenant ON landing_pages(tenant_id)`);\n await sqlite.execute(`CREATE UNIQUE INDEX IF NOT EXISTS idx_landing_pages_slug ON landing_pages(slug)`);\n\n // segments: saved audience. kind ∈ dynamic|static; rules = JSON over contacts + lead_status + signals.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS segments (\n id TEXT PRIMARY KEY,\n name TEXT,\n description TEXT,\n kind TEXT,\n rules TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_segments_tenant ON segments(tenant_id)`);\n\n // marketing_lists: named contact list. kind ∈ static|dynamic; dynamic → segment_id materializes members.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS marketing_lists (\n id TEXT PRIMARY KEY,\n name TEXT,\n kind TEXT,\n segment_id TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_marketing_lists_tenant ON marketing_lists(tenant_id)`);\n\n // list_members: contact↔list join. lead_email is the stable key when contact_id is unavailable.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS list_members (\n id TEXT PRIMARY KEY,\n list_id TEXT,\n contact_id TEXT,\n lead_email TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_list_members_list ON list_members(tenant_id, list_id)`);\n\n // ─── CONTENT + CAMPAIGN PRODUCTION ENGINE (WAVE 3) ───\n // Four tenant-scoped tables: editorial calendar, SEO briefs, asset library, campaign budgeting.\n // In TENANT_SCOPED_TABLES. NEVER use a SQL reserved word as a column name.\n\n // content_items: a content piece. type ∈ blog|social|video|email|landing|ad; status ∈\n // idea|brief|draft|review|scheduled|published. campaign_id/brief_id link out; asset_ids = JSON ids;\n // scheduled_at drives the editorial calendar; body = the draft.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS content_items (\n id TEXT PRIMARY KEY,\n title TEXT,\n type TEXT,\n status TEXT DEFAULT 'idea',\n channel TEXT,\n campaign_id TEXT,\n brief_id TEXT,\n body TEXT,\n asset_ids TEXT DEFAULT '[]',\n owner TEXT,\n scheduled_at TEXT,\n published_at TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_content_items_status ON content_items(tenant_id, status)`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_content_items_scheduled ON content_items(tenant_id, scheduled_at)`);\n\n // content_briefs: SEO brief for a content_item (content_id links back). outline/competitors/\n // internal_links = JSON/text; word_count_target = writer's target.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS content_briefs (\n id TEXT PRIMARY KEY,\n content_id TEXT,\n target_keyword TEXT,\n search_intent TEXT,\n outline TEXT,\n competitors TEXT,\n internal_links TEXT,\n word_count_target INTEGER,\n notes TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_content_briefs_tenant ON content_briefs(tenant_id)`);\n\n // marketing_assets: media registry. asset_type ∈ image|video|doc|pdf|other; source ∈\n // upload|wordpress|drive|link; bytes = file size.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS marketing_assets (\n id TEXT PRIMARY KEY,\n name TEXT,\n url TEXT,\n asset_type TEXT,\n tags TEXT,\n source TEXT DEFAULT 'upload',\n bytes INTEGER,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_marketing_assets_tenant ON marketing_assets(tenant_id)`);\n\n // campaign_budgets: planned vs actual spend per channel per period. actual_amount accrues (pacing).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS campaign_budgets (\n id TEXT PRIMARY KEY,\n campaign_id TEXT,\n channel TEXT,\n period TEXT,\n planned_amount REAL DEFAULT 0,\n actual_amount REAL DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_campaign_budgets_campaign ON campaign_budgets(tenant_id, campaign_id)`);\n\n // ─── ACQUISITION CHANNELS + AUTOMATION DEPTH (WAVE 4) ───\n // Six tenant-scoped tables: ad_campaigns, journeys + journey_runs, abm_targets, broadcasts,\n // partners. In TENANT_SCOPED_TABLES. NEVER use a SQL reserved word as a column name.\n\n // ad_campaigns: paid-media spend. platform ∈ google_ads|meta|linkedin|other; external_id = the\n // platform's campaign id; campaign_id links an internal campaigns row; period is a label.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ad_campaigns (\n id TEXT PRIMARY KEY,\n platform TEXT,\n external_id TEXT,\n name TEXT,\n status TEXT,\n spend REAL DEFAULT 0,\n impressions INTEGER DEFAULT 0,\n clicks INTEGER DEFAULT 0,\n conversions INTEGER DEFAULT 0,\n campaign_id TEXT,\n period TEXT,\n synced_at TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_ad_campaigns_platform ON ad_campaigns(tenant_id, platform)`);\n\n // journeys: triggered-automation rules. trigger_type ∈ form_submit|intent_signal|deal_stage|\n // list_added|manual (trigger_config JSON); action_type ∈ enroll_sequence|add_to_list|create_task|\n // notify|grade_lead (action_config JSON). active=1 gates; run_count bumps per match.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS journeys (\n id TEXT PRIMARY KEY,\n name TEXT,\n description TEXT,\n trigger_type TEXT,\n trigger_config TEXT,\n action_type TEXT,\n action_config TEXT,\n active INTEGER DEFAULT 1,\n run_count INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_journeys_trigger ON journeys(tenant_id, trigger_type, active)`);\n\n // journey_runs: one row per journey evaluation. entity_ref = triggering entity (lead_email/deal_id);\n // status ∈ matched|acted|skipped|error; detail = short outcome message.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS journey_runs (\n id TEXT PRIMARY KEY,\n journey_id TEXT,\n trigger_type TEXT,\n entity_ref TEXT,\n status TEXT,\n detail TEXT,\n ran_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_journey_runs_journey ON journey_runs(tenant_id, journey_id)`);\n\n // abm_targets: ABM target accounts (account_id → accounts). tier ∈ 1|2|3 (text); engagement_score\n // from real signals/activities/deals; status ∈ target|engaged|opportunity|won|dropped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS abm_targets (\n id TEXT PRIMARY KEY,\n account_id TEXT,\n tier TEXT,\n engagement_score REAL DEFAULT 0,\n status TEXT DEFAULT 'target',\n owner TEXT,\n notes TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_abm_targets_account ON abm_targets(tenant_id, account_id)`);\n\n // broadcasts: one-off/scheduled email sends. audience_type ∈ list|segment|all_contacts (audience_id\n // → chosen list/segment, NULL for all). channel ∈ gmail|outlook; status ∈ draft|scheduled|sending|sent.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS broadcasts (\n id TEXT PRIMARY KEY,\n name TEXT,\n subject TEXT,\n body TEXT,\n audience_type TEXT,\n audience_id TEXT,\n channel TEXT,\n status TEXT DEFAULT 'draft',\n scheduled_at TEXT,\n sent_count INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_broadcasts_status ON broadcasts(tenant_id, status)`);\n\n // partners: referral/affiliate/reseller/agency channel partners. referral_code carried on a referred\n // deal/contact source; status ∈ active|paused; deals_referred + revenue accrue from attribution.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS partners (\n id TEXT PRIMARY KEY,\n name TEXT,\n type TEXT,\n contact_email TEXT,\n referral_code TEXT,\n status TEXT DEFAULT 'active',\n deals_referred INTEGER DEFAULT 0,\n revenue REAL DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_partners_tenant ON partners(tenant_id)`);\n\n // ── Wave 5: analytics / optimization / market-intel ──\n // competitors — tracked rival domains. metrics = JSON snapshot (domain_rating/backlinks/\n // ref_domains/organic_keywords) from Ahrefs; tracked_keywords = JSON. Tenant-scoped.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS competitors (\n id TEXT PRIMARY KEY,\n name TEXT,\n domain TEXT,\n notes TEXT,\n tracked_keywords TEXT,\n metrics TEXT,\n last_checked TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_competitors_tenant ON competitors(tenant_id)`);\n\n // cro_tests — CRO tests linking an experiment + a landing_page. status ∈ draft|running|complete;\n // winner names the winning arm. Results read from the linked experiment (stat-sig via experiments.ts).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS cro_tests (\n id TEXT PRIMARY KEY,\n name TEXT,\n hypothesis TEXT,\n experiment_id INTEGER,\n landing_page_id TEXT,\n status TEXT DEFAULT 'draft',\n winner TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_cro_tests_tenant ON cro_tests(tenant_id, status)`);\n\n // market_digests — caches the AI market-intel digest. summary = synthesized text; signals = JSON list\n // of real sources used (ProductHunt/YouTube/intent_signals/competitor metrics).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS market_digests (\n id TEXT PRIMARY KEY,\n period TEXT,\n summary TEXT,\n signals TEXT,\n generated_at TEXT DEFAULT (datetime('now')),\n tenant_id TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_market_digests_tenant ON market_digests(tenant_id)`);\n\n // I3: agents.role for Listener/Topic/Creator/Analyst/General (guarded ALTER)\n try {\n const ac = await sqlite.execute(\"PRAGMA table_info(agents)\");\n const aHave = new Set((ac.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!aHave.has(\"role\")) await sqlite.execute(\"ALTER TABLE agents ADD COLUMN role TEXT DEFAULT 'general'\");\n } catch { /* non-critical */ }\n\n // Part 15.3: persistent business memory (evolving learned facts).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS memory (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n type TEXT DEFAULT 'fact',\n content TEXT NOT NULL,\n source TEXT DEFAULT 'manual',\n confidence REAL DEFAULT 0.7,\n pinned INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // Part 1: skills STATE — enabled/pinned + custom skills.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS skills (\n name TEXT PRIMARY KEY,\n enabled INTEGER DEFAULT 1,\n pinned INTEGER DEFAULT 0,\n source TEXT DEFAULT 'builtin',\n category TEXT,\n description TEXT,\n body TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // Part 3 fix 7: experiments.sample_size + significance (guarded ALTER).\n try {\n const ec = await sqlite.execute(\"PRAGMA table_info(experiments)\");\n const eHave = new Set((ec.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!eHave.has(\"sample_size\")) await sqlite.execute(\"ALTER TABLE experiments ADD COLUMN sample_size INTEGER\");\n if (!eHave.has(\"significance\")) await sqlite.execute(\"ALTER TABLE experiments ADD COLUMN significance REAL\");\n } catch { /* non-critical */ }\n\n // Part 14.2: agent_runs.trace JSON (TraceStep[]).\n try {\n const rc = await sqlite.execute(\"PRAGMA table_info(agent_runs)\");\n const rHave = new Set((rc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!rHave.has(\"trace\")) await sqlite.execute(\"ALTER TABLE agent_runs ADD COLUMN trace TEXT\");\n } catch { /* non-critical */ }\n\n // Multi-agent CAMPAIGNS — a campaign has many inputs + MANY member agents. A campaign RUN is the\n // Director orchestrating member agents (sequential/parallel) and synthesizing a final output.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS campaigns (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n workspace TEXT NOT NULL DEFAULT 'marketing',\n description TEXT,\n goal TEXT,\n channel TEXT,\n audience TEXT,\n mode TEXT DEFAULT 'sequential',\n status TEXT DEFAULT 'active',\n objective_id INTEGER,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n created_by TEXT DEFAULT 'user'\n )\n `);\n // campaign_agents — ordered membership (agents + pipeline order + role).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS campaign_agents (\n campaign_id TEXT NOT NULL,\n agent_id TEXT NOT NULL,\n sequence INTEGER DEFAULT 0,\n role TEXT,\n PRIMARY KEY (campaign_id, agent_id)\n )\n `);\n // campaign_runs — one Director pipeline execution.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS campaign_runs (\n id TEXT PRIMARY KEY,\n campaign_id TEXT NOT NULL,\n status TEXT NOT NULL DEFAULT 'running',\n input TEXT,\n final_output TEXT,\n trace TEXT,\n started_at TEXT DEFAULT (datetime('now')),\n finished_at TEXT\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_campaign_runs ON campaign_runs(campaign_id, started_at DESC)`);\n // Campaign TRIGGERS + LOOP. trigger_type ∈ manual|interval|schedule|event; trigger_config = JSON;\n // loop=1 reschedules after each run; next_run_at drives the cron runner. (Guarded ALTER.)\n try {\n const cc = await sqlite.execute(\"PRAGMA table_info(campaigns)\");\n const cHave = new Set((cc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n for (const [col, def] of [[\"trigger_type\", \"TEXT DEFAULT 'manual'\"], [\"trigger_config\", \"TEXT\"], [\"next_run_at\", \"TEXT\"], [\"last_run_at\", \"TEXT\"], [\"loop\", \"INTEGER DEFAULT 0\"]] as Array<[string, string]>) {\n if (!cHave.has(col)) await sqlite.execute(`ALTER TABLE campaigns ADD COLUMN ${col} ${def}`);\n }\n } catch { /* non-critical */ }\n // agent_runs gains campaign context (campaign_run_id + handed-off input from prior agents).\n try {\n const arc = await sqlite.execute(\"PRAGMA table_info(agent_runs)\");\n const arHave = new Set((arc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!arHave.has(\"campaign_run_id\")) await sqlite.execute(\"ALTER TABLE agent_runs ADD COLUMN campaign_run_id TEXT\");\n if (!arHave.has(\"received_input\")) await sqlite.execute(\"ALTER TABLE agent_runs ADD COLUMN received_input TEXT\");\n // task_id links an agent_run back to the task it executed (POST /api/tasks/:id/run).\n if (!arHave.has(\"task_id\")) await sqlite.execute(\"ALTER TABLE agent_runs ADD COLUMN task_id TEXT\");\n } catch { /* non-critical */ }\n\n // ─── AUTH / MULTI-TENANCY FOUNDATION ───\n // Identity tables — NO tenant_id (they ARE the tenant/identity layer).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS tenants (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n slug TEXT UNIQUE,\n plan TEXT DEFAULT 'trial',\n status TEXT DEFAULT 'active',\n created_at TEXT DEFAULT (datetime('now'))\n )\n `);\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS users (\n id TEXT PRIMARY KEY,\n email TEXT UNIQUE NOT NULL,\n name TEXT,\n password_hash TEXT,\n avatar_url TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n last_login_at TEXT\n )\n `);\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS memberships (\n tenant_id TEXT NOT NULL,\n user_id TEXT NOT NULL,\n role TEXT DEFAULT 'owner',\n created_at TEXT DEFAULT (datetime('now')),\n PRIMARY KEY (tenant_id, user_id)\n )\n `);\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sessions (\n id TEXT PRIMARY KEY,\n user_id TEXT NOT NULL,\n tenant_id TEXT NOT NULL,\n created_at TEXT DEFAULT (datetime('now')),\n expires_at TEXT,\n user_agent TEXT,\n revoked INTEGER DEFAULT 0\n )\n `);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_sessions_user ON sessions(user_id)`);\n\n // Phase D: read-only impersonation (\"view as tenant\"). Admin toggles this on THEIR OWN session;\n // resolveSessionToken redirects currentTenantId() to the target tenant for reads only (writes\n // blocked by main.ts). NULL = not impersonating. PRAGMA-guarded ALTER (idempotent).\n try {\n const sc = await sqlite.execute(\"PRAGMA table_info(sessions)\");\n const sHave = new Set((sc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!sHave.has(\"impersonate_tenant_id\")) await sqlite.execute(\"ALTER TABLE sessions ADD COLUMN impersonate_tenant_id TEXT\");\n } catch { /* non-critical: impersonation disabled until column exists; added next warm start */ }\n\n // ─── PLATFORM SUPERADMIN LAYER (above tenants) ───\n // The single audited exception to per-tenant isolation: a platform owner who reads across all\n // tenants. users gains is_platform_admin (master flag) + status. PRAGMA-guarded ALTERs (idempotent).\n try {\n const uc = await sqlite.execute(\"PRAGMA table_info(users)\");\n const uHave = new Set((uc.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (!uHave.has(\"is_platform_admin\")) await sqlite.execute(\"ALTER TABLE users ADD COLUMN is_platform_admin INTEGER DEFAULT 0\");\n if (!uHave.has(\"status\")) await sqlite.execute(\"ALTER TABLE users ADD COLUMN status TEXT DEFAULT 'active'\");\n } catch { /* non-critical: master flag set by migration below on next warm start */ }\n\n // platform_audit — append-only log of every master-only action. No tenant_id: spans all tenants.\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS platform_audit (\n id TEXT PRIMARY KEY,\n actor_user_id TEXT,\n actor_email TEXT,\n action TEXT,\n target_type TEXT,\n target_id TEXT,\n detail TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )\n `);\n\n // tenant_id column on EVERY business table (PRAGMA-guarded ALTER, idempotent). NOT on\n // tenants/users/memberships/sessions (those ARE the identity layer).\n const TENANT_SCOPED_TABLES = [\n \"integrations\", \"llm_config\", \"agents\", \"conversations\", \"tasks\", \"tasks_legacy\",\n \"notes\", \"metric_baselines\", \"intelligence_cache\", \"app_config\", \"metric_history\",\n \"agent_budgets\", \"agent_runs\", \"customer_cohorts\", \"lead_state_changes\", \"utm_conversions\",\n \"north_star\", \"nsm_history\", \"objectives\", \"key_results\", \"experiments\", \"intent_signals\",\n \"growth_digests\", \"calendar_entries\", \"knowledge_files\", \"action_queue\", \"deals\", \"memory\",\n \"skills\", \"campaigns\", \"campaign_agents\", \"campaign_runs\",\n \"accounts\", \"contacts\", \"deal_contacts\",\n \"activities\", \"deal_products\", \"pipeline_stages\",\n \"deal_stage_history\", \"sales_quotas\", \"pipeline_snapshots\", \"lead_status\",\n \"email_templates\", \"sequences\", \"sequence_steps\", \"sequence_enrollments\",\n \"marketing_personas\", \"utm_links\", \"brand_kit\",\n \"marketing_forms\", \"form_submissions\", \"landing_pages\",\n \"segments\", \"marketing_lists\", \"list_members\",\n \"content_items\", \"content_briefs\", \"marketing_assets\", \"campaign_budgets\",\n \"ad_campaigns\", \"journeys\", \"journey_runs\", \"abm_targets\", \"broadcasts\", \"partners\",\n ];\n for (const tbl of TENANT_SCOPED_TABLES) {\n try {\n const cols = await sqlite.execute(`PRAGMA table_info(${tbl})`);\n const have = new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n // Only ALTER if the table actually exists (PRAGMA returns 0 cols for missing tables) and lacks tenant_id.\n if (have.size > 0 && !have.has(\"tenant_id\")) {\n await sqlite.execute(`ALTER TABLE ${tbl} ADD COLUMN tenant_id TEXT`);\n }\n } catch { /* non-critical: table may not exist yet (e.g. tasks_legacy) */ }\n }\n\n // ONE-TIME backfill + seed (flag mig_tenant_v1). Backfill every business table's tenant_id to\n // \"t_default\" + insert the default tenant row. Owner user creation is left to api/auth.ts.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_tenant_v1'\");\n if (flag.rows.length === 0) {\n for (const tbl of TENANT_SCOPED_TABLES) {\n try {\n const cols = await sqlite.execute(`PRAGMA table_info(${tbl})`);\n const have = new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n if (have.has(\"tenant_id\")) {\n await sqlite.execute(`UPDATE ${tbl} SET tenant_id = 't_default' WHERE tenant_id IS NULL`);\n }\n } catch { /* non-critical: skip missing tables */ }\n }\n await sqlite.execute(\n \"INSERT OR IGNORE INTO tenants (id, name, slug) VALUES ('t_default', 'Default Workspace', 'default')\"\n );\n // NOTE: app_config now has tenant_id; write the migration flag scoped to t_default so the\n // gate read above (no tenant filter) still finds it on subsequent boots.\n await sqlite.execute(\n \"INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES ('mig_tenant_v1', 'done', 't_default', datetime('now'))\"\n );\n }\n } catch { /* non-critical: migration retried next warm start */ }\n\n // ─── PLATFORM MASTER PROMOTION (mig_platform_admin_v1) ───\n // ONE-TIME (flag mig_platform_admin_v1): promote the platform owner (PLATFORM_OWNER_EMAIL env) to\n // is_platform_admin=1. api/auth.ts also sets it at signup. Idempotent UPDATE.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_platform_admin_v1'\");\n if (flag.rows.length === 0) {\n const masterEmail = (Deno.env.get(\"PLATFORM_OWNER_EMAIL\") || \"\").trim().toLowerCase();\n await sqlite.execute(\n \"UPDATE users SET is_platform_admin = 1 WHERE lower(email) = ?\",\n [masterEmail]\n );\n await sqlite.execute(\n \"INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES ('mig_platform_admin_v1', 'done', 't_default', datetime('now'))\"\n );\n }\n } catch { /* non-critical: promotion retried next warm start (flag only set on success) */ }\n\n // ─── INTEGRATIONS UNIQUENESS → PER-TENANT (mig_integrations_v2) ───\n // Legacy integrations had global `name UNIQUE`; multi-tenant needs UNIQUE(tenant_id, name) so each\n // tenant owns its own google/outlook/etc row. SQLite can't ALTER a UNIQUE in place → ONE-TIME\n // guarded rebuild (flag mig_integrations_v2). Idempotent: rebuild only when flag absent AND the\n // live table still has the global UNIQUE(name) index. Rows already carry tenant_id='t_default'\n // (Stage 1 ALTER + backfill), so the copy preserves ownership.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_integrations_v2'\");\n if (flag.rows.length === 0) {\n // A FRESH DB is already per-tenant (top-level CREATE), so detect the OLD shape (global\n // UNIQUE(name) index lacking tenant_id) and only rebuild then.\n const icols = await sqlite.execute(\"PRAGMA table_info(integrations)\");\n const iHave = new Set((icols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n let needsRebuild = false;\n if (iHave.size > 0) {\n // index_list → index_info gives each unique index's columns. Legacy = (name); new = (tenant_id, name).\n try {\n const idxList = await sqlite.execute(\"PRAGMA index_list(integrations)\");\n for (const row of (idxList.rows || [])) {\n const idxName = String(Array.isArray(row) ? row[1] : row.name);\n const idxUnique = Number(Array.isArray(row) ? row[2] : row.unique);\n if (!idxUnique) continue;\n const info = await sqlite.execute(`PRAGMA index_info(${idxName})`);\n const idxCols = (info.rows || []).map((r: any) => String(Array.isArray(r) ? r[2] : r.name));\n // Old-shape: a unique index over exactly [name] with no tenant_id ⇒ rebuild required.\n if (idxCols.includes(\"name\") && !idxCols.includes(\"tenant_id\")) needsRebuild = true;\n }\n } catch { needsRebuild = true; /* if we can't introspect, rebuild to be safe */ }\n }\n if (needsRebuild) {\n // Build v2 with the SAME columns (now including tenant_id) but UNIQUE(tenant_id, name).\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS integrations_v2 (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n api_key TEXT,\n api_secret TEXT,\n access_token TEXT,\n refresh_token TEXT,\n extra_config TEXT,\n connected_at TEXT,\n updated_at TEXT,\n tenant_id TEXT,\n UNIQUE(tenant_id, name)\n )\n `);\n // Copy every row (existing rows already carry tenant_id='t_default' from Stage 1).\n await sqlite.execute(`\n INSERT OR IGNORE INTO integrations_v2\n (id, name, api_key, api_secret, access_token, refresh_token, extra_config, connected_at, updated_at, tenant_id)\n SELECT id, name, api_key, api_secret, access_token, refresh_token, extra_config, connected_at, updated_at,\n COALESCE(tenant_id, 't_default')\n FROM integrations\n `);\n await sqlite.execute(\"DROP TABLE integrations\");\n await sqlite.execute(\"ALTER TABLE integrations_v2 RENAME TO integrations\");\n }\n await sqlite.execute(\n \"INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES ('mig_integrations_v2', 'done', 't_default', datetime('now'))\"\n );\n }\n } catch { /* non-critical: rebuild retried next warm start (flag only set on success) */ }\n\n // ─── RELATIONAL CRM BACKFILL (mig_crm_relational_v1) ───\n // ONE-TIME (flag mig_crm_relational_v1). For EVERY deal, derive relational rows non-destructively\n // from its legacy text fields: upsert account by (tenant_id, lower(company)); upsert contact by\n // (tenant_id, lower(contact_email)) linked to that account; set deal.account_id +\n // primary_contact_id; insert deal_contacts(role='primary'). Global pass: reads each deal's OWN\n // tenant_id and writes rows carrying THAT tenant_id. Idempotent (existence checks + INSERT OR\n // IGNORE); never deletes/edits existing deal data.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_crm_relational_v1'\");\n if (flag.rows.length === 0) {\n // Pull every deal with its own tenant_id + legacy text fields.\n const dealsRes = await sqlite.execute(\n \"SELECT id, tenant_id, company, contact_name, contact_email FROM deals\"\n );\n const dealRows = (dealsRes.rows || []) as any[];\n for (const row of dealRows) {\n // libSQL returns array OR object rows by driver — read defensively by index/name.\n const dealId = String(Array.isArray(row) ? row[0] : row.id ?? \"\");\n const tenantId = String((Array.isArray(row) ? row[1] : row.tenant_id) ?? \"t_default\") || \"t_default\";\n const company = String((Array.isArray(row) ? row[2] : row.company) ?? \"\").trim();\n const contactName = String((Array.isArray(row) ? row[3] : row.contact_name) ?? \"\").trim();\n const contactEmail = String((Array.isArray(row) ? row[4] : row.contact_email) ?? \"\").trim();\n if (!dealId) continue;\n\n let accountId: string | null = null;\n let contactId: string | null = null;\n\n // ── ACCOUNT upsert by (tenant_id, lower(company)) ──\n if (company) {\n const existAcct = await sqlite.execute(\n \"SELECT id FROM accounts WHERE tenant_id = ? AND lower(name) = lower(?) LIMIT 1\",\n [tenantId, company]\n );\n if (existAcct.rows.length > 0) {\n const ar = existAcct.rows[0] as any;\n accountId = String(Array.isArray(ar) ? ar[0] : ar.id);\n } else {\n accountId = crypto.randomUUID();\n await sqlite.execute(\n `INSERT INTO accounts (id, name, tags, custom_fields, created_at, updated_at, tenant_id)\n VALUES (?, ?, '[]', '{}', datetime('now'), datetime('now'), ?)`,\n [accountId, company, tenantId]\n );\n }\n }\n\n // ── CONTACT upsert by (tenant_id, lower(contact_email)) ──\n if (contactEmail) {\n const existContact = await sqlite.execute(\n \"SELECT id, account_id FROM contacts WHERE tenant_id = ? AND lower(email) = lower(?) LIMIT 1\",\n [tenantId, contactEmail]\n );\n if (existContact.rows.length > 0) {\n const cr = existContact.rows[0] as any;\n contactId = String(Array.isArray(cr) ? cr[0] : cr.id);\n const existingAcctId = Array.isArray(cr) ? cr[1] : cr.account_id;\n // Backfill the contact's account link if it has none yet and we found one here.\n if (accountId && (existingAcctId === null || existingAcctId === undefined || existingAcctId === \"\")) {\n await sqlite.execute(\n \"UPDATE contacts SET account_id = ?, updated_at = datetime('now') WHERE id = ? AND tenant_id = ?\",\n [accountId, contactId, tenantId]\n );\n }\n } else {\n // Split contact_name → first (first token) / last (remainder).\n let firstName = \"\", lastName = \"\";\n if (contactName) {\n const parts = contactName.split(/\\s+/);\n firstName = parts.shift() || \"\";\n lastName = parts.join(\" \");\n }\n contactId = crypto.randomUUID();\n await sqlite.execute(\n `INSERT INTO contacts (id, account_id, first_name, last_name, email, tags, custom_fields, created_at, updated_at, tenant_id)\n VALUES (?, ?, ?, ?, ?, '[]', '{}', datetime('now'), datetime('now'), ?)`,\n [contactId, accountId, firstName, lastName, contactEmail, tenantId]\n );\n }\n }\n\n // ── Link the deal: set account_id / primary_contact_id only if still empty (idempotent) ──\n if (accountId || contactId) {\n await sqlite.execute(\n `UPDATE deals\n SET account_id = COALESCE(NULLIF(account_id, ''), ?),\n primary_contact_id = COALESCE(NULLIF(primary_contact_id, ''), ?)\n WHERE id = ? AND tenant_id = ?`,\n [accountId, contactId, dealId, tenantId]\n );\n }\n\n // ── deal_contacts(role='primary') join row (INSERT OR IGNORE on composite PK) ──\n if (contactId) {\n await sqlite.execute(\n `INSERT OR IGNORE INTO deal_contacts (deal_id, contact_id, role, tenant_id, created_at)\n VALUES (?, ?, 'primary', ?, datetime('now'))`,\n [dealId, contactId, tenantId]\n );\n }\n }\n await sqlite.execute(\n \"INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES ('mig_crm_relational_v1', 'done', 't_default', datetime('now'))\"\n );\n }\n } catch { /* non-critical: relational backfill retried next warm start (flag only set on success) */ }\n\n // ─── PER-TENANT KEY/UNIQUE REBUILDS (AUDIT FIXES 1-3) ───\n // Four tables got a tenant_id column but kept GLOBAL primary/unique keys + tenant-blind\n // OR-REPLACE/ON-CONFLICT writes, so they acted as ONE shared row across tenants. Each fix below\n // rebuilds the table with the key made PER-TENANT (SQLite can't ALTER a PK/UNIQUE in place), gated\n // by an app_config flag (mirrors mig_integrations_v2), NON-DESTRUCTIVE + idempotent: copy every row\n // assigning tenant_id (null → t_default), then drop+rename. Existing rows already carry\n // tenant_id='t_default' (Stage 1 ALTER + mig_tenant_v1), so ownership is preserved. The api/* read/\n // write paths are updated by their owners to key on tenant_id (the new PK/UNIQUE = their conflict target).\n\n // hasGlobalUniqueWithout(table, mustCols, absentCol): true when a UNIQUE index covers all mustCols\n // but NOT absentCol (the legacy global-unique shape) — so a FRESH per-tenant DB is never rebuilt.\n // Returns true on introspection failure (rebuild-to-be-safe).\n const hasGlobalUniqueWithout = async (\n table: string, mustCols: string[], absentCol: string\n ): Promise => {\n try {\n const idxList = await sqlite.execute(`PRAGMA index_list(${table})`);\n for (const row of (idxList.rows || [])) {\n if (!Number(Array.isArray(row) ? row[2] : (row as any).unique)) continue;\n const idxName = String(Array.isArray(row) ? row[1] : (row as any).name);\n const info = await sqlite.execute(`PRAGMA index_info(${idxName})`);\n const idxCols = (info.rows || []).map((r: any) => String(Array.isArray(r) ? r[2] : r.name));\n if (mustCols.every((c) => idxCols.includes(c)) && !idxCols.includes(absentCol)) return true;\n }\n return false;\n } catch { return true; }\n };\n const colsOf = async (table: string): Promise> => {\n try {\n const cols = await sqlite.execute(`PRAGMA table_info(${table})`);\n return new Set((cols.rows || []).map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n } catch { return new Set(); }\n };\n const setMigFlag = async (key: string): Promise => {\n // Flags are app-level: written at t_default with INSERT OR REPLACE so the unfiltered flag-gate\n // SELECTs (one row per key, all at t_default) keep resolving after app_config goes composite-PK.\n await sqlite.execute(\n `INSERT OR REPLACE INTO app_config (key, value, tenant_id, updated_at) VALUES (?, 'done', 't_default', datetime('now'))`,\n [key]\n );\n };\n\n // FIX 1: llm_config → PRIMARY KEY tenant_id (was id='default' singleton; one tenant clobbered all).\n // Keeps every column incl. the per-provider keys from the QA ALTER; `id` retained as a plain column.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_llm_config_tenant_v1'\");\n if (flag.rows.length === 0) {\n const lInfo = ((await sqlite.execute(\"PRAGMA table_info(llm_config)\")).rows || []) as any[];\n let tenantIsPk = false, hasIdCol = false;\n for (const r of lInfo) {\n const c = String(Array.isArray(r) ? r[1] : r.name);\n const pk = Number(Array.isArray(r) ? r[5] : (r as any).pk) || 0;\n if (c === \"id\") hasIdCol = true;\n if (c === \"tenant_id\" && pk > 0) tenantIsPk = true;\n }\n if (lInfo.length > 0 && hasIdCol && !tenantIsPk) {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS llm_config_v2 (\n tenant_id TEXT PRIMARY KEY,\n id TEXT DEFAULT 'default',\n provider TEXT NOT NULL DEFAULT 'anthropic',\n api_key TEXT NOT NULL DEFAULT '',\n endpoint TEXT,\n model TEXT NOT NULL DEFAULT 'claude-sonnet-4-6',\n updated_at TEXT,\n api_key_anthropic TEXT DEFAULT '',\n api_key_openai TEXT DEFAULT '',\n api_key_gemini TEXT DEFAULT '',\n api_key_custom TEXT DEFAULT '',\n endpoint_custom TEXT DEFAULT ''\n )\n `);\n await sqlite.execute(`\n INSERT OR IGNORE INTO llm_config_v2\n (tenant_id, id, provider, api_key, endpoint, model, updated_at,\n api_key_anthropic, api_key_openai, api_key_gemini, api_key_custom, endpoint_custom)\n SELECT COALESCE(tenant_id, 't_default'), COALESCE(id, 'default'),\n COALESCE(provider, 'anthropic'), COALESCE(api_key, ''), endpoint,\n COALESCE(model, 'claude-sonnet-4-6'), updated_at,\n COALESCE(api_key_anthropic, ''), COALESCE(api_key_openai, ''),\n COALESCE(api_key_gemini, ''), COALESCE(api_key_custom, ''), COALESCE(endpoint_custom, '')\n FROM llm_config\n `);\n await sqlite.execute(\"DROP TABLE llm_config\");\n await sqlite.execute(\"ALTER TABLE llm_config_v2 RENAME TO llm_config\");\n }\n await setMigFlag(\"mig_llm_config_tenant_v1\");\n }\n } catch { /* non-critical: retried next warm start (flag only set on success) */ }\n\n // FIX 2 (DELICATE): app_config → composite PRIMARY KEY (tenant_id, key) (was global key, so a\n // tenant's company_profile/north_star/config write overwrote another's row + stole its tenant_id).\n // Migration FLAGS (mig_*='done', all at t_default) keep working: each key has exactly one row at\n // t_default, so the unfiltered flag-gate SELECTs above still find them, and flag writes (INSERT OR\n // REPLACE at t_default) key on the new composite PK. Copy preserves each row's OWN tenant_id.\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_app_config_tenant_v1'\");\n if (flag.rows.length === 0) {\n const aInfo = ((await sqlite.execute(\"PRAGMA table_info(app_config)\")).rows || []) as any[];\n const aCols = new Set(aInfo.map((r: any) => String(Array.isArray(r) ? r[1] : r.name)));\n let tenantInPk = false;\n for (const r of aInfo) {\n const c = String(Array.isArray(r) ? r[1] : r.name);\n const pk = Number(Array.isArray(r) ? r[5] : (r as any).pk) || 0;\n if (c === \"tenant_id\" && pk > 0) tenantInPk = true;\n }\n if (aCols.size > 0 && aCols.has(\"tenant_id\") && !tenantInPk) {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS app_config_v2 (\n tenant_id TEXT NOT NULL DEFAULT 't_default',\n key TEXT NOT NULL,\n value TEXT,\n updated_at TEXT DEFAULT (datetime('now')),\n PRIMARY KEY (tenant_id, key)\n )\n `);\n await sqlite.execute(`\n INSERT OR IGNORE INTO app_config_v2 (tenant_id, key, value, updated_at)\n SELECT COALESCE(tenant_id, 't_default'), key, value, updated_at FROM app_config\n `);\n await sqlite.execute(\"DROP TABLE app_config\");\n await sqlite.execute(\"ALTER TABLE app_config_v2 RENAME TO app_config\");\n }\n await setMigFlag(\"mig_app_config_tenant_v1\");\n }\n } catch { /* non-critical: retried next warm start (flag only set on success) */ }\n\n // FIX 3a: intelligence_cache → UNIQUE(tenant_id, cache_key) (was global UNIQUE(cache_key); the\n // tenant-tagged INSERT OR REPLACE matched cache_key alone and clobbered another tenant's cache).\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_intelligence_cache_tenant_v1'\");\n if (flag.rows.length === 0) {\n const have = await colsOf(\"intelligence_cache\");\n if (have.size > 0 && await hasGlobalUniqueWithout(\"intelligence_cache\", [\"cache_key\"], \"tenant_id\")) {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS intelligence_cache_v2 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n cache_key TEXT NOT NULL,\n payload TEXT NOT NULL,\n generated_at TEXT DEFAULT (datetime('now')),\n expires_at TEXT NOT NULL,\n tenant_id TEXT,\n UNIQUE(tenant_id, cache_key)\n )\n `);\n await sqlite.execute(`\n INSERT OR IGNORE INTO intelligence_cache_v2 (cache_key, payload, generated_at, expires_at, tenant_id)\n SELECT cache_key, payload, generated_at, expires_at, COALESCE(tenant_id, 't_default')\n FROM intelligence_cache\n `);\n await sqlite.execute(\"DROP TABLE intelligence_cache\");\n await sqlite.execute(\"ALTER TABLE intelligence_cache_v2 RENAME TO intelligence_cache\");\n }\n await setMigFlag(\"mig_intelligence_cache_tenant_v1\");\n }\n } catch { /* non-critical: retried next warm start (flag only set on success) */ }\n\n // FIX 3b: metric_baselines → UNIQUE(tenant_id, integration, metric_key) (was global UNIQUE\n // (integration, metric_key)). Preserves the rolling_avg/sample_count columns from the A5 ALTER\n // (copied conditionally so the SELECT never references a column a legacy DB lacks).\n try {\n const flag = await sqlite.execute(\"SELECT value FROM app_config WHERE key = 'mig_metric_baselines_tenant_v1'\");\n if (flag.rows.length === 0) {\n const have = await colsOf(\"metric_baselines\");\n if (have.size > 0 && await hasGlobalUniqueWithout(\"metric_baselines\", [\"integration\", \"metric_key\"], \"tenant_id\")) {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS metric_baselines_v2 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n integration TEXT NOT NULL,\n metric_key TEXT NOT NULL,\n baseline_value REAL,\n baseline_period TEXT DEFAULT '30d',\n last_updated TEXT DEFAULT (datetime('now')),\n rolling_avg REAL,\n sample_count INTEGER DEFAULT 0,\n tenant_id TEXT,\n UNIQUE(tenant_id, integration, metric_key)\n )\n `);\n const rollingSel = have.has(\"rolling_avg\") ? \"rolling_avg\" : \"NULL\";\n const sampleSel = have.has(\"sample_count\") ? \"sample_count\" : \"0\";\n await sqlite.execute(`\n INSERT OR IGNORE INTO metric_baselines_v2\n (integration, metric_key, baseline_value, baseline_period, last_updated, rolling_avg, sample_count, tenant_id)\n SELECT integration, metric_key, baseline_value, baseline_period, last_updated,\n ${rollingSel}, ${sampleSel}, COALESCE(tenant_id, 't_default')\n FROM metric_baselines\n `);\n await sqlite.execute(\"DROP TABLE metric_baselines\");\n await sqlite.execute(\"ALTER TABLE metric_baselines_v2 RENAME TO metric_baselines\");\n }\n await setMigFlag(\"mig_metric_baselines_tenant_v1\");\n }\n } catch { /* non-critical: retried next warm start (flag only set on success) */ }\n\n // Seed default LLM config if absent. After FIX 1 the table is keyed by tenant_id, so seed the boot\n // tenant's row (t_default); column-guarded to also handle a not-yet-migrated id-keyed shape.\n try {\n const lHave = await colsOf(\"llm_config\");\n if (lHave.has(\"tenant_id\")) {\n const existing = await sqlite.execute(\"SELECT tenant_id FROM llm_config WHERE tenant_id = 't_default'\");\n if (existing.rows.length === 0) {\n await sqlite.execute(\n \"INSERT OR IGNORE INTO llm_config (tenant_id, id, provider, api_key, model, updated_at) VALUES ('t_default', 'default', 'anthropic', '', 'claude-sonnet-4-6', ?)\",\n [new Date().toISOString()]\n );\n }\n } else {\n const existing = await sqlite.execute(\"SELECT id FROM llm_config WHERE id = 'default'\");\n if (existing.rows.length === 0) {\n await sqlite.execute(\n \"INSERT INTO llm_config (id, provider, api_key, model, updated_at) VALUES ('default', 'anthropic', '', 'claude-sonnet-4-6', ?)\",\n [new Date().toISOString()]\n );\n }\n }\n } catch { /* non-critical: default LLM config seeded next warm start */ }\n\n // Demo-agent seeding disabled — starting from a clean agents table.\n // Re-enable by uncommenting the call below.\n // await seedDefaultAgents();\n}\n\nasync function seedDefaultAgents() {\n const existing = await sqlite.execute(\"SELECT id FROM agents LIMIT 1\");\n if (existing.rows.length > 0) return;\n\n const now = new Date().toISOString();\n for (const agent of DEFAULT_AGENT_SEEDS) {\n await sqlite.execute(\n `INSERT INTO agents (id, name, workspace, description, system_prompt, tools, model, enabled, created_at, updated_at, created_by)\n VALUES (?, ?, ?, ?, ?, ?, ?, 1, ?, ?, ?)`,\n [crypto.randomUUID(), agent.name, agent.workspace, agent.description, agent.system_prompt, agent.tools, agent.model, now, now, \"user\"]\n );\n }\n}\n",[192],"595dcf88-5f3a-11f1-aede-1607ee4eb77e","router.ts","30479b74-56ca-11f1-a296-ee650bb23af1",1077,["D",1780483963707],127,"import { sqlite, initDB, DEFAULT_AGENT_SEEDS, currentRole, currentTenantId, currentCtx } from \"./db.ts\";\nimport { registerUser, loginUser, getMe, upsertOAuthUser, bootstrapOwner } from \"./api/auth.ts\";\nimport { resolveSession, sessionSetCookie, sessionClearCookie } from \"./api/session.ts\";\nimport { newOAuthState, CLEAR_OAUTH_STATE, redirectWithCookie, oauthStateValid, identityLoginStart, identityLoginCallback } from \"./api/oauth_login.ts\";\nimport { listAgents, createAgent, updateAgent, deleteAgent } from \"./api/agents.ts\";\nimport { getLLMConfig, saveLLMConfig } from \"./api/llm.ts\";\nimport { handleChat, handleChatStream, listConversations, getConversationById, deleteConversation } from \"./api/chat.ts\";\nimport { handleIntegrationProxy, getCredentials, GOOGLE_SERVICES, MICROSOFT_SCOPES, getIntegrationSchema, listDescriptorNames } from \"./api/integrations.ts\";\nimport { serveAppModule } from \"./app_modules.ts\";\nimport { getFunnelSummary, getCorrelations, getRecommendations, getLeadScores, getAIOKpis, getIntelligenceFeed } from \"./api/intelligence.ts\";\nimport { listSignals, recordSignal, actionSignal, getHotLeads, getHotLeadsCount } from \"./api/signals.ts\";\nimport { listObjectives, createObjective, updateObjective, deleteObjective, createKeyResult, updateKeyResult, getOKRDashboard, syncObjectivesFromFunnel } from \"./api/okrs.ts\";\nimport { getNSM, setNSM, recordNSMValue, getNSMProgress } from \"./api/nsm.ts\";\nimport { listCohorts, createCohort, deleteCohort, getCohortSummary, getEngagementVelocity, getAttributionSummary, recordConversion, getRetention, getRevenueModel } from \"./api/aio.ts\";\nimport { getLatestDigest, generateDigest, getDigestHistory } from \"./api/digest.ts\";\nimport { listExperiments, createExperiment, updateExperiment, deleteExperiment, runExperiment, completeExperiment, getExperimentStats, suggestICE } from \"./api/experiments.ts\";\nimport { getUnifiedCalendar, getCalendarSummary, createCalendarEntry, updateCalendarEntry, deleteCalendarEntry } from \"./api/calendar_unified.ts\";\nimport { getGraphify } from \"./api/graphify.ts\";\nimport { listTasks, createTask, updateTask, deleteTask, listIceRankedTasks, generateTasksFromSource, runTask } from \"./api/tasks.ts\";\nimport { listDeals, getDeal, createDeal, updateDeal, deleteDeal, syncDealsFromLeads, listCandidates, importDeals } from \"./api/deals.ts\";\nimport { listKnowledgeFiles, getKnowledgeFile, createKnowledgeFile, updateKnowledgeFile, deleteKnowledgeFile, summarizeKnowledgeFile, buildKnowledgeGraph, AI_FEATURES } from \"./api/knowledge.ts\";\nimport { getCompanyProfile, setCompanyProfile, getBrandContext, setBrandContext } from \"./api/company.ts\";\nimport { enrichCompanyProfile } from \"./api/company-enrichment.ts\";\nimport { listMemory, addMemory, updateMemory, deleteMemory } from \"./api/memory.ts\";\nimport { listSkills, getSkill, setSkillState, createCustomSkill } from \"./api/skills.ts\";\nimport { getMethodConfig, saveMethodConfig, getMethodsMetadata } from \"./api/method_config.ts\";\nimport { listActions, enqueueAction, getAction, approveAction, rejectAction } from \"./api/actions.ts\";\nimport { processPendingRuns } from \"./cron.ts\";\nimport { getExecutedWorkCounts } from \"./api/propagate.ts\";\nimport { listCampaigns, getCampaign, getCampaignAgents, createCampaign, deleteCampaign, listCampaignRuns, orchestrateCampaignRun, updateCampaign, setCampaignTrigger } from \"./api/campaigns.ts\";\nimport { getWorldActivity, getWorldBoard, getWorldProject } from \"./api/world.ts\";\nimport { handlePlatformRoutes } from \"./api/platform.ts\";\nimport { isCrmPath, handleCrmRoutes } from \"./api/crm_routes.ts\";\nimport { isMarketingPath, handleMarketingRoutes } from \"./api/marketing_routes.ts\";\n\nconst corsHeaders = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, PUT, DELETE, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Authorization\",\n};\n\n// App version for the data-free health probe. Prefer the val.town deploy id (DENO_DEPLOYMENT_ID\n// is set by the runtime per deploy); fall back to a hardcoded release tag when running locally.\nconst APP_VERSION = (() => {\n try { return Deno.env.get(\"DENO_DEPLOYMENT_ID\") || \"p3-s3\"; } catch { return \"p3-s3\"; }\n})();\n\nfunction json(data: unknown, status = 200): Response {\n return new Response(JSON.stringify(data), {\n status,\n headers: {\n \"Content-Type\": \"application/json\",\n ...corsHeaders,\n },\n });\n}\n// JSON response that also carries a Set-Cookie header (session set/clear on auth endpoints).\nfunction jsonWithCookie(data: unknown, setCookie: string, status = 200): Response {\n const res = json(data, status);\n res.headers.append(\"Set-Cookie\", setCookie);\n return res;\n}\n// Best-effort client IP for per-IP rate limiting (val.town/Cloudflare forwarded headers).\nfunction clientIp(req: Request): string {\n return req.headers.get(\"x-forwarded-for\")?.split(\",\")[0].trim() || req.headers.get(\"cf-connecting-ip\") || \"\";\n}\n\n// Mask an integration row before returning it: NO endpoint may return secrets in cleartext\n// — only presence flags + a short masked hint.\nfunction maskIntegration(obj: any, googleConnected = false) {\n return {\n ...obj,\n connected: !!(obj.api_key || obj.access_token) ||\n (GOOGLE_SERVICES.includes(String(obj.name)) && googleConnected),\n has_api_key: !!obj.api_key,\n has_api_secret: !!obj.api_secret,\n has_access_token: !!obj.access_token,\n api_key: obj.api_key ? String(obj.api_key).slice(0, 4) + \"***\" : undefined,\n api_secret: obj.api_secret ? \"***\" : undefined,\n access_token: obj.access_token ? \"***\" : undefined,\n refresh_token: obj.refresh_token ? \"***\" : undefined,\n };\n}\n\n\nexport async function handleRequest(req: Request, url: URL): Promise {\n const { pathname } = url;\n const method = req.method;\n const tid = currentTenantId(); // tenant scope for every business query (stable within request)\n\n // CORS preflight\n if (method === \"OPTIONS\") {\n return new Response(null, { status: 204, headers: corsHeaders });\n }\n\n try {\n // GET /api/health — data-free liveness probe: { ok, version } (+ legacy status/timestamp).\n if (pathname === \"/api/health\" && method === \"GET\") {\n return json({ ok: true, version: APP_VERSION, status: \"ok\", timestamp: new Date().toISOString() });\n }\n\n // GET /api/health/auth - gated token verifier. Reaching this (past the main.ts gate) means the\n // APP_TOKEN is valid; the login screen uses it to confirm the password.\n if (pathname === \"/api/health/auth\" && method === \"GET\") {\n return json({ ok: true });\n }\n\n // --- Auth: email/password + session ----\n // Thin handlers — logic lives in api/auth.ts; the \"nexus_session\" cookie via api/session.ts.\n if (pathname === \"/api/auth/register\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const r = await registerUser({ email: body.email, password: body.password, name: body.name, honeypot: body.honeypot ?? body.company_url, turnstileToken: body.turnstileToken, ip: clientIp(req) });\n if (!r.ok) return json({ ok: false, error: r.error }, 400);\n return jsonWithCookie({ ok: true }, sessionSetCookie(r.ctxToken!));\n }\n if (pathname === \"/api/auth/login\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const r = await loginUser({ email: body.email, password: body.password, ip: clientIp(req) });\n if (!r.ok) return json({ ok: false, error: r.error }, 401);\n return jsonWithCookie({ ok: true }, sessionSetCookie(r.ctxToken!));\n }\n if (pathname === \"/api/auth/logout\" && method === \"POST\") {\n return jsonWithCookie({ ok: true }, sessionClearCookie());\n }\n if (pathname === \"/api/auth/me\" && method === \"GET\") {\n const ctx = await resolveSession(req);\n if (!ctx) return json({ ok: false, user: null }, 200);\n return json({ ok: true, ...(await getMe(ctx)) });\n }\n\n // GET /api/config - LLM runtime config + all app_config key/values\n if (pathname === \"/api/config\" && method === \"GET\") {\n const llmConfig = await getLLMConfig();\n const merged: Record = {\n provider: llmConfig.provider,\n model: llmConfig.model,\n has_key: (llmConfig as any).has_key,\n };\n try {\n const r = await sqlite.execute(\"SELECT key, value FROM app_config WHERE tenant_id = ?\", [tid]);\n for (const row of (r.rows || []) as any[]) {\n const k = String(row[0]);\n let v: any = row[1];\n if (typeof v === \"string\") { try { v = JSON.parse(v); } catch { /* keep raw string */ } }\n merged[k] = v;\n }\n } catch { /* app_config missing → just return LLM fields */ }\n return json(merged);\n }\n // POST /api/config - upsert arbitrary key/value into app_config (Settings toggles, ROI inputs…).\n if (pathname === \"/api/config\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const updated: Record = {};\n try {\n for (const [k, v] of Object.entries(body)) {\n if (k === \"provider\" || k === \"model\" || k === \"has_key\") continue;\n const value = typeof v === \"string\" ? v : JSON.stringify(v);\n await sqlite.execute(\n \"INSERT INTO app_config (key, value, tenant_id) VALUES (?, ?, ?) ON CONFLICT(key) DO UPDATE SET value = excluded.value, tenant_id = excluded.tenant_id, updated_at = datetime('now')\",\n [k, value, tid],\n );\n updated[k] = v;\n }\n return json({ success: true, updated });\n } catch (e: any) {\n return json({ error: String(e?.message || e) }, 500);\n }\n }\n\n // --- Frontend JS module routes (table lives in app_modules.ts to keep router under 80KB) ----\n if (method === \"GET\") {\n const _moduleSrc = serveAppModule(pathname);\n if (_moduleSrc !== null) {\n return new Response(_moduleSrc, {\n headers: {\n \"Content-Type\": \"application/javascript\",\n \"Cache-Control\": \"no-cache, no-store, must-revalidate\",\n ...corsHeaders,\n },\n });\n }\n }\n\n // --- Intelligence routes ----\n if (pathname === \"/api/intelligence/funnel-summary\" && method === \"GET\") {\n const force = url.searchParams.get(\"force\") === \"1\";\n const days = parseInt(url.searchParams.get(\"days\") ?? \"30\", 10);\n return json(await getFunnelSummary(force, days));\n }\n if (pathname === \"/api/intelligence/correlations\" && method === \"GET\") {\n const force = url.searchParams.get(\"force\") === \"1\";\n return json(await getCorrelations(force));\n }\n if (pathname === \"/api/intelligence/recommendations\" && method === \"GET\") {\n return json(await getRecommendations());\n }\n if (pathname === \"/api/intelligence/leads\" && method === \"GET\") {\n return json(await getLeadScores());\n }\n if (pathname === \"/api/intelligence/kpis\" && method === \"GET\") {\n return json(await getAIOKpis());\n }\n if (pathname === \"/api/intelligence/feed\" && method === \"GET\") {\n return json(await getIntelligenceFeed());\n }\n // Execution-aware metric: how much real work was completed (tasks/experiments).\n if (pathname === \"/api/intelligence/executed-work\" && method === \"GET\") {\n const days = parseInt(url.searchParams.get(\"days\") ?? \"30\", 10);\n return json(await getExecutedWorkCounts(days));\n }\n\n // --- Signals routes ----\n if (pathname === \"/api/signals\" && method === \"GET\") {\n const actioned = url.searchParams.get(\"actioned\");\n const lead_email = url.searchParams.get(\"lead_email\") ?? undefined;\n return json(await listSignals({ lead_email, actioned: actioned === \"true\" ? true : actioned === \"false\" ? false : undefined }));\n }\n if (pathname === \"/api/signals\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (!body.lead_email || !body.signal_type) {\n return json({ error: \"lead_email and signal_type are required\" }, 400);\n }\n return json(await recordSignal(body), 201);\n }\n const signalActionMatch = pathname.match(/^\\/api\\/signals\\/([^/]+)\\/action$/);\n if (signalActionMatch && method === \"PUT\") {\n const result = await actionSignal(signalActionMatch[1]);\n return result ? json(result) : json({ error: \"Signal not found\" }, 404);\n }\n if (pathname === \"/api/signals/hot-leads\" && method === \"GET\") {\n return json(await getHotLeads());\n }\n if (pathname === \"/api/signals/hot-leads/count\" && method === \"GET\") {\n return json({ count: await getHotLeadsCount() });\n }\n\n // --- OKR / Objectives routes ----\n if (pathname === \"/api/okrs\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n return json(await listObjectives({ workspace }));\n }\n if (pathname === \"/api/okrs\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createObjective(body), 201);\n }\n if (pathname === \"/api/okrs/dashboard\" && method === \"GET\") {\n return json(await getOKRDashboard());\n }\n // Explicit funnel→OKR sync — refreshes stored KR/objective progress on demand.\n if (pathname === \"/api/okrs/sync\" && method === \"POST\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n return json(await syncObjectivesFromFunnel({ workspace }));\n }\n const okrIdMatch = pathname.match(/^\\/api\\/okrs\\/([^/]+)$/);\n if (okrIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n return json(await updateObjective(okrIdMatch[1], body));\n }\n if (okrIdMatch && method === \"DELETE\") {\n const ok = await deleteObjective(okrIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n const okrKrMatch = pathname.match(/^\\/api\\/okrs\\/([^/]+)\\/key-results$/);\n if (okrKrMatch && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createKeyResult(okrKrMatch[1], body), 201);\n }\n const krIdMatch = pathname.match(/^\\/api\\/okrs\\/key-results\\/([^/]+)$/);\n if (krIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n return json(await updateKeyResult(krIdMatch[1], body));\n }\n\n // --- NSM routes ----\n if (pathname === \"/api/nsm\" && method === \"GET\") {\n return json(await getNSM());\n }\n if (pathname === \"/api/nsm\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await setNSM(body));\n }\n if (pathname === \"/api/nsm/record\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n await recordNSMValue(body.value);\n return json({ success: true });\n }\n if (pathname === \"/api/nsm/progress\" && method === \"GET\") {\n return json(await getNSMProgress());\n }\n\n // --- AIO / Cohort routes ----\n if (pathname === \"/api/aio/cohorts\" && method === \"GET\") {\n return json(await listCohorts());\n }\n if (pathname === \"/api/aio/cohorts\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createCohort(body), 201);\n }\n const cohortIdMatch = pathname.match(/^\\/api\\/aio\\/cohorts\\/([^/]+)$/);\n if (cohortIdMatch && method === \"DELETE\") {\n return json({ success: await deleteCohort(cohortIdMatch[1]) });\n }\n if (pathname === \"/api/aio/cohort-summary\" && method === \"GET\") {\n return json(await getCohortSummary());\n }\n if (pathname === \"/api/aio/engagement-velocity\" && method === \"GET\") {\n return json(await getEngagementVelocity());\n }\n\n // --- Frontend-expected aliases (cohorts/attribution/key-results) ----\n if (pathname === \"/api/cohorts\" && method === \"GET\") {\n return json(await getCohortSummary());\n }\n if (pathname === \"/api/cohorts\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createCohort(body), 201);\n }\n const cohortAliasMatch = pathname.match(/^\\/api\\/cohorts\\/([^/]+)$/);\n if (cohortAliasMatch && method === \"DELETE\") {\n return json({ success: await deleteCohort(cohortAliasMatch[1]) });\n }\n if (pathname === \"/api/attribution\" && method === \"GET\") {\n return json(await getAttributionSummary());\n }\n // retention (GRR/NRR + curve) + revenue model endpoints.\n if (pathname === \"/api/intelligence/retention\" && method === \"GET\") {\n return json(await getRetention());\n }\n if (pathname === \"/api/intelligence/revenue-model\" && method === \"GET\") {\n return json(await getRevenueModel());\n }\n if (pathname === \"/api/attribution\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n await recordConversion(body);\n return json({ success: true }, 201);\n }\n const krAliasMatch = pathname.match(/^\\/api\\/key-results\\/([^/]+)$/);\n if (krAliasMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n return json(await updateKeyResult(krAliasMatch[1], body));\n }\n\n // --- Digest routes ----\n if (pathname === \"/api/digest\" && method === \"GET\") {\n return json(await getLatestDigest());\n }\n if (pathname === \"/api/digest/generate\" && method === \"POST\") {\n return json(await generateDigest());\n }\n if (pathname === \"/api/digest/history\" && method === \"GET\") {\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"10\", 10);\n return json(await getDigestHistory(limit));\n }\n\n // --- Experiments routes ----\n if (pathname === \"/api/experiments\" && method === \"GET\") {\n const status = url.searchParams.get(\"status\") ?? undefined;\n const funnel_stage = url.searchParams.get(\"stage\") ?? url.searchParams.get(\"funnel_stage\") ?? undefined;\n const aarrr_lever = url.searchParams.get(\"aarrr_lever\") ?? undefined;\n return json(await listExperiments({ status, funnel_stage, aarrr_lever }));\n }\n if (pathname === \"/api/experiments\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createExperiment(body), 201);\n }\n if (pathname === \"/api/experiments/stats\" && method === \"GET\") {\n return json(await getExperimentStats());\n }\n // suggest-ice: alias of ice-suggest (same handler).\n if ((pathname === \"/api/experiments/ice-suggest\" || pathname === \"/api/experiments/suggest-ice\") && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await suggestICE(body));\n }\n // GET /api/experiments/:id/stats — sub-resource form; getExperimentStats() is tenant-aggregate (portfolio-wide).\n // Declared before the run|complete and bare :id matchers so \"stats\" isn't read as an id.\n const expStatsMatch = pathname.match(/^\\/api\\/experiments\\/([^/]+)\\/stats$/);\n if (expStatsMatch && method === \"GET\") {\n return json(await getExperimentStats());\n }\n const expActionMatch = pathname.match(/^\\/api\\/experiments\\/([^/]+)\\/(run|complete)$/);\n if (expActionMatch && method === \"POST\") {\n const id = expActionMatch[1];\n const body = await req.json().catch(() => ({}));\n const result = expActionMatch[2] === \"run\"\n ? await runExperiment(id, Number(body.baseline_value) || 0)\n : await completeExperiment(id, {\n result_value: Number(body.result_value) || 0,\n outcome: body.outcome ?? \"inconclusive\",\n learnings: body.learnings,\n control: body.control,\n variant: body.variant,\n sample_size: body.sample_size,\n });\n return result ? json(result) : json({ error: \"Experiment not found\" }, 404);\n }\n const expIdMatch = pathname.match(/^\\/api\\/experiments\\/([^/]+)$/);\n if (expIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const updated = await updateExperiment(expIdMatch[1], body);\n return updated ? json(updated) : json({ error: \"Experiment not found\" }, 404);\n }\n if (expIdMatch && method === \"DELETE\") {\n const ok = await deleteExperiment(expIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Calendar unified route ----\n if (pathname === \"/api/calendar/unified\" && method === \"GET\") {\n const today = new Date().toISOString().slice(0, 10);\n const from = url.searchParams.get(\"from\") ?? url.searchParams.get(\"start\") ?? today;\n const to = url.searchParams.get(\"to\") ?? url.searchParams.get(\"end\") ?? from;\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const sourcesParam = url.searchParams.getAll(\"source\");\n return json(await getUnifiedCalendar({ from, to, workspace, sources: sourcesParam.length ? sourcesParam as any : undefined }));\n }\n if (pathname === \"/api/calendar/summary\" && method === \"GET\") {\n const date = url.searchParams.get(\"date\") ?? new Date().toISOString().slice(0, 10);\n return json(await getCalendarSummary(date));\n }\n if (pathname === \"/api/calendar/entries\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createCalendarEntry(body), 201);\n }\n const calEntryMatch = pathname.match(/^\\/api\\/calendar\\/entries\\/([^/]+)$/);\n if (calEntryMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const updated = await updateCalendarEntry(calEntryMatch[1], body);\n return updated ? json(updated) : json({ error: \"Not found\" }, 404);\n }\n if (calEntryMatch && method === \"DELETE\") {\n const ok = await deleteCalendarEntry(calEntryMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Knowledge files (AI knowledge injection + graph view) ----\n if (pathname === \"/api/knowledge\" && method === \"GET\") {\n return json(await listKnowledgeFiles());\n }\n if (pathname === \"/api/knowledge\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const created = await createKnowledgeFile(body);\n return created ? json(created, 201) : json({ error: \"content required\" }, 400);\n }\n if (pathname === \"/api/knowledge/graph\" && method === \"GET\") {\n return json(await buildKnowledgeGraph());\n }\n if (pathname === \"/api/knowledge/features\" && method === \"GET\") {\n return json(AI_FEATURES);\n }\n const knSummarizeMatch = pathname.match(/^\\/api\\/knowledge\\/([^/]+)\\/summarize$/);\n if (knSummarizeMatch && method === \"POST\") {\n const summary = await summarizeKnowledgeFile(knSummarizeMatch[1]);\n const file = await getKnowledgeFile(knSummarizeMatch[1]);\n return json({ summary, tokens_estimate: file?.tokens_estimate ?? 0 });\n }\n const knIdMatch = pathname.match(/^\\/api\\/knowledge\\/([^/]+)$/);\n if (knIdMatch && method === \"GET\") {\n const f = await getKnowledgeFile(knIdMatch[1]);\n return f ? json(f) : json({ error: \"Not found\" }, 404);\n }\n if (knIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const updated = await updateKnowledgeFile(knIdMatch[1], body);\n return updated ? json(updated) : json({ error: \"Not found\" }, 404);\n }\n if (knIdMatch && method === \"DELETE\") {\n const ok = await deleteKnowledgeFile(knIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Company Profile + Brand Context (Part 15.2) — the business identity ---\n if (pathname === \"/api/company-profile\" && method === \"GET\") {\n return json(await getCompanyProfile());\n }\n if (pathname === \"/api/company-profile\" && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n return json(await setCompanyProfile(body));\n }\n if (pathname === \"/api/company-profile/enrich\" && method === \"POST\") {\n try {\n const enriched = await enrichCompanyProfile();\n return json({ enriched, message: \"AI enrichment complete — review and save.\" });\n } catch (e) {\n return json({ error: String(e?.message ?? e) }, 500);\n }\n }\n if (pathname === \"/api/brand-context\" && method === \"GET\") {\n return json({ text: await getBrandContext() });\n }\n if (pathname === \"/api/brand-context\" && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n await setBrandContext(String(body.text ?? \"\"));\n return json({ ok: true });\n }\n\n // --- Memory (Part 15.3) — persistent evolving business facts ----\n if (pathname === \"/api/memory\" && method === \"GET\") {\n return json(await listMemory());\n }\n if (pathname === \"/api/memory\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const m = await addMemory(body);\n return m ? json(m, 201) : json({ error: \"content required\" }, 400);\n }\n const memIdMatch = pathname.match(/^\\/api\\/memory\\/([0-9]+)$/);\n if (memIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const m = await updateMemory(Number(memIdMatch[1]), body);\n return m ? json(m) : json({ error: \"Not found\" }, 404);\n }\n if (memIdMatch && method === \"DELETE\") {\n const ok = await deleteMemory(Number(memIdMatch[1]));\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Skills (Playbooks) — marketing skill catalog + per-skill state ----\n if (pathname === \"/api/skills\" && method === \"GET\") {\n return json(await listSkills());\n }\n if (pathname === \"/api/skills\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const created = await createCustomSkill(body);\n return created.ok ? json(created, 201) : json({ error: created.error || \"invalid skill\" }, 400);\n }\n const skillNameMatch = pathname.match(/^\\/api\\/skills\\/([^/]+)$/);\n if (skillNameMatch && method === \"GET\") {\n const s = await getSkill(decodeURIComponent(skillNameMatch[1]));\n return s ? json(s) : json({ error: \"Not found\" }, 404);\n }\n if (skillNameMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const ok = await setSkillState(decodeURIComponent(skillNameMatch[1]), body);\n return ok ? json({ ok: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Method config (prioritization methodology: ICE/RICE/WSJF) ----\n if (pathname === \"/api/method-config/methods\" && method === \"GET\") {\n return json(getMethodsMetadata());\n }\n if (pathname === \"/api/method-config\" && method === \"GET\") {\n return json(await getMethodConfig());\n }\n if (pathname === \"/api/method-config\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await saveMethodConfig(body));\n }\n\n // --- Action queue (human-in-the-loop execution) ----\n if (pathname === \"/api/actions\" && method === \"GET\") {\n const status = url.searchParams.get(\"status\") ?? undefined;\n return json(await listActions(status));\n }\n if (pathname === \"/api/actions\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const created = await enqueueAction(body);\n return created ? json(created, 201) : json({ error: \"integration + endpoint required\" }, 400);\n }\n const actionApproveMatch = pathname.match(/^\\/api\\/actions\\/([^/]+)\\/approve$/);\n if (actionApproveMatch && method === \"POST\") {\n const a = await approveAction(actionApproveMatch[1]);\n return a ? json(a) : json({ error: \"Not found\" }, 404);\n }\n const actionRejectMatch = pathname.match(/^\\/api\\/actions\\/([^/]+)\\/reject$/);\n if (actionRejectMatch && method === \"POST\") {\n const a = await rejectAction(actionRejectMatch[1]);\n return a ? json(a) : json({ error: \"Not found\" }, 404);\n }\n const actionIdMatch = pathname.match(/^\\/api\\/actions\\/([^/]+)$/);\n if (actionIdMatch && method === \"GET\") {\n const a = await getAction(actionIdMatch[1]);\n return a ? json(a) : json({ error: \"Not found\" }, 404);\n }\n\n // Manual trigger for the execution engine (the interval val also calls processPendingRuns).\n if (pathname === \"/api/agents/run-pending\" && method === \"POST\") {\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"3\", 10);\n return json(await processPendingRuns(limit));\n }\n\n // GET /api/agents/activity?limit=N — cross-agent run feed; before the /api/agents/:id matchers.\n if (pathname === \"/api/agents/activity\" && method === \"GET\") {\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"20\", 10);\n const r = await sqlite.execute(\n `SELECT r.id, r.agent_id, a.name, a.workspace, r.status, r.started_at, r.finished_at, r.output\n FROM agent_runs r LEFT JOIN agents a ON a.id = r.agent_id AND a.tenant_id = ?\n WHERE r.tenant_id = ?\n ORDER BY r.started_at DESC LIMIT ?`,\n [tid, tid, limit]\n );\n return json({ activity: r.rows.map((row) => rowToObject(r.columns, row)) });\n }\n\n // --- Agent runs ----\n const agentRunsMatch = pathname.match(/^\\/api\\/agents\\/([^/]+)\\/runs$/);\n if (agentRunsMatch && method === \"GET\") {\n const agent_id = agentRunsMatch[1];\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"20\", 10);\n const result = await sqlite.execute(\n \"SELECT * FROM agent_runs WHERE agent_id = ? AND tenant_id = ? ORDER BY started_at DESC LIMIT ?\",\n [agent_id, tid, limit]\n );\n return json(result.rows.map((row) => rowToObject(result.columns, row)));\n }\n if (agentRunsMatch && method === \"POST\") {\n const agent_id = agentRunsMatch[1];\n const body = await req.json().catch(() => ({}));\n const id = crypto.randomUUID();\n const now = new Date().toISOString();\n await sqlite.execute(\n \"INSERT INTO agent_runs (id, agent_id, trigger, status, started_at, tenant_id) VALUES (?, ?, ?, ?, ?, ?)\",\n [id, agent_id, body.trigger ?? \"manual\", \"pending\", now, tid]\n );\n // App-level automation (no cron): run the queued job in-request. Fire-and-forget; status='running' claim guards dupes.\n processPendingRuns(1).catch(() => {});\n return json({ id, agent_id, trigger: body.trigger ?? \"manual\", status: \"pending\", started_at: now }, 201);\n }\n\n // Integrations routes\n // GET /api/integrations - list all integrations\n if (pathname === \"/api/integrations\" && method === \"GET\") {\n const result = await sqlite.execute(\"SELECT * FROM integrations WHERE tenant_id = ? ORDER BY name ASC\", [tid]);\n const rows = result.rows.map((row) => rowToObject(result.columns, row));\n // Google-family services inherit the central 'google' OAuth token, so they are \"connected\"\n // whenever google is — even without their own api_key/access_token row.\n const googleConnected = rows.some((o) => o.name === \"google\" && o.access_token);\n const integrations = rows.map((obj) => maskIntegration(obj, googleConnected));\n return json(integrations);\n }\n\n // POST /api/integrations/:name - upsert integration credentials\n const integrationUpsertMatch = pathname.match(/^\\/api\\/integrations\\/([^/]+)$/);\n if (integrationUpsertMatch && method === \"POST\") {\n const name = integrationUpsertMatch[1];\n const body = await req.json().catch(() => ({}));\n const now = new Date().toISOString();\n\n const existing = await sqlite.execute(\n \"SELECT id, api_key, api_secret, access_token, refresh_token, extra_config FROM integrations WHERE name = ? AND tenant_id = ?\",\n [name, tid]\n );\n\n // Keep the existing secret when the incoming value is blank/missing/masked (\"***\"),\n // so saving the masked Settings form does not wipe stored credentials.\n const keepSecret = (incoming: any, current: any) => {\n if (incoming == null) return current ?? null;\n const s = String(incoming);\n if (s === \"\" || s.includes(\"***\")) return current ?? null;\n return incoming;\n };\n\n if (existing.rows.length > 0) {\n const cur = existing.rows[0] as any[]; // [id, api_key, api_secret, access_token, refresh_token, extra_config]\n await sqlite.execute(\n `UPDATE integrations SET\n api_key = ?,\n api_secret = ?,\n access_token = ?,\n refresh_token = ?,\n extra_config = ?,\n updated_at = ?\n WHERE name = ? AND tenant_id = ?`,\n [\n keepSecret(body.api_key, cur[1]),\n keepSecret(body.api_secret, cur[2]),\n keepSecret(body.access_token, cur[3]),\n keepSecret(body.refresh_token, cur[4]),\n body.extra_config != null ? JSON.stringify(body.extra_config) : (cur[5] ?? null),\n now,\n name,\n tid,\n ]\n );\n } else {\n // Insert\n const id = crypto.randomUUID();\n await sqlite.execute(\n `INSERT INTO integrations (id, name, api_key, api_secret, access_token, refresh_token, extra_config, connected_at, updated_at, tenant_id)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [\n id,\n name,\n body.api_key ?? null,\n body.api_secret ?? null,\n body.access_token ?? null,\n body.refresh_token ?? null,\n body.extra_config != null ? JSON.stringify(body.extra_config) : null,\n now,\n now,\n tid,\n ]\n );\n }\n\n const updated = await sqlite.execute(\n \"SELECT * FROM integrations WHERE name = ? AND tenant_id = ?\",\n [name, tid]\n );\n const raw = rowToObject(updated.columns, updated.rows[0]);\n // NEVER return the just-saved secrets in cleartext — mask like the list endpoint.\n const googleConnected = raw.name === \"google\"\n ? !!raw.access_token\n : !!(await getCredentials(\"google\"))?.access_token;\n return json({ success: true, integration: maskIntegration(raw, googleConnected) });\n }\n\n // DELETE /api/integrations/:name\n const integrationDeleteMatch = pathname.match(/^\\/api\\/integrations\\/([^/]+)$/);\n if (integrationDeleteMatch && method === \"DELETE\") {\n const name = integrationDeleteMatch[1];\n await sqlite.execute(\"DELETE FROM integrations WHERE name = ? AND tenant_id = ?\", [name, tid]);\n return json({ success: true, message: `Integration '${name}' deleted` });\n }\n\n // Part 13: GET /api/integrations/:name/schema — descriptor-generated action/trigger schema\n // (UI form fields + AI tool hints). null when the integration is still hand-written.\n const integrationSchemaMatch = pathname.match(/^\\/api\\/integrations\\/([^/]+)\\/schema$/);\n if (integrationSchemaMatch && method === \"GET\") {\n const schema = getIntegrationSchema(integrationSchemaMatch[1]);\n return schema ? json(schema) : json({ error: \"no descriptor for this integration yet\", descriptor_integrations: listDescriptorNames() }, 404);\n }\n\n // GET|POST /api/integrations/:name/data - proxy to integration API\n const integrationDataMatch = pathname.match(/^\\/api\\/integrations\\/([^/]+)\\/data$/);\n if (integrationDataMatch && (method === \"GET\" || method === \"POST\")) {\n const name = integrationDataMatch[1];\n const credentials = await getCredentials(name);\n if (!credentials) {\n return json({ error: `Integration '${name}' not found or not configured` }, 404);\n }\n return handleIntegrationProxy(req, name, url.searchParams, credentials);\n }\n\n // Agents routes\n // GET /api/agents - list agents (optional ?workspace= query param)\n if (pathname === \"/api/agents\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const agents = await listAgents(workspace);\n return json(agents);\n }\n\n // POST /api/agents - create agent\n if (pathname === \"/api/agents\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const agent = await createAgent(body);\n return json(agent, 201);\n }\n\n // PUT /api/agents/:id - update agent\n const agentUpdateMatch = pathname.match(/^\\/api\\/agents\\/([^/]+)$/);\n if (agentUpdateMatch && method === \"PUT\") {\n const id = agentUpdateMatch[1];\n const body = await req.json().catch(() => ({}));\n const agent = await updateAgent(id, body);\n if (!agent) {\n return json({ error: `Agent '${id}' not found` }, 404);\n }\n return json(agent);\n }\n\n // DELETE /api/agents/:id - delete agent\n const agentDeleteMatch = pathname.match(/^\\/api\\/agents\\/([^/]+)$/);\n if (agentDeleteMatch && method === \"DELETE\") {\n const id = agentDeleteMatch[1];\n const deleted = await deleteAgent(id);\n if (!deleted) {\n return json({ error: `Agent '${id}' not found` }, 404);\n }\n return json({ success: true, message: `Agent '${id}' deleted` });\n }\n\n // LLM config routes\n // GET /api/llm/config - get LLM config (mask api_key)\n if (pathname === \"/api/llm/config\" && method === \"GET\") {\n const config = await getLLMConfig();\n if (!config) {\n return json({ error: \"LLM config not found\" }, 404);\n }\n // Mask api_key: show first 8 chars + ***\n const masked = {\n ...config,\n api_key: config.api_key\n ? config.api_key.slice(0, 8) + \"***\"\n : \"\",\n };\n return json(masked);\n }\n\n // POST /api/llm/config - save LLM config\n if (pathname === \"/api/llm/config\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const config = await saveLLMConfig(body);\n // Mask api_key in response\n const masked = {\n ...config,\n api_key: config.api_key\n ? config.api_key.slice(0, 8) + \"***\"\n : \"\",\n };\n return json(masked);\n }\n\n // Chat route\n // POST /api/chat - AI chat handler\n if (pathname === \"/api/chat\" && method === \"POST\") {\n return handleChat(req);\n }\n\n // POST /api/chat/stream - SSE streaming chat\n if (pathname === \"/api/chat/stream\" && method === \"POST\") {\n return handleChatStream(req);\n }\n\n // --- Conversation history routes ----\n if (pathname === \"/api/conversations\" && method === \"GET\") {\n return json(await listConversations());\n }\n const convIdMatch = pathname.match(/^\\/api\\/conversations\\/([^/]+)$/);\n if (convIdMatch && method === \"GET\") {\n const conv = await getConversationById(convIdMatch[1]);\n return conv ? json(conv) : json({ error: \"Not found\" }, 404);\n }\n if (convIdMatch && method === \"DELETE\") {\n const ok = await deleteConversation(convIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // Tasks routes\n // GET /api/tasks - list tasks (optional ?workspace= query param)\n if (pathname === \"/api/tasks\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const status = url.searchParams.get(\"status\") ?? undefined;\n return json(await listTasks({ workspace, status }));\n }\n\n // POST /api/tasks - create task\n if (pathname === \"/api/tasks\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createTask(body), 201);\n }\n\n // GET /api/tasks/ice - ICE-ranked backlog (sorted by ICE score desc).\n // Declared before the /api/tasks/:id matchers so \"ice\" isn't read as an id.\n if (pathname === \"/api/tasks/ice\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"50\", 10);\n return json(await listIceRankedTasks({ workspace, limit }));\n }\n\n // POST /api/tasks/generate - LLM-plan tasks from an experiment/KR/objective/campaign.\n if (pathname === \"/api/tasks/generate\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (!body.source_type || !body.source_id) {\n return json({ error: \"source_type and source_id are required\" }, 400);\n }\n return json(await generateTasksFromSource(body), 201);\n }\n\n // POST /api/tasks/:id/run - execute the task with an agent (sub-resource; before the bare :id matchers).\n const taskRunMatch = pathname.match(/^\\/api\\/tasks\\/([^/]+)\\/run$/);\n if (taskRunMatch && method === \"POST\") {\n const result = await runTask(taskRunMatch[1]);\n if (!result.task) return json({ error: result.error || \"Not found\" }, 404);\n return json(result);\n }\n\n // PUT /api/tasks/:id - update task (e.g. toggle status)\n const taskUpdateMatch = pathname.match(/^\\/api\\/tasks\\/([^/]+)$/);\n if (taskUpdateMatch && method === \"PUT\") {\n const id = taskUpdateMatch[1];\n const body = await req.json().catch(() => ({}));\n const updated = await updateTask(id, body);\n if (!updated) return json({ error: \"Not found\" }, 404);\n return json(updated);\n }\n\n // DELETE /api/tasks/:id - delete task\n const taskDeleteMatch = pathname.match(/^\\/api\\/tasks\\/([^/]+)$/);\n if (taskDeleteMatch && method === \"DELETE\") {\n const ok = await deleteTask(taskDeleteMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Relational CRM (accounts + contacts + deal↔contact links) ----\n // Delegate to api/crm_routes.ts; tenant context is already set by main.ts. Kept thin to stay under the 80KB file limit.\n if (isCrmPath(pathname)) return handleCrmRoutes(req, url, method);\n if (isMarketingPath(pathname)) return handleMarketingRoutes(req, url, method);\n\n // --- CRM Deals (sales pipeline) ----\n if (pathname === \"/api/deals\" && method === \"GET\") {\n return json(await listDeals(url.searchParams.get(\"stage\") ?? undefined));\n }\n if (pathname === \"/api/deals\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createDeal(body), 201);\n }\n // Seed deals from qualified leads (explicit; checked before the :id matcher).\n if (pathname === \"/api/deals/sync\" && method === \"POST\") {\n return json(await syncDealsFromLeads());\n }\n // Interactive import: candidate leads to review, then import the selected emails.\n if (pathname === \"/api/deals/candidates\" && method === \"GET\") {\n return json(await listCandidates());\n }\n if (pathname === \"/api/deals/import\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await importDeals(Array.isArray(body.emails) ? body.emails : []));\n }\n const dealIdMatch = pathname.match(/^\\/api\\/deals\\/([^/]+)$/);\n if (dealIdMatch && dealIdMatch[1] !== \"sync\" && method === \"GET\") {\n const d = await getDeal(dealIdMatch[1]);\n return d ? json(d) : json({ error: \"Not found\" }, 404);\n }\n if (dealIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n const updated = await updateDeal(dealIdMatch[1], body);\n return updated ? json(updated) : json({ error: \"Not found\" }, 404);\n }\n if (dealIdMatch && method === \"DELETE\") {\n const ok = await deleteDeal(dealIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // --- Data export (CSV) ----\n if (pathname === \"/api/export/signals\" && method === \"GET\") {\n const r = await sqlite.execute(\"SELECT id, lead_email, lead_name, signal_type, signal_source, signal_strength, actioned, detected_at FROM intent_signals WHERE tenant_id = ? ORDER BY detected_at DESC\", [tid]);\n const cols = r.columns;\n let csv = cols.join(\",\") + \"\\n\";\n for (const row of r.rows) {\n csv += (row as any[]).map((v) => `\"${String(v ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\") + \"\\n\";\n }\n return new Response(csv, { headers: { \"Content-Type\": \"text/csv\", \"Content-Disposition\": 'attachment; filename=\"signals.csv\"', ...corsHeaders } });\n }\n if (pathname === \"/api/export/okrs\" && method === \"GET\") {\n const objR = await sqlite.execute(\"SELECT id, title, description, workspace, status, progress, quarter, created_at FROM objectives WHERE tenant_id = ? ORDER BY created_at DESC\", [tid]);\n const krR = await sqlite.execute(\"SELECT id, objective_id, title, target_value, current_value, unit, progress, updated_at FROM key_results WHERE tenant_id = ? ORDER BY updated_at DESC\", [tid]);\n let csv = \"type,\" + objR.columns.join(\",\") + \"\\n\";\n for (const row of objR.rows) {\n csv += \"objective,\" + (row as any[]).map((v) => `\"${String(v ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\") + \"\\n\";\n }\n if (krR.rows.length > 0) {\n csv += \"\\ntype,\" + krR.columns.join(\",\") + \"\\n\";\n for (const row of krR.rows) {\n csv += \"key_result,\" + (row as any[]).map((v) => `\"${String(v ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\") + \"\\n\";\n }\n }\n return new Response(csv, { headers: { \"Content-Type\": \"text/csv\", \"Content-Disposition\": 'attachment; filename=\"okrs.csv\"', ...corsHeaders } });\n }\n if (pathname === \"/api/export/tasks\" && method === \"GET\") {\n const r = await sqlite.execute(\"SELECT id, title, description, status, priority, workspace, due_date, created_at, updated_at FROM tasks WHERE tenant_id = ? ORDER BY created_at DESC\", [tid]);\n const cols = r.columns;\n let csv = cols.join(\",\") + \"\\n\";\n for (const row of r.rows) {\n csv += (row as any[]).map((v) => `\"${String(v ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\") + \"\\n\";\n }\n return new Response(csv, { headers: { \"Content-Type\": \"text/csv\", \"Content-Disposition\": 'attachment; filename=\"tasks.csv\"', ...corsHeaders } });\n }\n\n // --- LinkedIn OAuth flow (legacy aliases) ----\n if (pathname === \"/api/oauth/linkedin/start\" && method === \"GET\") {\n return Response.redirect(`${url.origin}/api/auth/linkedin`);\n }\n if (pathname === \"/api/oauth/linkedin/callback\" && method === \"GET\") {\n return Response.redirect(`${url.origin}/api/auth/linkedin/callback?${url.search.slice(1)}`);\n }\n\n // --- Graphify - app map for MCP / AI-native navigation ----\n if (pathname === \"/api/graphify\" && method === \"GET\") {\n return json(await getGraphify());\n }\n\n // --- Calendar today shortcut (TodayPanel widget) ----\n if (pathname === \"/api/calendar/today\" && method === \"GET\") {\n const today = new Date().toISOString().slice(0, 10);\n return json(await getUnifiedCalendar({ from: today, to: today }));\n }\n\n // --- Digest latest alias ----\n if (pathname === \"/api/digest/latest\" && method === \"GET\") {\n return json(await getLatestDigest());\n }\n\n // --- PLATFORM SUPERADMIN (master-only; the single audited exception to tenant isolation) ----\n // GATE first, then delegate all /api/platform/* routes to handlePlatformRoutes (api/platform.ts).\n if (pathname.startsWith(\"/api/platform/\")) {\n if (!currentCtx().isPlatformAdmin) return json({ error: \"Forbidden: platform admin only\" }, 403);\n return handlePlatformRoutes(req, url, method);\n }\n\n // --- ADMIN LOCKDOWN: every destructive /api/admin/* route is owner-only (plus per-route confirm\n // tokens below). No-op under default SYSTEM_CTX (role \"owner\"); locks out trial members. ----\n if (pathname.startsWith(\"/api/admin/\") && currentRole() !== \"owner\") {\n return json({ error: \"Forbidden: owner role required\" }, 403);\n }\n\n // Owner bootstrap (owner-only): create/attach the platform owner to t_default for email+password login. Idempotent.\n if (pathname === \"/api/admin/bootstrap-owner\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (!body.email || !body.password) return json({ ok: false, error: \"email + password required\" }, 400);\n const r = await bootstrapOwner(String(body.email), String(body.password));\n return json({ ok: r.ok, created: r.created });\n }\n\n // Admin backup/export (owner-only): read-only, tenant-scoped (WHERE tenant_id=?) JSON snapshot of this tenant's rows.\n // Confirm is optional: pass { confirm: \"EXPORT\" } to be explicit, otherwise it just runs.\n if (pathname === \"/api/admin/export\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm != null && body.confirm !== \"EXPORT\") {\n return json({ ok: false, error: \"Send confirm: EXPORT (or omit confirm)\" }, 400);\n }\n const EXPORT_TABLES = [\n \"tasks\", \"deals\", \"campaigns\", \"objectives\", \"key_results\", \"experiments\",\n \"agents\", \"action_queue\", \"calendar_entries\", \"knowledge_files\", \"notes\",\n ];\n const data: Record = {};\n const counts: Record = {};\n for (const table of EXPORT_TABLES) {\n try {\n const r = await sqlite.execute(`SELECT * FROM ${table} WHERE tenant_id = ?`, [tid]);\n const rows = (r.rows || []).map((row) => rowToObject(r.columns, row));\n data[table] = rows;\n counts[table] = rows.length;\n } catch {\n // Missing table (e.g. not yet created): record empty rather than failing the whole export.\n data[table] = [];\n counts[table] = 0;\n }\n }\n const snapshot = {\n ok: true,\n tenant_id: tid,\n version: APP_VERSION,\n exported_at: new Date().toISOString(),\n tables: EXPORT_TABLES,\n counts,\n data,\n };\n const filename = `nexus-backup-${tid}-${new Date().toISOString().slice(0, 10)}.json`;\n return new Response(JSON.stringify(snapshot, null, 2), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"Content-Disposition\": `attachment; filename=\"${filename}\"`,\n \"Cache-Control\": \"no-store\",\n ...corsHeaders,\n },\n });\n }\n\n // --- Admin purge (clear intelligence cache) ----\n if (pathname === \"/api/admin/purge\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"PURGE\") return json({ error: \"Send confirm: PURGE\" }, 400);\n await sqlite.execute(\"DELETE FROM intelligence_cache WHERE tenant_id = ?\", [tid]).catch(() => {});\n await sqlite.execute(\"UPDATE app_config SET value=NULL WHERE key='funnel_cache' AND tenant_id = ?\", [tid]).catch(() => {});\n return json({ success: true, message: \"Cache purged\" });\n }\n\n // Remove demo/seed data: starter agents matched exactly on (name, description, system_prompt) so edits are never touched.\n if (pathname === \"/api/admin/seed-status\" && method === \"GET\") {\n let agents = 0;\n for (const seed of DEFAULT_AGENT_SEEDS) {\n const r = await sqlite.execute(\n \"SELECT COUNT(*) FROM agents WHERE name = ? AND description = ? AND system_prompt = ? AND tenant_id = ?\",\n [seed.name, seed.description, seed.system_prompt, tid],\n ).catch(() => null);\n if (r?.rows?.length) agents += Number((r.rows[0] as any[])[0]) || 0;\n }\n return json({ ok: true, total: agents, counts: { agents } });\n }\n if (pathname === \"/api/admin/cleanup-mock\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"CLEANUP\") return json({ ok: false, error: \"Send confirm: CLEANUP\" }, 400);\n let removed = 0;\n for (const seed of DEFAULT_AGENT_SEEDS) {\n const r = await sqlite.execute(\n \"DELETE FROM agents WHERE name = ? AND description = ? AND system_prompt = ? AND tenant_id = ?\",\n [seed.name, seed.description, seed.system_prompt, tid],\n ).catch(() => null);\n removed += Number((r as any)?.rowsAffected ?? 0) || 0;\n }\n return json({ ok: true, total: removed });\n }\n\n // Bulk reset: wipe ALL agents (= agents + campaigns, same table) plus related\n // run/budget/conversation rows that deleteAgent() does not cascade. Confirm-guarded.\n if (pathname === \"/api/admin/reset-agents\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"CLEANUP\") return json({ ok: false, error: \"Send confirm: CLEANUP\" }, 400);\n const counts: Record = {};\n for (const table of [\"agents\", \"agent_runs\", \"agent_budgets\", \"conversations\"]) {\n const r = await sqlite.execute(`DELETE FROM ${table} WHERE tenant_id = ?`, [tid]).catch(() => null);\n counts[table] = Number((r as any)?.rowsAffected ?? 0) || 0;\n }\n return json({ ok: true, counts });\n }\n\n // Factory reset (this app + tenant only): wipe the data tables below, keeping integrations/llm_config/knowledge_files. Confirm-guarded.\n if (pathname === \"/api/admin/reset-data\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"CLEANUP\") return json({ ok: false, error: \"Send confirm: CLEANUP\" }, 400);\n const WIPE_TABLES = [\n \"agents\", \"conversations\", \"agent_budgets\", \"agent_runs\",\n \"objectives\", \"key_results\", \"north_star\", \"nsm_history\",\n \"experiments\", \"tasks\", \"tasks_legacy\", \"notes\",\n \"intent_signals\", \"growth_digests\", \"calendar_entries\", \"action_queue\",\n \"customer_cohorts\", \"lead_state_changes\", \"utm_conversions\",\n \"metric_baselines\", \"metric_history\", \"intelligence_cache\", \"app_config\",\n ];\n const counts: Record = {};\n for (const table of WIPE_TABLES) {\n const r = await sqlite.execute(`DELETE FROM ${table} WHERE tenant_id = ?`, [tid]).catch(() => null);\n counts[table] = Number((r as any)?.rowsAffected ?? 0) || 0;\n }\n return json({ ok: true, kept: [\"integrations\", \"llm_config\", \"knowledge_files\"], counts });\n }\n\n // Precise working-data wipe (KEEPS knowledge_files/skills/memory/app_config/integrations/llm_config). Confirm-guarded.\n if (pathname === \"/api/admin/wipe-working-data\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"WIPE\") return json({ ok: false, error: \"Send confirm: WIPE\" }, 400);\n const WIPE = [\n \"agents\", \"agent_runs\", \"agent_budgets\", \"conversations\", \"deals\",\n \"experiments\", \"tasks\", \"tasks_legacy\", \"objectives\", \"key_results\",\n \"north_star\", \"nsm_history\", \"intent_signals\", \"growth_digests\",\n \"calendar_entries\", \"action_queue\", \"customer_cohorts\", \"lead_state_changes\",\n \"utm_conversions\", \"metric_baselines\", \"metric_history\", \"intelligence_cache\", \"notes\",\n ];\n const counts: Record = {};\n for (const table of WIPE) {\n const r = await sqlite.execute(`DELETE FROM ${table} WHERE tenant_id = ?`, [tid]).catch(() => null);\n counts[table] = Number((r as any)?.rowsAffected ?? 0) || 0;\n }\n return json({\n ok: true,\n kept: [\"knowledge_files\", \"skills\", \"memory\", \"app_config\", \"integrations\", \"llm_config\"],\n counts,\n });\n }\n\n // --- Targeted wipe: tasks + run history + agents/budgets + campaign↔agent links ONLY ----\n // Keeps campaigns/objectives/everything else. Confirm-guarded.\n if (pathname === \"/api/admin/wipe-tasks-agents\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"WIPE\") return json({ ok: false, error: \"Send confirm: WIPE\" }, 400);\n const WIPE = [\"tasks\", \"tasks_legacy\", \"agent_runs\", \"agent_budgets\", \"campaign_agents\", \"agents\", \"campaign_runs\"];\n const counts: Record = {};\n for (const table of WIPE) {\n const r = await sqlite.execute(`DELETE FROM ${table} WHERE tenant_id = ?`, [tid]).catch(() => null);\n counts[table] = Number((r as any)?.rowsAffected ?? 0) || 0;\n }\n // Also clear internal completion events (metric_history integration='internal') and task-done\n // calendar markers (event_type='completion'); keep real integration metric history intact.\n const mh = await sqlite.execute(\"DELETE FROM metric_history WHERE integration = 'internal' AND tenant_id = ?\", [tid]).catch(() => null);\n counts[\"metric_history(internal)\"] = Number((mh as any)?.rowsAffected ?? 0) || 0;\n const cm = await sqlite.execute(\"DELETE FROM calendar_entries WHERE event_type = 'completion' AND tenant_id = ?\", [tid]).catch(() => null);\n counts[\"calendar_entries(completion)\"] = Number((cm as any)?.rowsAffected ?? 0) || 0;\n return json({ ok: true, wiped: [...WIPE, \"metric_history(internal)\", \"calendar_entries(completion)\"], counts });\n }\n\n // --- Targeted wipe: action_queue (approvals / prepared+sent action history) ONLY ----\n if (pathname === \"/api/admin/wipe-actions\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n if (body.confirm !== \"WIPE\") return json({ ok: false, error: \"Send confirm: WIPE\" }, 400);\n const r = await sqlite.execute(\"DELETE FROM action_queue WHERE tenant_id = ?\", [tid]).catch(() => null);\n return json({ ok: true, wiped: [\"action_queue\"], counts: { action_queue: Number((r as any)?.rowsAffected ?? 0) || 0 } });\n }\n\n // --- Google OAuth redirect flow ----\n if (pathname === \"/api/auth/google\" && method === \"GET\") {\n const creds = await getCredentials(\"google\");\n // OAuth client is env-driven by design (GOOGLE_CLIENT_ID/SECRET); fall back to the DB row.\n const clientId = Deno.env.get(\"GOOGLE_CLIENT_ID\") || creds?.extra_config?.client_id || creds?.extra_config?.clientId;\n const redirectUri = creds?.extra_config?.redirect_uri || creds?.extra_config?.redirectUri || `${url.origin}/api/auth/google/callback`;\n if (!clientId) {\n return new Response(\"Google OAuth not configured. Add Client ID in Settings - Google.\", { status: 400 });\n }\n const scopes = [\n \"https://www.googleapis.com/auth/userinfo.email\",\n \"https://www.googleapis.com/auth/analytics.readonly\",\n \"https://www.googleapis.com/auth/webmasters.readonly\",\n \"https://www.googleapis.com/auth/gmail.modify\",\n \"https://www.googleapis.com/auth/drive.readonly\",\n \"https://www.googleapis.com/auth/spreadsheets.readonly\",\n \"https://www.googleapis.com/auth/documents.readonly\",\n \"https://www.googleapis.com/auth/calendar\",\n \"https://www.googleapis.com/auth/youtube.readonly\",\n \"https://www.googleapis.com/auth/yt-analytics.readonly\",\n ].join(\" \");\n const { state, setCookie } = newOAuthState();\n const params = new URLSearchParams({\n client_id: clientId,\n redirect_uri: redirectUri,\n response_type: \"code\",\n scope: scopes,\n access_type: \"offline\",\n prompt: \"consent\",\n state,\n });\n return redirectWithCookie(`https://accounts.google.com/o/oauth2/v2/auth?${params}`, setCookie);\n }\n\n if (pathname === \"/api/auth/google/callback\" && method === \"GET\") {\n const code = url.searchParams.get(\"code\");\n const error = url.searchParams.get(\"error\");\n if (error || !code) {\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(error || \"no_code\")}`);\n }\n if (!oauthStateValid(req, url)) {\n return Response.redirect(`${url.origin}/?auth_error=state_mismatch`);\n }\n const creds = await getCredentials(\"google\");\n // Trim env/DB values to avoid whitespace producing a malformed (invalid_request) token request.\n const clientId = (Deno.env.get(\"GOOGLE_CLIENT_ID\") || creds?.extra_config?.client_id || creds?.extra_config?.clientId || \"\").trim();\n const clientSecret = (Deno.env.get(\"GOOGLE_CLIENT_SECRET\") || creds?.api_secret || \"\").trim();\n const redirectUri = (creds?.extra_config?.redirect_uri || creds?.extra_config?.redirectUri || `${url.origin}/api/auth/google/callback`).trim();\n\n // Fail fast with a clear cause if a required param is empty (Google would return invalid_request).\n if (!clientId || !clientSecret) {\n const missing = !clientId && !clientSecret ? \"client_id_and_secret\"\n : !clientId ? \"client_id\" : \"client_secret\";\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(\"missing_\" + missing)}`);\n }\n\n const tokenRes = await fetch(\"https://oauth2.googleapis.com/token\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/x-www-form-urlencoded\" },\n body: new URLSearchParams({ code, client_id: clientId, client_secret: clientSecret, redirect_uri: redirectUri, grant_type: \"authorization_code\" }),\n });\n const tokens = await tokenRes.json() as any;\n if (!tokenRes.ok || !tokens.access_token) {\n const detail = [tokens.error, tokens.error_description].filter(Boolean).join(\": \") || \"token_exchange_failed\";\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(detail)}`);\n }\n\n const now = new Date().toISOString();\n const newExtraConfig = { ...(creds?.extra_config || {}), token_expiry: Date.now() + (tokens.expires_in || 3600) * 1000 };\n const existing = await sqlite.execute(\"SELECT id FROM integrations WHERE name = ? AND tenant_id = ?\", [\"google\", tid]);\n if (existing.rows.length > 0) {\n await sqlite.execute(\n \"UPDATE integrations SET access_token=?, refresh_token=?, extra_config=?, updated_at=? WHERE name='google' AND tenant_id = ?\",\n [tokens.access_token, tokens.refresh_token || creds?.refresh_token || null, JSON.stringify(newExtraConfig), now, tid]\n );\n } else {\n await sqlite.execute(\n \"INSERT INTO integrations (id, name, access_token, refresh_token, extra_config, connected_at, updated_at, tenant_id) VALUES (?,?,?,?,?,?,?,?)\",\n [crypto.randomUUID(), \"google\", tokens.access_token, tokens.refresh_token || null, JSON.stringify(newExtraConfig), now, now, tid]\n );\n }\n return redirectWithCookie(`${url.origin}/?auth=google_ok`, CLEAR_OAUTH_STATE);\n }\n\n // --- LinkedIn OAuth flow ----\n if (pathname === \"/api/auth/linkedin\" && method === \"GET\") {\n const creds = await getCredentials(\"linkedin\");\n const clientId = creds?.extra_config?.clientId || creds?.extra_config?.client_id;\n const redirectUri = `${url.origin}/api/auth/linkedin/callback`;\n if (!clientId) {\n return new Response(\"LinkedIn OAuth not configured. Add Client ID in Settings - LinkedIn.\", { status: 400 });\n }\n const { state, setCookie } = newOAuthState();\n const params = new URLSearchParams({\n response_type: \"code\",\n client_id: clientId,\n redirect_uri: redirectUri,\n scope: \"openid profile email w_member_social r_basicprofile\",\n state,\n });\n return redirectWithCookie(`https://www.linkedin.com/oauth/v2/authorization?${params}`, setCookie);\n }\n\n if (pathname === \"/api/auth/linkedin/callback\" && method === \"GET\") {\n const code = url.searchParams.get(\"code\");\n const error = url.searchParams.get(\"error\");\n if (error || !code) {\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(error || \"no_code\")}`);\n }\n if (!oauthStateValid(req, url)) {\n return Response.redirect(`${url.origin}/?auth_error=state_mismatch`);\n }\n const creds = await getCredentials(\"linkedin\");\n const clientId = creds?.extra_config?.clientId || creds?.extra_config?.client_id || \"\";\n const clientSecret = creds?.api_secret || \"\";\n const redirectUri = `${url.origin}/api/auth/linkedin/callback`;\n\n const tokenRes = await fetch(\"https://www.linkedin.com/oauth/v2/accessToken\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/x-www-form-urlencoded\" },\n body: new URLSearchParams({ grant_type: \"authorization_code\", code, redirect_uri: redirectUri, client_id: clientId, client_secret: clientSecret }),\n });\n const tokens = await tokenRes.json() as any;\n if (!tokenRes.ok || !tokens.access_token) {\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(tokens.error || \"linkedin_token_failed\")}`);\n }\n\n // Fetch LinkedIn profile to get the author URN needed for posts endpoint.\n let authorId = creds?.extra_config?.author_id || \"\";\n try {\n const profileRes = await fetch(\"https://api.linkedin.com/v2/me\", {\n headers: { Authorization: `Bearer ${tokens.access_token}` },\n });\n if (profileRes.ok) {\n const profile = await profileRes.json() as any;\n if (profile.id) authorId = `urn:li:person:${profile.id}`;\n }\n } catch { /* non-fatal - author_id can be set manually later */ }\n\n const now = new Date().toISOString();\n const newExtraConfig = { ...(creds?.extra_config || {}), token_expiry: Date.now() + (tokens.expires_in || 5184000) * 1000, author_id: authorId };\n const existing = await sqlite.execute(\"SELECT id FROM integrations WHERE name = ? AND tenant_id = ?\", [\"linkedin\", tid]);\n if (existing.rows.length > 0) {\n await sqlite.execute(\n \"UPDATE integrations SET access_token=?, extra_config=?, updated_at=? WHERE name='linkedin' AND tenant_id = ?\",\n [tokens.access_token, JSON.stringify(newExtraConfig), now, tid]\n );\n } else {\n await sqlite.execute(\n \"INSERT INTO integrations (id, name, access_token, extra_config, connected_at, updated_at, tenant_id) VALUES (?,?,?,?,?,?,?)\",\n [crypto.randomUUID(), \"linkedin\", tokens.access_token, JSON.stringify(newExtraConfig), now, now, tid]\n );\n }\n return redirectWithCookie(`${url.origin}/?auth=linkedin_ok`, CLEAR_OAUTH_STATE);\n }\n\n // Microsoft (Outlook) OAuth: env-first client (MICROSOFT_CLIENT_ID/SECRET), `common` tenant, DB fallback. Mirrors Google.\n if (pathname === \"/api/auth/microsoft\" && method === \"GET\") {\n const mcreds = await getCredentials(\"outlook\");\n const clientId = (Deno.env.get(\"MICROSOFT_CLIENT_ID\") || mcreds?.extra_config?.client_id || mcreds?.extra_config?.clientId || \"\").trim();\n if (!clientId) {\n return new Response(\"Microsoft OAuth not configured. Set MICROSOFT_CLIENT_ID (env) or add the Client ID in Settings → Outlook.\", { status: 400 });\n }\n const redirectUri = `${url.origin}/api/auth/microsoft/callback`;\n const scopes = MICROSOFT_SCOPES;\n const { state, setCookie } = newOAuthState();\n const params = new URLSearchParams({\n client_id: clientId,\n response_type: \"code\",\n redirect_uri: redirectUri,\n response_mode: \"query\",\n scope: scopes,\n state,\n });\n return redirectWithCookie(`https://login.microsoftonline.com/common/oauth2/v2.0/authorize?${params}`, setCookie);\n }\n\n if (pathname === \"/api/auth/microsoft/callback\" && method === \"GET\") {\n const code = url.searchParams.get(\"code\");\n const error = url.searchParams.get(\"error\");\n if (error || !code) {\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(error || \"no_code\")}`);\n }\n if (!oauthStateValid(req, url)) {\n return Response.redirect(`${url.origin}/?auth_error=state_mismatch`);\n }\n const creds = await getCredentials(\"outlook\");\n const clientId = (Deno.env.get(\"MICROSOFT_CLIENT_ID\") || creds?.extra_config?.client_id || creds?.extra_config?.clientId || \"\").trim();\n const clientSecret = (Deno.env.get(\"MICROSOFT_CLIENT_SECRET\") || creds?.api_secret || \"\").trim();\n const redirectUri = `${url.origin}/api/auth/microsoft/callback`;\n if (!clientId || !clientSecret) {\n const missing = !clientId && !clientSecret ? \"client_id_and_secret\" : !clientId ? \"client_id\" : \"client_secret\";\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(\"missing_microsoft_\" + missing)}`);\n }\n const scopes = MICROSOFT_SCOPES;\n const tokenRes = await fetch(\"https://login.microsoftonline.com/common/oauth2/v2.0/token\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/x-www-form-urlencoded\" },\n body: new URLSearchParams({ client_id: clientId, client_secret: clientSecret, code, redirect_uri: redirectUri, grant_type: \"authorization_code\", scope: scopes }),\n });\n const tokens = await tokenRes.json() as any;\n if (!tokenRes.ok || !tokens.access_token) {\n const detail = [tokens.error, tokens.error_description].filter(Boolean).join(\": \") || \"token_exchange_failed\";\n return Response.redirect(`${url.origin}/?auth_error=${encodeURIComponent(detail)}`);\n }\n\n const now = new Date().toISOString();\n const newExtraConfig = { ...(creds?.extra_config || {}), token_expiry: Date.now() + (tokens.expires_in || 3600) * 1000 };\n const existing = await sqlite.execute(\"SELECT id FROM integrations WHERE name = ? AND tenant_id = ?\", [\"outlook\", tid]);\n if (existing.rows.length > 0) {\n await sqlite.execute(\n \"UPDATE integrations SET access_token=?, refresh_token=?, extra_config=?, updated_at=? WHERE name='outlook' AND tenant_id = ?\",\n [tokens.access_token, tokens.refresh_token || creds?.refresh_token || null, JSON.stringify(newExtraConfig), now, tid]\n );\n } else {\n await sqlite.execute(\n \"INSERT INTO integrations (id, name, access_token, refresh_token, extra_config, connected_at, updated_at, tenant_id) VALUES (?,?,?,?,?,?,?,?)\",\n [crypto.randomUUID(), \"outlook\", tokens.access_token, tokens.refresh_token || null, JSON.stringify(newExtraConfig), now, now, tid]\n );\n }\n return redirectWithCookie(`${url.origin}/?auth=outlook_ok`, CLEAR_OAUTH_STATE);\n }\n\n // Identity login OAuth (sign-in, NOT data scopes): scope \"openid email profile\"; callback mints an app session.\n const loginMatch = pathname.match(/^\\/api\\/login\\/(google|microsoft)(\\/callback)?$/);\n if (loginMatch && method === \"GET\") {\n const provider = loginMatch[1] as \"google\" | \"microsoft\";\n return loginMatch[2]\n ? await identityLoginCallback(provider, req, url)\n : identityLoginStart(provider, url);\n }\n\n // --- Notes routes (TeamHub collaborative notes) ----\n if (pathname === \"/api/notes\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? \"marketing\";\n const result = await sqlite.execute(\n \"SELECT * FROM notes WHERE workspace=? AND tenant_id = ? ORDER BY pinned DESC, created_at DESC\",\n [workspace, tid]\n );\n return json(result.rows.map((r) => rowToObject(result.columns, r)));\n }\n if (pathname === \"/api/notes\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const id = crypto.randomUUID();\n const now = new Date().toISOString();\n await sqlite.execute(\n \"INSERT INTO notes (id, content, pinned, workspace, created_at, updated_at, tenant_id) VALUES (?,?,?,?,?,?,?)\",\n [id, body.content ?? \"\", body.pinned ? 1 : 0, body.workspace ?? \"marketing\", now, now, tid]\n );\n return json({ id, content: body.content ?? \"\", pinned: !!body.pinned, workspace: body.workspace ?? \"marketing\", created_at: now, updated_at: now }, 201);\n }\n const noteMatch = pathname.match(/^\\/api\\/notes\\/([^/]+)$/);\n if (noteMatch && method === \"PUT\") {\n const id = noteMatch[1];\n const body = await req.json().catch(() => ({}));\n const now = new Date().toISOString();\n const setParts: string[] = [\"updated_at=?\"];\n const setArgs: any[] = [now];\n if (body.pinned !== undefined) { setParts.push(\"pinned=?\"); setArgs.push(body.pinned ? 1 : 0); }\n if (body.content !== undefined) { setParts.push(\"content=?\"); setArgs.push(body.content); }\n await sqlite.execute(\n `UPDATE notes SET ${setParts.join(\", \")} WHERE id=? AND tenant_id = ?`,\n [...setArgs, id, tid]\n );\n const r = await sqlite.execute(\"SELECT * FROM notes WHERE id=? AND tenant_id = ?\", [id, tid]);\n if (!r.rows.length) return json({ error: \"Not found\" }, 404);\n return json(rowToObject(r.columns, r.rows[0]));\n }\n if (noteMatch && method === \"DELETE\") {\n await sqlite.execute(\"DELETE FROM notes WHERE id=? AND tenant_id = ?\", [noteMatch[1], tid]);\n return json({ success: true });\n }\n\n // --- World dashboard rollups (logs / kanban / project panels) ----\n // /api/world/activity — cross-agent runs; ?campaign[_id]=ID scopes to that campaign, bare ?campaign = legacy filter.\n if (pathname === \"/api/world/activity\" && method === \"GET\") {\n const limit = parseInt(url.searchParams.get(\"limit\") ?? \"40\", 10);\n const status = url.searchParams.get(\"status\") ?? undefined;\n const agent_id = url.searchParams.get(\"agent_id\") ?? undefined;\n const campaignParam = url.searchParams.get(\"campaign_id\") ?? url.searchParams.get(\"campaign\");\n const campaign_id = campaignParam ? campaignParam : undefined;\n const campaign = url.searchParams.get(\"campaign\") != null && !campaign_id;\n return json(await getWorldActivity({ limit, status, agent_id, campaign, campaign_id }));\n }\n // /api/world/board — tasks grouped into the 4 kanban columns; ?campaign[_id]=ID scopes them.\n if (pathname === \"/api/world/board\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const campaign_id = url.searchParams.get(\"campaign_id\") ?? url.searchParams.get(\"campaign\") ?? undefined;\n return json(await getWorldBoard({ workspace, campaign_id: campaign_id || undefined }));\n }\n // /api/world/project — active campaign brief + real run-usage rollup; ?campaign[_id]=ID focuses it.\n if (pathname === \"/api/world/project\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n const campaign_id = url.searchParams.get(\"campaign_id\") ?? url.searchParams.get(\"campaign\") ?? undefined;\n return json(await getWorldProject({ workspace, campaign_id: campaign_id || undefined }));\n }\n\n // --- Campaigns (Director: multi-agent orchestrated pipelines) ----\n if (pathname === \"/api/campaigns\" && method === \"GET\") {\n const workspace = url.searchParams.get(\"workspace\") ?? undefined;\n return json(await listCampaigns(workspace));\n }\n if (pathname === \"/api/campaigns\" && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await createCampaign(body), 201);\n }\n // Sub-resources (:id/runs, :id/agents, :id/trigger) before the bare :id matcher.\n const campaignRunsMatch = pathname.match(/^\\/api\\/campaigns\\/([^/]+)\\/runs$/);\n if (campaignRunsMatch && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n // Runs the Director pipeline synchronously and returns the run result.\n return json(await orchestrateCampaignRun(campaignRunsMatch[1], body.input));\n }\n if (campaignRunsMatch && method === \"GET\") {\n return json(await listCampaignRuns(campaignRunsMatch[1]));\n }\n const campaignAgentsMatch = pathname.match(/^\\/api\\/campaigns\\/([^/]+)\\/agents$/);\n if (campaignAgentsMatch && method === \"GET\") {\n return json(await getCampaignAgents(campaignAgentsMatch[1]));\n }\n // Clicking a campaign returns its tasks (OKR→Campaign→Task link).\n const campaignTasksMatch = pathname.match(/^\\/api\\/campaigns\\/([^/]+)\\/tasks$/);\n if (campaignTasksMatch && method === \"GET\") {\n return json(await listTasks({ campaign_id: campaignTasksMatch[1] }));\n }\n const campaignTriggerMatch = pathname.match(/^\\/api\\/campaigns\\/([^/]+)\\/trigger$/);\n if (campaignTriggerMatch && method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n return json(await setCampaignTrigger(campaignTriggerMatch[1], body));\n }\n const campaignIdMatch = pathname.match(/^\\/api\\/campaigns\\/([^/]+)$/);\n if (campaignIdMatch && method === \"GET\") {\n const c = await getCampaign(campaignIdMatch[1]);\n return c ? json(c) : json({ error: \"Not found\" }, 404);\n }\n if (campaignIdMatch && method === \"PUT\") {\n const body = await req.json().catch(() => ({}));\n return json(await updateCampaign(campaignIdMatch[1], body));\n }\n if (campaignIdMatch && method === \"DELETE\") {\n const ok = await deleteCampaign(campaignIdMatch[1]);\n return ok ? json({ success: true }) : json({ error: \"Not found\" }, 404);\n }\n\n // No route matched\n return json({ error: \"Not found\", path: pathname }, 404);\n } catch (err) {\n const message = err instanceof Error ? err.message : String(err);\n console.error(`[router] Error handling ${method} ${pathname}:`, message);\n return json({ error: \"Internal server error\", detail: message }, 500);\n }\n}\n\n/**\n * Converts a sqlite result row (array of values) to a plain object\n * using the columns array from the result set.\n */\nfunction rowToObject(columns: string[], row: unknown[]): Record {\n const obj: Record = {};\n for (let i = 0; i < columns.length; i++) {\n obj[columns[i]] = row[i];\n }\n return obj;\n}\n",[200],"731c8246-5f37-11f1-acf9-1607ee4eb77e","main.ts",1066,["D",1780482718390],23,"http","import { initDB, tenantStore, SYSTEM_CTX } from \"./db.ts\";\nimport { handleRequest } from \"./router.ts\";\nimport { getFrontendHTML, getWorldHTML } from \"./frontend.ts\";\nimport { seedKnowledge } from \"./api/knowledge_seed.ts\";\nimport { resolveSession } from \"./api/session.ts\";\n\n// Foundation tenant context: a session resolves to a TenantCtx; absent one we fall back to the\n// system/default SYSTEM_CTX (imported from db.ts, typed as TenantCtx) so all existing\n// (t_default-backfilled) data keeps working exactly as today.\n\nlet initialized = false;\n\n// Shared-secret gate. Set APP_TOKEN in the val's environment. Every /api/* route\n// requires `Authorization: Bearer ` (or `?token=` for download links),\n// EXCEPT the public paths below. Fail-closed: if APP_TOKEN is unset, data routes 503.\nconst APP_TOKEN = (Deno.env.get(\"APP_TOKEN\") || \"\").trim();\n\n// Auth ENABLED (Phase 3): /api/* requires a valid nexus_session cookie OR the APP_TOKEN bearer\n// (recovery hatch). Public paths (app modules, /api/auth/*, /api/login/*, /api/health) stay open so\n// the login screen always loads. Verified: owner session authorizes, anonymous → 401 → login screen.\nconst AUTH_DISABLED = false;\n\n// Public /api paths that must stay reachable without the app token:\n// - /api/app-* : the frontend's own JS modules (static code, no secrets; loaded by \n \n \n \n \n \n \n \n \n\n\n

\n \n\n`;\n}\n\n// Part 10: the Agent World shell — a \"the-delegation\" simulation DASHBOARD at /world. Same origin\n// (shares nexus_token + every /api/* route), so the agents + panels run over the REAL backend.\n// This shell is the SOLE creator of every fixed DOM id the world modules target. It is a CSS-grid\n// dashboard (light theme): a top bar, a LEFT logs panel, a CENTER stage (scene header + a BOUNDED\n// canvas + a kanban under it) and a RIGHT project panel. The canvas is NOT fullscreen — it lives\n// inside #world-canvas-wrap and the scene sizes to that wrapper. three.js (+ addons) load from a\n// CDN via the importmap; the 3D + panel logic is in /api/app-world (the entry) and its sub-modules.\nexport function getWorldHTML(): string {\n return `\n\n\n \n \n Nexus AI · Agent World\n \n \n \n \n\n\n

\n \n

\n \n\n`;\n}\n",[491],"731798f6-5f2f-11f1-ae87-1607ee4eb77e","db.ts","2f68e1b8-56ca-11f1-b821-ee650bb23af1",1048,["D",1780479282410],55,"import { sqlite as _sqlite } from \"https://esm.town/v/std/sqlite\";\n\n// ── DB BACKEND (portable across hosts) ───────────────────────────────────────\n// AIHive runs on val.town today (std/sqlite) but is migrating to Firebase Hosting + Cloud Run, where\n// the database is a real Turso libSQL (no shared rate limit, runs on any host). This selects the\n// backend from ENV without changing a single caller: when TURSO_DATABASE_URL is set we use the Turso\n// libSQL client; otherwise we fall back to val.town's std/sqlite. Both expose the SAME\n// execute(string | {sql, args}) → {rows, columns, rowsAffected} shape, so the rest of the app and the\n// rate-limit retry wrapper below are unchanged. On val.town (no TURSO_* env) this is a no-op: the\n// libSQL client is never even imported.\nfunction _env(k: string): string | undefined {\n try {\n const g = globalThis as any;\n return g.Deno?.env?.get?.(k) ?? g.process?.env?.[k];\n } catch {\n return undefined;\n }\n}\nconst _tursoUrl = _env(\"TURSO_DATABASE_URL\");\nlet _exec: (stmt: any) => Promise;\nif (_tursoUrl) {\n // Lazy import so std-only (val.town) deploys never load the libSQL client.\n const { createClient } = await import(\"https://esm.sh/@libsql/client@0.14.0/web\");\n const _client = createClient({ url: _tursoUrl, authToken: _env(\"TURSO_AUTH_TOKEN\") });\n _exec = (stmt: any) => _client.execute(stmt);\n} else {\n // Compatibility shim: std/sqlite accepts execute(string) or execute({sql, args}).\n const _bound = _sqlite.execute.bind(_sqlite);\n _exec = (stmt: any) => _bound(stmt);\n}\n\n// RATE-LIMIT RESILIENCE: std/sqlite enforces a shared rate limit. A cold-boot initDB fires ~170\n// CREATE/ALTER/INDEX calls in a burst and can exceed the window MID-init — which used to wedge the\n// whole app in a permanent 500 loop (init fails part-way → memo resets → next request re-bursts →\n// fails again, never converging). We retry rate-limited calls with backoff so init (and any runtime\n// query) PACES THROUGH the limit and completes. Non-rate-limit errors are rethrown immediately.\nconst _isRateLimit = (e: any): boolean => /rate limit|too many request|429/i.test(String((e && e.message) || e));\nconst _backoffMs = (e: any, attempt: number): number => {\n const m = String((e && e.message) || e).match(/retry in (\\d+)\\s*second/i);\n if (m) return Math.min(8000, (Number(m[1]) + 1) * 1000); // honor the server's hint when given\n return Math.min(4000, 500 * Math.pow(2, attempt));\n};\nconst _execRetry = async (stmt: any): Promise => {\n for (let attempt = 0; ; attempt++) {\n try {\n return await _exec(stmt);\n } catch (e) {\n if (attempt < 10 && _isRateLimit(e)) {\n await new Promise((r) => setTimeout(r, _backoffMs(e, attempt)));\n continue;\n }\n throw e;\n }\n }\n};\nexport const sqlite = {\n ..._sqlite,\n execute: async (stmt: string | { sql: string; args?: any[] }, args?: any[]): Promise => {\n if (typeof stmt === \"string\" && args !== undefined) {\n return _execRetry({ sql: stmt, args });\n }\n return _execRetry(stmt as any);\n },\n};\n\n// ─── TENANT CONTEXT (AUTH/TENANT FOUNDATION) ────────────────────────────────\n// Per-request tenant/user/role context carried via AsyncLocalStorage. main.ts wraps each\n// handleRequest in tenantStore.run(ctx, ...). When there is no active store (cron, system\n// boot, recovery APP_TOKEN path) we DEFAULT to t_default/system/owner so all existing data\n// (backfilled to tenant_id=\"t_default\") keeps working exactly as today.\nimport { AsyncLocalStorage } from \"node:async_hooks\";\n\n// impersonating / realTenantId are set ONLY for read-only \"view as tenant\" (Phase D): when a\n// platform admin toggles sessions.impersonate_tenant_id, resolveSessionToken redirects tenantId to\n// the target tenant while keeping userId/email/role/isPlatformAdmin = the master. realTenantId holds\n// the master's OWN membership tenant. Optional so every existing ctx literal stays valid.\nexport type TenantCtx = { tenantId: string; userId: string; email: string; role: \"owner\" | \"admin\" | \"member\"; isPlatformAdmin: boolean; impersonating?: boolean; realTenantId?: string };\n// SYSTEM_CTX is the platform-trusted recovery context (APP_TOKEN / cron / boot). It IS platform admin.\nexport const SYSTEM_CTX: TenantCtx = { tenantId: \"t_default\", userId: \"system\", email: \"system\", role: \"owner\", isPlatformAdmin: true };\n\nexport const tenantStore = new AsyncLocalStorage();\nexport function currentCtx(): TenantCtx {\n // The bare no-store default is intentionally NOT platform admin (safe-by-default). Only the\n // explicit SYSTEM_CTX (used by main.ts for cron/recovery) carries isPlatformAdmin:true.\n return tenantStore.getStore() || { tenantId: \"t_default\", userId: \"system\", email: \"system\", role: \"owner\", isPlatformAdmin: false };\n}\nexport function currentTenantId(): string { return currentCtx().tenantId; }\nexport function currentUserId(): string { return currentCtx().userId; }\nexport function currentRole(): \"owner\" | \"admin\" | \"member\" { return currentCtx().role; }\n\n// MEMOIZED: run the full schema setup ONCE per warm instance, not on every request. initDB was\n// awaited per-request, firing dozens of CREATE/ALTER sqlite calls each time → std/sqlite 429\n// \"Too Many Requests\" → intermittent 500s across the app (the \"too many requests\" symptom). The\n// cached promise makes every subsequent call free; on failure we reset so the next request retries.\nlet _initPromise: Promise | null = null;\nexport function initDB(): Promise {\n if (!_initPromise) {\n _initPromise = _initDBImpl().catch((e) => { _initPromise = null; throw e; });\n }\n return _initPromise;\n}\nasync function _initDBImpl(): Promise {\n // Schema lives in ./schema.ts to keep db.ts under val.town's 80KB/file cap. Lazy import\n // avoids a static cycle (schema.ts imports sqlite from db.ts).\n const { runSchema } = await import(\"./schema.ts\");\n await runSchema();\n}\n\n// The starter \"demo\" agents seeded on first boot. Exported so the admin \"Remove demo data\"\n// route can identify + remove untouched seed rows (matched by name+description+system_prompt,\n// so a user-renamed/edited agent is never deleted).\nexport const DEFAULT_AGENT_SEEDS = [\n {\n name: \"SEO Analyst\",\n workspace: \"marketing\",\n description: \"Analyzes SEO performance using Ahrefs and Google Search Console data\",\n system_prompt: \"You are an expert SEO analyst. Analyze search performance data, identify opportunities, and provide actionable recommendations to improve organic search rankings.\",\n tools: JSON.stringify([\"ahrefs\", \"gsc\", \"ga4\"]),\n model: \"claude-sonnet-4-6\",\n },\n {\n name: \"Content Creator\",\n workspace: \"marketing\",\n description: \"Creates and manages content for WordPress, YouTube, and social media\",\n system_prompt: \"You are a creative content strategist. Help plan, write, and optimize content for blogs, videos, and social media platforms to drive engagement and conversions.\",\n tools: JSON.stringify([\"wordpress\", \"youtube\", \"ga4\"]),\n model: \"claude-sonnet-4-6\",\n },\n {\n name: \"Analytics Reporter\",\n workspace: \"marketing\",\n description: \"Generates reports from GA4 and Search Console data\",\n system_prompt: \"You are a data analyst specializing in digital marketing analytics. Create insightful reports from GA4 and Search Console data to track KPIs and identify growth opportunities.\",\n tools: JSON.stringify([\"ga4\", \"gsc\", \"ahrefs\"]),\n model: \"claude-sonnet-4-6\",\n },\n {\n name: \"LinkedIn Outreach\",\n workspace: \"sales\",\n description: \"Manages LinkedIn prospecting and connection campaigns\",\n system_prompt: \"You are a sales development expert specializing in LinkedIn outreach. Help craft personalized connection requests, follow-up messages, and engagement strategies to build pipeline.\",\n tools: JSON.stringify([\"linkedin\", \"outlook\"]),\n model: \"claude-sonnet-4-6\",\n },\n {\n name: \"Lead Researcher\",\n workspace: \"sales\",\n description: \"Researches prospects using LinkedIn and web data\",\n system_prompt: \"You are a research specialist who finds and qualifies sales leads. Analyze LinkedIn profiles, company data, and online presence to identify ideal prospects and gather intelligence for sales outreach.\",\n tools: JSON.stringify([\"linkedin\"]),\n model: \"claude-sonnet-4-6\",\n },\n {\n name: \"Email Composer\",\n workspace: \"sales\",\n description: \"Drafts and sends personalized sales emails via Outlook\",\n system_prompt: \"You are a sales email expert who crafts compelling, personalized outreach emails. Write emails that are concise, value-focused, and drive response rates while maintaining brand voice.\",\n tools: JSON.stringify([\"outlook\", \"linkedin\"]),\n model: \"claude-sonnet-4-6\",\n },\n];\n\n\n",[499],"cf768a30-5e7b-11f1-a326-1607ee4eb77e","cron_entry.ts","fb10767e-5b1a-11f1-b807-ee650bb23af1",925,["D",1780402127969],"// Scheduled entrypoint — deploy this file as a Val Town INTERVAL/CRON trigger (separate from\n// the HTTP val in main.ts). A single file can't be both the HTTP handler AND the scheduled\n// trigger, which is why the execution engine never ran on its own before: cron.ts had a default\n// export but nothing scheduled it.\n//\n// What it does each tick:\n// 1) processPendingRuns() — runs the LLM tool-loop for any agent_runs with status='pending'\n// (external sends still go to the human-approval action_queue; nothing auto-sends).\n// 2) syncObjectivesFromFunnel() — refreshes + persists KR/objective progress from live funnel\n// data (the GET /api/okrs list no longer writes on read, so this keeps stored progress fresh).\n//\n// Schedule: set the interval in the Val Town dashboard (Triggers → e.g. every 5 minutes).\nimport { initDB } from \"./db.ts\";\nimport { processPendingRuns } from \"./cron.ts\";\nimport { syncObjectivesFromFunnel } from \"./api/okrs.ts\";\nimport { processCampaignTriggers } from \"./api/campaigns.ts\";\n\n// Crash-proof scheduled tick. A single failure (DB 429, a bad run, a flaky integration)\n// must be logged and swallowed so the default export NEVER throws to the Val Town runtime\n// (an uncaught throw kills the tick silently and can disable the trigger). initDB() and each\n// unit of work are guarded INDEPENDENTLY so one failing does not skip the others.\nexport default async function (): Promise {\n // 1) Ensure the DB is ready. If this fails (e.g. DB 429), we cannot safely run the\n // tenant-scoped work below, so log and return — but never throw.\n try {\n await initDB();\n } catch (err) {\n console.error(\"[cron] initDB failed; skipping this tick:\", err);\n return;\n }\n\n // 2) Process pending agent runs (external sends still go to the human-approval queue).\n let runs = { processed: 0, failed: 0 };\n try {\n runs = await processPendingRuns(5);\n } catch (err) {\n console.error(\"[cron] processPendingRuns failed:\", err);\n }\n\n // 3) Fire scheduled/looping campaigns on the cron tick (after agent runs are processed).\n try {\n await processCampaignTriggers(2);\n } catch (err) {\n console.error(\"[cron] processCampaignTriggers failed:\", err);\n }\n\n // 4) Refresh + persist KR/objective progress from live funnel data.\n let okr = { objectives: 0, key_results: 0 };\n try {\n okr = await syncObjectivesFromFunnel();\n } catch (err) {\n console.error(\"[cron] syncObjectivesFromFunnel failed:\", err);\n }\n\n console.log(\n `[cron] runs processed=${runs.processed} failed=${runs.failed}; ` +\n `okr synced objectives=${okr.objectives} key_results=${okr.key_results}`,\n );\n}\n",[507],"cfec26aa-5e7b-11f1-a9e2-1607ee4eb77e","cron.ts","50e655da-5a69-11f1-ab8c-ee650bb23af1",926,["D",1780402128741],"// Execution engine — the missing piece that makes agents actually RUN.\n// Deploy as a val.town interval val (default export runs on a schedule, e.g. every 1–5 min),\n// or trigger manually via POST /api/agents/run-pending (wired in router.ts).\n//\n// It polls agent_runs for status='pending', loads the agent config, and runs an LLM\n// tool-loop reusing chat.ts's buildTools()/executeTool(). External sends still go to the\n// human-approval action_queue (the tools enqueue, they don't send) — so the engine is safe.\nimport { sqlite, tenantStore } from \"./db.ts\";\nimport { getLLMConfig, callLLM } from \"./api/llm.ts\";\nimport { getAgent } from \"./api/agents.ts\";\nimport { buildTools, executeTool } from \"./api/chat.ts\";\nimport { getSkillsIndex } from \"./api/skills.ts\";\nimport { captureMemoryFromConversation } from \"./api/memory.ts\";\nimport { getCompanyContextString } from \"./api/company.ts\";\n\nconst ENGINE_SYSTEM_SUFFIX =\n \"\\n\\nYou can ACT using tools. External sends (email / LinkedIn / WordPress) are QUEUED for human \" +\n \"approval — prepare them and they will appear in Approvals; they will NOT send until a human approves. \" +\n \"Create tasks and schedule calendar events directly. Take the single next-best action toward the goal.\";\n\nexport async function processPendingRuns(limit = 3): Promise<{ processed: number; failed: number }> {\n let processed = 0;\n let failed = 0;\n let rows: any[] = [];\n // Fetch the marketing skills index once per invocation; append to the agent system prompt\n // so the autonomous agent can apply_skill on a match. Empty index = no-op.\n const skillsIdx = await getSkillsIndex({ maxChars: 5000 }).catch(() => \"\");\n const skillsSuffix = skillsIdx\n ? \"\\n\\n\" + skillsIdx + \"\\n\\nWhen a request matches a skill above, call apply_skill with that skill name before acting.\"\n : \"\";\n // Unified business context: prepended to every autonomous agent's system prompt so each run\n // knows the company. Cheap single app_config read; empty string on any error = no-op.\n const companyCtx = await getCompanyContextString(2000).catch(() => \"\");\n try {\n const r = await sqlite.execute(\n \"SELECT id, agent_id, input, tenant_id FROM agent_runs WHERE status = 'pending' ORDER BY started_at ASC LIMIT ?\",\n [limit],\n );\n rows = (r.rows || []) as any[];\n } catch {\n return { processed, failed };\n }\n\n for (const row of rows) {\n const runId = String(row[0]);\n const agentId = String(row[1]);\n const input = row[2] ? String(row[2]) : \"\";\n // MULTI-TENANT CORRECTNESS: cron runs with NO active tenant store, so currentTenantId()\n // would default to t_default and every tenant's pending runs would be processed (and their\n // tool calls / data reads/writes scoped) under t_default. Read THIS row's tenant_id and run\n // the whole processing body inside tenantStore.run so the agent executes in ITS OWN tenant\n // scope. Backfilled/legacy rows with NULL tenant_id fall back to t_default (current behavior).\n const tenantId = row[3] ? String(row[3]) : \"t_default\";\n const ctx = { tenantId, userId: \"system\", email: \"system\", role: \"owner\" as const };\n // Per-run result so the outer counters update after the scoped body returns.\n const r = await tenantStore.run(ctx, async (): Promise<{ ok: boolean }> => {\n // Wrapped so one run's UPDATE failure can't abort the drain.\n try {\n await sqlite.execute(\"UPDATE agent_runs SET status='running' WHERE id=?\", [runId]);\n } catch { /* status flip best-effort; continue processing */ }\n // Part 14.2: structured execution trace (TraceStep[]) — hoisted so a partial trace can be\n // persisted in the catch block on failure. Reset per run.\n const trace: any[] = [];\n try {\n const agent = await getAgent(agentId);\n const llmConfig = await getLLMConfig();\n if (!(llmConfig as any).api_key) throw new Error(\"No LLM API key configured\");\n\n const systemPrompt =\n (companyCtx ? companyCtx + \"\\n\\n---\\n\\n\" : \"\") +\n (agent?.system_prompt || \"You are an autonomous marketing & sales agent.\") + ENGINE_SYSTEM_SUFFIX + skillsSuffix;\n const messages: any[] = [{\n role: \"user\",\n content: input || `Run agent \"${agent?.name || agentId}\". Decide and prepare the next best action toward its goal.`,\n }];\n\n const tools = buildTools();\n let llm = await callLLM(llmConfig, messages, { systemPrompt, tools, maxTokens: 2048 });\n const actionLog: string[] = [];\n let rounds = 0;\n while (llm.tool_calls && llm.tool_calls.length > 0 && rounds < 4) {\n rounds++;\n trace.push({ t: \"round\", n: rounds, ts: new Date().toISOString() });\n messages.push({ role: \"assistant\", content: llm.content || \"\", tool_calls: llm.tool_calls });\n for (const tc of llm.tool_calls) {\n let args: Record = {};\n try {\n args = typeof tc.function.arguments === \"string\"\n ? JSON.parse(tc.function.arguments)\n : (tc.function.arguments || {});\n } catch { args = {}; }\n const toolStart = performance.now();\n const res = await executeTool(tc.function.name, args, llmConfig);\n const ms = Math.round(performance.now() - toolStart);\n actionLog.push(`${tc.function.name}: ${res}`);\n trace.push({\n t: \"tool\",\n name: tc.function.name,\n args_preview: JSON.stringify(args).slice(0, 300),\n result_preview: String(res).slice(0, 300),\n ok: true,\n ms,\n ts: new Date().toISOString(),\n });\n messages.push({ role: \"tool\", tool_call_id: tc.id, content: res });\n }\n llm = await callLLM(llmConfig, messages, { systemPrompt, tools, maxTokens: 2048 });\n }\n\n const output = ((llm.content || \"\") + (actionLog.length ? \"\\n\\n[actions]\\n\" + actionLog.join(\"\\n\") : \"\")).slice(0, 8000);\n trace.push({ t: \"message\", role: \"assistant\", preview: String(output).slice(0, 300), ts: new Date().toISOString() });\n // Per-run status UPDATE in its own try/catch so one failure does not abort the drain.\n try {\n await sqlite.execute(\n \"UPDATE agent_runs SET status='completed', finished_at=datetime('now'), output=?, trace=? WHERE id=?\",\n [output, JSON.stringify(trace), runId],\n );\n } catch { /* completion write best-effort; run already executed */ }\n // Fire-and-forget: distill durable business facts from this completed run into memory.\n captureMemoryFromConversation([\n { role: \"user\", content: messages[0]?.content || input },\n { role: \"assistant\", content: llm.content || output },\n ]).catch(() => {});\n return { ok: true };\n } catch (e: any) {\n // Part 14.2: persist the partial trace captured before the failure.\n await sqlite.execute(\n \"UPDATE agent_runs SET status='failed', finished_at=datetime('now'), error=?, trace=? WHERE id=?\",\n [String(e?.message || e), JSON.stringify(trace), runId],\n ).catch(() => {});\n return { ok: false };\n }\n });\n if (r.ok) processed++; else failed++;\n }\n return { processed, failed };\n}\n\n// val.town interval-val entrypoint.\nexport default async function (): Promise {\n const result = await processPendingRuns(3);\n console.log(`[cron] agent runs processed=${result.processed} failed=${result.failed}`);\n}\n",[514],"7197bde6-5f37-11f1-960f-1607ee4eb77e","app_modules.ts","09d7b534-5f15-11f1-9609-1607ee4eb77e",1064,["D",1780482715858],"// Frontend ES-module route table — extracted from router.ts to keep router under val.town's\n// 80000-byte/file hard cap. Each /api/app-* path maps to a get*JS() that returns the browser\n// ES-module source string; the SPA shell lazy-imports these. Add new frontend modules HERE\n// (one import + one map entry) so router.ts never grows. serveAppModule() returns the module\n// source for a known path, or null when the path is not a frontend module route.\nimport { getAppJS } from \"./frontend_js.ts\";\nimport { getUIJS } from \"./frontend_ui.ts\";\nimport { getChartsJS } from \"./frontend_charts.ts\";\nimport { getRenderersJS } from \"./frontend_renderers.ts\";\nimport { getRenderers2JS } from \"./frontend_renderers2.ts\";\nimport { getCRMJS } from \"./frontend_crm.ts\";\nimport { getCRM2JS } from \"./frontend_crm2.ts\";\nimport { getMarketing2JS } from \"./frontend_marketing2.ts\";\nimport { getMarketing3JS } from \"./frontend_marketing3.ts\";\nimport { getMarketing4JS } from \"./frontend_marketing4.ts\";\nimport { getMarketing5JS } from \"./frontend_marketing5.ts\";\nimport { getMarketing6JS } from \"./frontend_marketing6.ts\";\nimport { getMarketingWorkspaceJS } from \"./frontend_marketing.ts\";\nimport { getPagesJS } from \"./frontend_pages.ts\";\nimport { getCommandJS } from \"./frontend_command.ts\";\nimport { getIntelligenceJS } from \"./frontend_intelligence.ts\";\nimport { getSettingsJS } from \"./frontend_settings.ts\";\nimport { getWidgetsJS } from \"./frontend_widgets.ts\";\nimport { getObjectivesJS } from \"./frontend_objectives.ts\";\nimport { getCalendarJS } from \"./frontend_calendar.ts\";\nimport { getExperimentsJS } from \"./frontend_experiments.ts\";\nimport { getSkillsJS } from \"./frontend_skills.ts\";\nimport { getWorldJS } from \"./frontend_world.ts\";\nimport { getWorldSceneJS } from \"./frontend_world_scene.ts\";\nimport { getWorldOfficeJS } from \"./frontend_world_office.ts\";\nimport { getWorldAgentsJS } from \"./frontend_world_agents.ts\";\nimport { getWorldUIJS } from \"./frontend_world_ui.ts\";\nimport { getWorldDashJS } from \"./frontend_world_dash.ts\";\nimport { getCampaignsJS } from \"./frontend_campaigns.ts\";\nimport { getTasksJS } from \"./frontend_tasks.ts\";\nimport { getKnowledgeGraphJS } from \"./frontend_knowledge_graph.ts\";\nimport { getApprovalsJS } from \"./frontend_approvals.ts\";\nimport { getPlatformJS } from \"./frontend_platform.ts\";\nimport { getSupportJS } from \"./frontend_support.ts\";\n\nconst MODULE_ROUTES: Record string> = {\n \"/api/app-js\": getAppJS,\n \"/api/app-ui\": getUIJS,\n \"/api/app-charts\": getChartsJS,\n \"/api/app-renderers\": getRenderersJS,\n \"/api/app-renderers2\": getRenderers2JS,\n \"/api/app-pages\": getPagesJS,\n \"/api/app-command\": getCommandJS,\n \"/api/app-intelligence\": getIntelligenceJS,\n \"/api/app-settings\": getSettingsJS,\n \"/api/app-widgets\": getWidgetsJS,\n \"/api/app-objectives\": getObjectivesJS,\n \"/api/app-calendar\": getCalendarJS,\n \"/api/app-experiments\": getExperimentsJS,\n \"/api/app-campaigns\": getCampaignsJS,\n \"/api/app-tasks\": getTasksJS,\n \"/api/app-knowledge-graph\": getKnowledgeGraphJS,\n \"/api/app-approvals\": getApprovalsJS,\n \"/api/app-crm\": getCRMJS,\n \"/api/app-crm2\": getCRM2JS,\n \"/api/app-marketing2\": getMarketing2JS,\n \"/api/app-marketing3\": getMarketing3JS,\n \"/api/app-marketing4\": getMarketing4JS,\n \"/api/app-marketing5\": getMarketing5JS,\n \"/api/app-marketing6\": getMarketing6JS,\n \"/api/app-marketing\": getMarketingWorkspaceJS,\n \"/api/app-skills\": getSkillsJS,\n \"/api/app-world\": getWorldJS,\n \"/api/app-world-scene\": getWorldSceneJS,\n \"/api/app-world-office\": getWorldOfficeJS,\n \"/api/app-world-agents\": getWorldAgentsJS,\n \"/api/app-world-ui\": getWorldUIJS,\n \"/api/app-world-dash\": getWorldDashJS,\n \"/api/app-platform\": getPlatformJS,\n \"/api/app-support\": getSupportJS,\n};\n\n// Return the browser module source for a frontend route, or null if pathname is not one.\nexport function serveAppModule(pathname: string): string | null {\n const fn = MODULE_ROUTES[pathname];\n return fn ? fn() : 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State (enabled/pinned/custom) lives in the\n// 'skills' DB table; this module is the immutable builtin content.\nimport { SKILLS_PART_1 } from \"./catalog/part_1.ts\";\nimport { SKILLS_PART_2 } from \"./catalog/part_2.ts\";\nimport { SKILLS_PART_3 } from \"./catalog/part_3.ts\";\nimport { SKILLS_PART_4 } from \"./catalog/part_4.ts\";\nimport { SKILLS_PART_5 } from \"./catalog/part_5.ts\";\nimport { SKILLS_PART_6 } from \"./catalog/part_6.ts\";\nimport { SKILLS_PART_7 } from \"./catalog/part_7.ts\";\nimport { SKILLS_PART_8 } from \"./catalog/part_8.ts\";\nimport { SKILLS_PART_9 } from \"./catalog/part_9.ts\";\nimport { REFS_PART_1 } from \"./refs/ref_1.ts\";\nimport { REFS_PART_2 } from \"./refs/ref_2.ts\";\nimport { REFS_PART_3 } from \"./refs/ref_3.ts\";\nimport { REFS_PART_4 } from \"./refs/ref_4.ts\";\nimport { REFS_PART_5 } from \"./refs/ref_5.ts\";\nimport { REFS_PART_6 } from \"./refs/ref_6.ts\";\nimport { REFS_PART_7 } from \"./refs/ref_7.ts\";\nimport { REFS_PART_8 } from \"./refs/ref_8.ts\";\nimport { REFS_PART_9 } from \"./refs/ref_9.ts\";\nimport { REFS_PART_10 } from \"./refs/ref_10.ts\";\nimport { REFS_PART_11 } from \"./refs/ref_11.ts\";\nimport { REFS_PART_12 } from \"./refs/ref_12.ts\";\nimport { REFS_PART_13 } from \"./refs/ref_13.ts\";\nimport { REFS_PART_14 } from \"./refs/ref_14.ts\";\nimport { REFS_PART_15 } from \"./refs/ref_15.ts\";\nimport { REFS_PART_16 } from \"./refs/ref_16.ts\";\n\nexport interface SkillDef { name: string; category: string; description: string; version: string; body: string }\nexport interface SkillRef { skill: string; ref: string; body: string }\n\nexport const BUILTIN_SKILLS: SkillDef[] = [...SKILLS_PART_1, ...SKILLS_PART_2, ...SKILLS_PART_3, ...SKILLS_PART_4, ...SKILLS_PART_5, ...SKILLS_PART_6, ...SKILLS_PART_7, ...SKILLS_PART_8, ...SKILLS_PART_9];\nconst REFS: SkillRef[] = [...REFS_PART_1, ...REFS_PART_2, ...REFS_PART_3, ...REFS_PART_4, ...REFS_PART_5, ...REFS_PART_6, ...REFS_PART_7, ...REFS_PART_8, ...REFS_PART_9, ...REFS_PART_10, ...REFS_PART_11, ...REFS_PART_12, ...REFS_PART_13, ...REFS_PART_14, ...REFS_PART_15, ...REFS_PART_16];\n\nconst BY_NAME = new Map(BUILTIN_SKILLS.map((s) => [s.name, s]));\n\nexport function getSkillDef(name: string): SkillDef | undefined { return BY_NAME.get(name); }\nexport function getSkillBody(name: string): string | null { return BY_NAME.get(name)?.body ?? null; }\nexport function listReferences(name: string): string[] { return REFS.filter((r) => r.skill === name).map((r) => r.ref); }\nexport function getReference(name: string, ref: string): string | null {\n const hit = REFS.find((r) => r.skill === name && r.ref === ref);\n return hit ? hit.body : null;\n}\n",[],[528,874],"ff025a0e-5d6d-11f1-b895-1607ee4eb77e","ref_9.ts","ff025aea-5d6d-11f1-b895-1607ee4eb77e","c9768083-9936-4697-a4d4-80b7978fe701",668,["D",1780286243627],"// GENERATED by deploy/gen_skills_catalog.mjs — do not edit by hand.\nimport type { SkillRef } from \"../index.ts\";\nexport const REFS_PART_9: SkillRef[] = [\n {\"skill\":\"marketing-plan\",\"ref\":\"example-quietude\",\"body\":\"# Example — Quietude Marketing Plan v1\\n\\n**This is the canonical reference example for the `/marketing-plan` skill.** It's based on a real fCMO engagement for a hybrid hardware-and-software wellness platform. **Names, domains, and identifying details have been changed** — the client is called \\\"Quietude\\\" here, and the team members have been renamed (Alex / Sam / Casey / Devon). The funnel numbers, budget, and structural lessons preserve the shape of the original engagement so the example retains its teaching value.\\n\\nUse this as the \\\"what good looks like\\\" reference when drafting a new plan. The structure, tone, depth, and operational specificity are the bar to clear.\\n\\n**Quietude's archetype:** Hybrid hardware + software with deep-tech / clinical credibility layer. See `references/client-types.md` for archetype patterns.\\n\\n**Funding-stage context:** Pre-seed-close (mid-raise on $3M seed). Tier 1 per `references/funding-stage-unlocks.md`. $0 paid budget; organic + lifecycle + ambassador only.\\n\\n**What was strong about this plan:**\\n- Strategic frame (Section 2) leaned on the founder's own meditation-vs-regulation framing as the content pillar\\n- Current state (Section 3) included the 17-section audit rubric scored against existing materials (no formal audit run)\\n- 90-day roadmap (Section 9) had owner-assigned moves, not just actions\\n- Ops stack (Section 11) included a concrete operational proof-point (Customer.io MCP used live by non-technical founder on the kickoff call)\\n- Tactical idea bank (Section 12) cross-referenced all 139 marketing-ideas to AARRR + Quietude-specific status, including 23 explicit skips with rationale\\n\\n---\\n\\n# Quietude — Marketing Plan v1\\n\\n**Prepared by:** Casey Reed (fCMO)\\n**For:** Alex, Sam, and the Quietude team\\n**Date:** 2026-05-27\\n**Status:** Draft v1 — for team review\\n\\n## 1. Executive summary\\n\\nQuietude has built something rare: a clinically validated, brand-coherent, founder-led product in a category that doesn't yet have a name. The opportunity in the next twelve months is not to invent a marketing engine from scratch — it's to **convert the existing organic gravity into a measurable, repeatable funnel**, then layer paid acquisition on top of that funnel once the seed round closes.\\n\\n**Three big bets, ranked by leverage:**\\n\\n1. **Fix the leak before pouring water in.** The Day 1 → Day 35 funnel shape (1.34% → 5.46%) tells us the product converts given time and contact. What it's missing is a working first-session moment (the headphone gate is killing conversion) and a lifecycle layer to deliver the contact. These two pieces — onboarding rebuild and Customer.io flows shipped — are the unlock for everything else.\\n2. **Compound the moats Quietude already has.** Peer-reviewed clinical study, longevity-influencer PR, 15K live event participants, Alex's founder voice — these are link generators, content pillars, and credibility anchors that most wellness brands would kill for. They're under-leveraged. SEO, content, and App Store optimization translate them into search and discovery surface area.\\n3. **Build the founder-and-fCMO operating system that lets a 4-person team market like a 20-person one.** This is what makes the plan actually executable at Quietude's team size and burn rate — agentic tooling on top of Customer.io, Shopify, App Store, Stripe, GitHub, and the marketing skill library means we ship without hiring.\\n\\n**What twelve months looks like, plausibly:**\\n\\n- App goes from beta to GA. Onboarding converts at meaningful lift over today's baseline.\\n- 4 SEO content pillars staked, with Pillar 1 (Nervous System Regulation) and Pillar 2 (Sleep + Eye Mask) ranking on Tier-1 keywords.\\n- Full lifecycle live in Customer.io: onboarding, lapsed re-engagement, hardware post-purchase, subscription-center opt-ins.\\n- Ambassador program live with 15–25 active hosts. First Quietude Guides cert pilot run.\\n- Eye mask wedge selling at scale via Shopify with a clean hardware → app activation path. Blended CAC measured and tracked.\\n- Paid acquisition firing post-seed-close at $5–10K/mo initial test budget, scaling to $20–50K/mo if unit economics validate.\\n- Series A narrative writes itself: clinical evidence + activation lift + lifecycle compounding + first B2B install reference cases.\\n\\n**The 90-day priorities** (which the rest of this doc operationalizes):\\n\\n1. Kill the headphones gate. Ship the bedrock fix this week.\\n2. Run the three-variant onboarding test. Find the activation winner.\\n3. Ship Customer.io Flows 6 (eye mask post-purchase) and 4 (lapsed user) — hold Flow 2 (onboarding) until app UI stabilizes.\\n4. Rewrite the App Store listing in Quietude's brand voice. Highest-leverage non-site asset right now.\\n5. Stake the SEO foundation: consolidate to `quietude.app`, publish Pillar 1 hub + 3 spokes, publish the peer-reviewed psychophysiology study landing page.\\n6. Launch the ambassador program with the ~5 inbound waiting.\\n\\nEverything else compounds on top of those six.\\n\\n---\\n\\n## 2. Strategic frame\\n\\nThis section distills positioning, ICP, and brand voice into what the team needs to keep in mind while executing. Full detail lives in `marketing-os.md`, `icp.md`, and `sound-philosophy.md`.\\n\\n### What Quietude is, in one sentence\\n\\nA nervous system intelligence platform — clinically validated spatial audio + AI reflection companion (Mira) + hardware + venue installations + practitioner network. *\\\"We start with sound. We expand to every sense. We end with cities.\\\"*\\n\\n### The category we're claiming (and defending)\\n\\nQuietude doesn't fit the meditation app category, the focus audio category, or the sleep tech category. The brand makes a stronger claim: **bottom-up nervous system regulation through spatial audio**, with clinical evidence as proof and somatic credibility as defense.\\n\\nThe category-defining frame, per Alex (2026-05-19): **Meditation is top-down. Quietude is bottom-up.** Meditation uses the mind to command the body — mental kung fu that fails the very people most likely to need help, because the prefrontal cortex is offline when stressed. Quietude enters through the brainstem, before the thinking mind. The body responds before it has to try. (Full content-pillar treatment in `meditation-vs-regulation.md`.)\\n\\nThis is the single most important strategic message. It belongs in App Store copy, onboarding, lifecycle email, SEO content, ambassador talking points, and the seed deck.\\n\\n### Who we're for (D2C ICP, distilled)\\n\\nOverstimulated high-achieving professionals, 25–45, urban (Bay Area, NYC, London, Berlin, Austin). Tech workers, founders, creators, academics, designers, consultants. Often neurodivergent (ADHD, HSP, gifted). Sophisticated wellness buyers — already invested heavily in their inner life.\\n\\n**Their stated problem:** *\\\"I can't shut my brain off. I've tried meditation apps. They don't work.\\\"*\\n\\n**Their real problem:** Overstimulation, not under-motivation. Their gift (quick thinking) became a curse. They need permission to stop optimizing — including their rest.\\n\\n**What they're actually buying:** the *feeling* of stability, sensory indulgence, beautiful rituals, effortless effectiveness, a luxurious shortcut to the genius they can't access in chaos.\\n\\n### The business model logic (per seed deck)\\n\\n**B2B seeds the market. D2C harvests.** A venue install puts Quietude in front of ~20K people/year at ~$17K cost → 5% convert to subs → ~$430K/year per venue. Six compound channels (referral, Guides, content, home hosting, PR, community) make CAC approach zero by Year 3. Year 5: 75% of new subs come from near-zero-cost channels.\\n\\n**fCMO scope per kickoff: D2C-led.** Alex owns B2B sales through events/network/founder credibility. The fCMO leverage is on the app/hardware D2C side. This plan reflects that split — B2B is acknowledged as the harvest engine but not treated as primary work surface.\\n\\n### Brand voice (the non-negotiable)\\n\\nPer Marketing OS:\\n- **Tone.** Authoritative yet accessible. Intimate yet professional. Revolutionary yet grounded. Authority comes from lived experience, not explanation.\\n- **Speak from the body, not the mind.** Every sentence restores somatic safety and orientation. Language opens space rather than closing meaning.\\n- **YES vocabulary:** Aliveness, inner life, nervous system, spatial sound, resonance, somatic safety, embodied clarity, natural rhythm, orientation, initiation, truth-telling.\\n- **NO vocabulary:** Zen, chill, vibes, \\\"high-vibe,\\\" spiritual bypass, meditation clichés, didactic/explainer language, \\\"let me explain why this works.\\\"\\n- **Core method: Initiatory Reflection.** Writing's purpose isn't to explain or convince — it's to shift the reader's internal state. The result should be *\\\"something in me moved,\\\"* not *\\\"I understand this concept.\\\"*\\n- **CTA rule:** Never pressure. \\\"We do not remind. We invite.\\\"\\n\\nThis rule constrains every piece of copy across every AARRR stage. When in doubt: rewrite from the body.\\n\\n---\\n\\n## 3. Current state\\n\\nThis is what we're starting from — team, budget, what's already in motion, what's stuck, scored against the CF Marketing Audit 17-section rubric.\\n\\n### Team composition (marketing surface area)\\n\\n| Person | Role | Marketing surface area |\\n|---|---|---|\\n| **Alex** | Co-founder, CEO | Owns: personal LinkedIn, live events, B2B sales, founder narrative, investor relations, brand voice authorship |\\n| **Sam** | Co-founder, CXO | Owns: clinical/somatic credibility, brand-voice stewardship, somatic angle on copy review, practitioner network |\\n| **Devon** | Lead Dev | Owns: product/UI build, instrumentation, Customer.io event wiring, App Store deployment |\\n| **Ed Dorsey** | Design Advisor | Advisory cadence (ex-Apple/Airbnb/Strava) |\\n| **Emily Babich** | Creative Strategy | Advisory cadence |\\n| **Matt Mikkelsen** | Field Recording | Audio library, not marketing |\\n| **Casey Reed** | fCMO | Strategy, lifecycle, SEO, onboarding tests, content, ambassador program, ops stack |\\n\\n**No dedicated marketing hire yet.** First hire likely post-seed close (Q3 2026 candidate): a lifecycle + content marketing manager who owns Customer.io, SEO content production, and ambassador operations day-to-day.\\n\\n### Marketing budget (current)\\n\\n- **Paid acquisition:** $0. Confirmed by Alex, 2026-05-20: *\\\"D2C UA so far: My personal LinkedIn posts, live Quietude events, organic word of mouth, and organic app store discovery.\\\"* No paid layer.\\n- **Tooling stack:** Customer.io subscription, Shopify (eye mask storefront), App Store Connect, GA4 (or pending), Stripe, Notion, Dub.co (ambassador attribution). Estimate ~$500–1,500/mo combined.\\n- **fCMO retainer:** Casey Reed engagement.\\n- **PR:** No paid PR. Organic longevity-influencer tailwind, consumer-tech angels + foundation-model lab network.\\n\\n**Implication:** The 90-day plan must produce gains without any paid lever pulled. Everything in the next 12 weeks is organic, lifecycle, or product-level. Paid is a Q2–Q3 unlock.\\n\\n### What's already done (acknowledge, then build on)\\n\\n| Asset | Status | Marketing leverage |\\n|---|---|---|\\n| Peer-reviewed peer-reviewed psychophysiology study (2025) | Published | Anchor of clinical authority. Most undermarketed asset Quietude owns. |\\n| longevity-influencer eye-mask endorsement | Live, generating Shopify sales | Press hook. Underused for landing-page social proof. |\\n| consumer-tech angels + foundation-model lab investment | Closed | Investor PR opportunity. \\\"Why I invested\\\" Substack/Medium pieces. |\\n| 15K+ live event participants over a decade | Real | Email list potential, ambassador pool, testimonial bank, B2B reference. |\\n| Quietude eye mask (5K in stock) | Selling | The wedge product. Hardware → app activation path. |\\n| 38% 12-month retention (vs. category avg 20%) | Real | Headline metric. Belongs everywhere. |\\n| Customer.io + Shopify integration | Wired | The lifecycle infrastructure exists. Flows just need to ship. |\\n| 4 GitHub repos for context + product | Set up | `quietude-context` (shared brain), `quietude-promo`, `quietude-app` (app), `mira` (AI), `quietude-api` |\\n| Alex's Sound Philosophy doc | Working doc | Linkable position paper once polished and published. |\\n| ~5 inbound ambassadors waiting | Inbound | Referral program ready to launch — no demand-gen needed for v1. |\\n| Aurora B2B install (~€250K, July deadline) | In-flight | First flagship venue. Reference case once installed. |\\n| Notion Knowledge Directory | Live | Internal context. |\\n| Customer.io MCP (Claude integration) | Validated on kickoff | Non-technical team can ship flows independently. |\\n\\n### What's in-flight (drafted but not shipped)\\n\\n| Item | Status | Blocker |\\n|---|---|---|\\n| Flow 2 — App Onboarding (8 emails / 14 days) | Draft | App UI in flux; copy references screens that may change |\\n| Flow 4 — Lapsed User Re-engagement (5 emails / 38 days) | Draft | None — ship-ready |\\n| Flow 6 — Eye Mask Post-Purchase | Draft | None — ship-ready |\\n| Onboarding rebuild (3-variant test plan) | Strategy doc done | Eng scoping + headphone-gate removal |\\n| SEO 90-day plan + keyword research | Done | Awaiting domain consolidation decision + content production start |\\n\\n### What's stuck (and needs to unstick this quarter)\\n\\n| Issue | Cost of inaction | Action |\\n|---|---|---|\\n| Headphones hard-gate in onboarding | Confirmed conversion drop post-launch | Kill this week (bedrock fix) |\\n| 4 domains unconsolidated (quietude.app, quietude.space, quietude.audio, quietude.center) | SEO authority fragmenting, transactional email confusion | Consolidate to `quietude.app` per SEO data |\\n| App Store listing copy not in brand voice | Highest-traffic Quietude surface; off-brand experience for arriving users | Rewrite in voice (Pillar 1) |\\n| Domain consolidation requires 301 plan + email sender migration | Risk of traffic loss if mishandled | Plan in weeks 1–2, execute weeks 3–4 |\\n| `quietude-promo` repo hasn't shipped since March 2026 | Marketing site is stale | Confirm whether it's live; rewrite or replace |\\n| 29% monthly App Store churn vs. 38% 12-month retention claim | Metric definition mismatch confusing the team | Reconcile with Devon + Customer.io data |\\n| Mira post-session reflection scope unknown | Blocks Variant B and Variant C onboarding tests | Resolve with Devon |\\n\\n### Audit rubric snapshot (17-section)\\n\\nScored 0–5 from materials, using the embedded rubric in `references/current-state-rubric.md`. Marked \\\"scored from materials\\\" rather than \\\"formal audit\\\" — Alex can push back on any score where they have better data.\\n\\n| # | Section | Score | Note |\\n|---|---|---|---|\\n| 1 | Positioning | **4** | Clear, original category claim. The bottom-up frame is the strongest piece. Needs broader external articulation. |\\n| 2 | Customer research | **4** | Deep founder-led research, decade of live participants. Could be more systematically captured. |\\n| 3 | Homepage | **2** | `quietude-promo` hasn't shipped since March. Off-brand voice in places. |\\n| 4 | Sales / product pages | **2** | Eye mask page exists on Shopify but isn't optimized for SEO or sales narrative. No app-product landing page in brand voice. |\\n| 5 | Conversion pages | **2** | `/partner` exists on `quietude.app`. No `/science`, `/eye-mask`, `/ambassadors`, `/guides` pages live. |\\n| 6 | Competitor comparison | **1** | Nothing exists. Big SEO + sales opportunity (own \\\"Quietude vs. Calm/Headspace/Brain.fm/Endel\\\" SERPs). |\\n| 7 | Resources / content | **1** | Sound Philosophy not yet public. peer-reviewed psychophysiology study not yet on a dedicated page. No blog. |\\n| 8 | Onboarding | **2** | Headphones gate killing conversion. Hold-and-fix project this quarter. |\\n| 9 | Email lifecycle | **1** | All three flows drafted, none live. Ship-order set. |\\n| 10 | Sales material | **3** | Seed deck is strong (investor-facing). B2B sales material more founder-led than asset-led. |\\n| 11 | Messaging | **5** | Alex + Sam have authored the most distinctive brand voice in the wellness category. This is a moat. |\\n| 12 | Pricing | **3** | $30/mo app, $45 eye mask, $7,500 speakers, $50–200K B2B. Hasn't been pressure-tested for D2C conversion lift. |\\n| 13 | CRO | **2** | App Store conversion rate trackable but no A/B history. Headphones gate is the obvious first test removal. |\\n| 14 | GTM / launches | **2** | App in throttled beta. Major launches (eye mask, Mira public) haven't had structured GTM. |\\n| 15 | Ads | **0** | No paid layer. Reflects the current organic strategy — not a weakness, but the budget unlock means this will move. |\\n| 16 | SEO | **1** | Current state: 7 organic visits/mo. Plan exists; execution not yet started. |\\n| 17 | Internationalization | **1** | Finland HQ + global ICP, but EN-only and US-centric copy. Defer until Q4+. |\\n\\n**Total: 36 / 85 (42%).** The shape matters more than the score: high in Positioning + Messaging + Customer research, low in Conversion pages + Email lifecycle + SEO + Resources + Ads. That's the gap this plan closes.\\n\\n---\\n\\n## 4. Acquisition\\n\\n> *\\\"How do strangers become aware of Quietude?\\\"*\\n\\n### Current state\\n\\n100% organic. Four real channels: Alex's personal LinkedIn, live Quietude events, organic word of mouth, organic App Store discovery. Plus passive PR drag from longevity-influencer endorsement + clinical study.\\n\\nThis is good news, not bad. Every dollar of revenue earned to date has been earned without paid acquisition. The bar to exceed it isn't high; the upside on top of an organic base is significant.\\n\\n### The plan\\n\\n**Channel 1 — SEO (primary 90-day investment).**\\nThe full 90-day plan lives in `seo/plan.md`. Summary: consolidate to `quietude.app`, target three asymmetric clusters (nervous-system regulation KD 14–32, weighted/blackout sleep mask KD 6–30, WELL + social-wellness-club B2B KD 5–34), publish 4 content pillars. 90-day target: 500–1,500 organic visits/mo, 80+ ranking keywords. 12-month target: 10,000/mo, 1,000+ keywords.\\n\\n**Channel 2 — App Store optimization (highest-leverage non-site asset).**\\nThe App Store listing is currently the most-visited Quietude URL by Apple's algorithm. Fixing the copy is higher-leverage this quarter than fixing the marketing site. Rewrite in brand voice. Add the meditation-vs-regulation framing. Lead with the clinical anchor. Test screenshot variations.\\n\\n**Channel 3 — Alex's LinkedIn (productize the channel).**\\nToday it's ad-hoc founder posting. The next move is structured: a 2–3x/week cadence, post categories that map to the content pillars (nervous system, sound science, founder journey, clinical evidence, behind-the-scenes), trackable links via Dub, follower → email subscriber → app install funnel measured. This is Alex's voice — the channel only works if he's the one writing. fCMO + Typefully scheduling makes the cadence sustainable.\\n\\n**Channel 4 — PR amplification.**\\nlongevity-influencer tailwind is real but underused on owned surfaces. Add a `/notable-users` or `/in-the-press` page. Pitch the peer-reviewed psychophysiology study to 5 outlets (wellness press: Well+Good, MindBodyGreen; tech-adjacent: Wired with the longevity-influencer hook; mainstream: Outside, Forbes Wellness). HARO/Help-A-B2B-Writer responses citing Quietude's data. Investor PR moments (\\\"Why I invested in Quietude\\\" Substack pieces from consumer-tech angels — push for these with backlinks).\\n\\n**Channel 5 — Event-to-app instrumentation.**\\nLive events are the highest-converting ICP exposure Quietude has (15K+ participants, decade of trust). They're un-instrumented. Add: per-event QR code → app install + email capture, post-event lifecycle (Customer.io Flow 7?), event ROI tracking. Goal: turn an event from a one-night conversion moment into a 30-day funnel.\\n\\n**Channel 6 — Eye mask wedge (consumer entry product).**\\n5K masks in stock. Shopify storefront exists but isn't optimized. Improvements: SEO-optimize the product page (target \\\"weighted sleep mask,\\\" \\\"blackout sleep mask,\\\" \\\"silk sleep mask\\\"), add reviews via Judge.me (per kickoff decision), 30-day return policy (US-market expectation, per kickoff), build the listicle (\\\"Quietude vs. Manta vs. Nodpod vs. Lumon\\\"). Consider Amazon listing as a v2 distribution play.\\n\\n**Channel 7 — B2B venue installs (kept lean per kickoff).**\\nAlex owns this. Marketing supports with: case studies after each install, `/partner` page rewrite in voice (already exists on quietude.app), Pillar 4 content (\\\"The Missing Sound Feature in WELL\\\"), reciprocal links from partner venues baked into contracts.\\n\\n**Channel 8 — Paid layer (unlocked post-seed close).**\\nHeld until seed funding lands. Initial test budget: $5–10K/mo split across Apple Search Ads (highest-intent for App Store), Meta (Instagram + Facebook for eye mask), LinkedIn (B2B venue buyers). Don't fire until: (a) onboarding bedrock fix is shipped, (b) Flow 6 is live, (c) at least one Pillar landing page is in voice. Paid amplifies what already works — premature paid amplifies what's broken.\\n\\n### 90-day acquisition moves\\n\\n- Weeks 1–2: Domain consolidation decision + 301 plan. App Store listing rewrite first pass.\\n- Weeks 3–4: Domain 301s executed. GSC migration. SEO Pillar 1 hub drafted.\\n- Weeks 5–8: Pillar 1 hub + 3 spokes published. Pillar 2 (Eye Mask) hub + listicle published. Alex's LinkedIn cadence operationalized via Typefully. peer-reviewed psychophysiology study lands on dedicated `/science` page.\\n- Weeks 9–12: Pillar 4 (WELL/B2B) cornerstone published. Sound Philosophy goes public at `/research/sound-philosophy`. First PR push: pitch study + longevity-influencer hook to 5 outlets.\\n\\n### 12-month acquisition outlook\\n\\n- Q1 (Months 1–3): Foundation. SEO pillars staked. App Store rewrite shipped. LinkedIn cadence stable. PR push launched.\\n- Q2 (Months 4–6, post-seed close): Paid acquisition pilot at $5–10K/mo. SEO compounding — Pillar 1 ranking. First B2B install reference case live.\\n- Q3 (Months 7–9): Paid scales to $20–30K/mo if unit economics hold. All four pillars producing. App GA — new GTM moment.\\n- Q4 (Months 10–12): Compound channels live. 50+ pieces of pillar content. First Quietude Guides program pilot creating local SEO + earned media.\\n\\n### Skills + tools\\n\\n- **Skills:** `seo-audit`, `ai-seo`, `programmatic-seo`, `schema`, `content-strategy`, `competitors`, `launch`, `ads`, `ad-creative`, `social`, `typefully`, `analytics`, `copywriting`, `marketing-website-design`, `free-tools`\\n- **MCPs / APIs:** Ahrefs API, DataForSEO API, Typefully MCP (LinkedIn scheduling), GA4 MCP (when wired), GitHub MCP (`quietude-promo` repo work), Notion (knowledge directory), Stripe MCP (LTV / paid-CAC math), `agent-browser` (LinkedIn drafting + testing), `defuddle` (research)\\n\\n---\\n\\n## 5. Activation\\n\\n> *\\\"Once someone tries Quietude, do they have an experience that converts?\\\"*\\n\\n### Current state\\n\\nDay 1 → paid: **1.34%**. Day 7 → paid: **3.73%**. Day 35 → paid: **5.46%**. *The funnel shape is the signal.* The ~4× lift over 35 days means the product converts given time and contact — both of which the current onboarding undermines and the lifecycle layer doesn't yet provide.\\n\\nCaveats: app is in throttled beta. Metrics are noisy. Don't optimize against absolutes; optimize against funnel *shape* and *cohort comparison*.\\n\\n### The plan\\n\\n**Move 1 — Kill the headphones hard-gate (bedrock fix, this week).**\\nConfirmed conversion drop after the gate shipped. The fix isn't better copy on the gate — it's removing the gate. Replace with passive headphone detection + soft single-line nudge. No regret change. Full reasoning in `onboarding-recommendation.md`.\\n\\n**Move 2 — Run the three-variant onboarding test.**\\nThree variants, each a pure expression of one belief about what drives activation in this ICP:\\n- **Variant 1 — Trust First.** Bold promise + clinical anchor + testimonial wall + 1-line mechanism. Tests whether the saturated ICP needs framing before they'll invest.\\n- **Variant 2 — Seen First.** Multi-step diagnostic → AI-generated \\\"we see you\\\" summary → personalized session. Tests whether being accurately named is the conversion event.\\n- **Variant 3 — Felt First.** Audio starts on app open. ~15 words on screen. The session IS the onboarding. Tests whether the product can carry it cold.\\n\\nTest sequence (sequential, ~7 weeks to a winner): bedrock baseline → V3 vs. baseline → winner vs. V1 → winner vs. V2. Full system in `onboarding-recommendation.md`.\\n\\n**Move 3 — App Store listing rewrite.**\\nHighest-leverage non-site asset. Rewrite in brand voice. Lead with meditation-vs-regulation. Screenshot variations to test. This is also an Acquisition move (organic discovery) but it lives here because it's the threshold to the trial.\\n\\n**Move 4 — Customer.io Flow 2 (held until UI stable).**\\nThe 8-email / 14-day onboarding sequence is drafted and on-brand. Holding the ship because the emails reference in-app screens that will change during the onboarding rebuild. Once a winning onboarding variant ships, Flow 2 gets a copy refresh against the final UI and goes live.\\n\\n**Move 5 — Paywall + pricing review (cross-cuts to Revenue).**\\nWhat's the current trial structure? Length, paywall trigger, intro pricing? When the funnel shape is \\\"lift over 35 days,\\\" extending trial may convert better than aggressively gating earlier. To be audited in Q1.\\n\\n### 90-day activation moves\\n\\n- Week 1: Headphones gate removed. Baseline established.\\n- Weeks 2–3: Variant 3 (Felt First) prototyped, instrumented, shipped to a test cohort.\\n- Weeks 4–5: Read Variant 3 vs. baseline. Decide ship/iterate. Begin Variant 1 build.\\n- Weeks 6–7: Variant 1 (Trust First) live.\\n- Weeks 8–9: Read V1 vs. winner. Begin Variant 2 build.\\n- Weeks 10–11: Variant 2 (Seen First) live.\\n- Week 12: Final read. Winning variant scheduled for permanent ship. Flow 2 unblocked.\\n\\n### 12-month activation outlook\\n\\n- Q1: Winning variant identified and shipped.\\n- Q2: Flow 2 ships. Paywall A/B tests start.\\n- Q3: GA launch — onboarding re-validated at higher traffic. Cohort segmentation by acquisition source (Shopify/eye-mask vs. direct vs. ambassador vs. paid) starts to drive variant forks.\\n- Q4: Onboarding is no longer the bottleneck. Focus moves to Activation → Retention transition (sessions 2–7).\\n\\n### Skills + tools\\n\\n- **Skills:** `onboarding`, `signup`, `cro`, `cro`, `paywalls`, `popups`, `copywriting`, `copy-editing`, `copycraft`, `marketing-website-design`, `ab-testing`, `marketing-psychology`\\n- **MCPs / APIs:** App Store Connect (manual + `dev-browser` for screenshot automation), GitHub MCP (`quietude-app` app repo for onboarding code), Figma / Pencil MCP (for onboarding screen design), Customer.io MCP (for any in-app/email coordination), GA4 MCP (activation events)\\n\\n---\\n\\n## 6. Retention\\n\\n> *\\\"Once someone converts, do they stay — and deepen?\\\"*\\n\\n### Current state\\n\\n**Headline metric (per seed deck): 38% 12-month retention** — nearly double the category average (~20%). This is the strongest single retention signal in the deck and one of the most undermarketed claims Quietude owns.\\n\\n**App Store snapshot, 2026-05-16:** 145 paid, 42 churned (~29% monthly churn). Definition mismatch with the 38% claim — to reconcile. Possibly: 38% is annual cohort retention (people who paid month 1 and still pay month 12), 29% is gross monthly churn (people who paid this month who didn't pay next month). Both can be true. Need to clarify which metric is reported externally and which is the actual product health signal.\\n\\n### The plan\\n\\n**Move 1 — Ship Flow 6 first (Eye Mask Post-Purchase).**\\nPer kickoff decision and the onboarding-recommendation doc: this is the ship-ready flow. Hardware-anchored, doesn't reference in-app screens, can ship today. Wires the hardware → app activation path (eye mask buyers should get a free 6-month Premium trial — formalize this as part of the flow).\\n\\n**Move 2 — Ship Flow 4 second (Lapsed User Re-engagement).**\\nFive emails over 38 days. Language is universal — doesn't depend on app UI state. Ship after Flow 6 is live.\\n\\n**Move 3 — Hold Flow 2 (Onboarding).**\\nEight emails over 14 days. Holds until app UI stabilizes post-onboarding-rebuild. Don't ship copy that will need rewriting in 8 weeks.\\n\\n**Move 4 — Customer.io subscription center with opt-in topics.**\\nPer kickoff decision. Topics: events, app updates, somatics & nervous system, eye mask promotions. Users self-segment. Improves deliverability (lower complaint rates) and gives lifecycle a richer segmentation surface.\\n\\n**Move 5 — Mira post-session reflection (when scoped).**\\nMost powerful retention move medium-term. After a session, Mira asks *\\\"What did you notice?\\\"* Optional preset chips + free text. Two payoffs: (a) gives Mira priors for personalization on session 2+, (b) reflection responses become a content + segmentation goldmine for the team. Scope question for Devon — does Mira currently support this, or is it new build?\\n\\n**Move 6 — Hardware → app activation flow.**\\nThe eye-mask-buyer-becomes-Premium-subscriber path is hinted in the seed deck (blended CAC via hardware) but isn't visible in the App Store dashboard. Audit the existing flow: does an eye mask Shopify purchase actually deliver a free Premium code? How is it redeemed? What's the conversion rate? This is foundational to the \\\"B2C wedge\\\" thesis.\\n\\n**Move 7 — Reconcile the retention metric.**\\nWhat's the actual definition of \\\"38% 12-month retention\\\"? Cohort? Plan type (monthly vs. annual)? Survives this even if the answer is uncomfortable — the team and investors need to be talking about the same metric.\\n\\n**Move 8 — Annual plan as default (cross-cuts to Revenue).**\\nIndustry pattern: defaulting to annual reduces churn anxiety and improves LTV. To test in Q2.\\n\\n### 90-day retention moves\\n\\n- Weeks 1–2: Flow 6 (eye mask post-purchase) ships. Address fixes from kickoff review (study link line break, CAN-SPAM footer, founder face-bubble signature, Judge.me reviews).\\n- Weeks 3–4: Flow 4 (lapsed user re-engagement) ships.\\n- Weeks 5–6: Customer.io subscription center built and live.\\n- Weeks 7–8: Hardware → app activation flow audited and documented. Fix any leaks.\\n- Weeks 9–10: Retention metric reconciliation (with Devon).\\n- Weeks 11–12: Win-back campaign for churned cohort — test re-activation copy.\\n\\n### 12-month retention outlook\\n\\n- Q1: Flows 6 + 4 firing. Subscription center live.\\n- Q2: Flow 2 ships (post-onboarding-rebuild). Mira post-session reflection in production. Annual plan default tested.\\n- Q3: GA launch — retention metrics re-baselined at higher volume. Cohort-based lifecycle flows (eye mask vs. direct app install).\\n- Q4: Full lifecycle compound. Retention is no longer a top-three concern — focus moves to Referral and Revenue.\\n\\n### Skills + tools\\n\\n- **Skills:** `emails`, `churn-prevention`, `copywriting`, `copy-editing`, `paywalls`, `ab-testing`\\n- **MCPs / APIs:** **Customer.io MCP** (validated on kickoff — non-technical team can ship flows), Shopify (eye mask buyers as event source), Stripe MCP (subscription state, churn cohort pulls), GA4 MCP (session events, retention curves)\\n\\n---\\n\\n## 7. Referral\\n\\n> *\\\"Do retained users bring more users — and at what cost?\\\"*\\n\\n### Current state\\n\\n~5 inbound ambassadors waiting (per kickoff). Dub.co set up. No formal program yet. WOM happens naturally per Alex's UA breakdown.\\n\\nThis is one of the strongest leading indicators in the business: 5 unaffiliated people have raised their hand asking to bring Quietude to their network *before any program exists*. That signal doesn't show up in apps with weaker product-market fit.\\n\\n### The plan\\n\\n**Move 1 — Launch the ambassador program with the 5 inbound.**\\nTier 1 of the program. Per-ambassador landing pages (e.g., `quietude.app/with/sarah`). Dub.co tracks attribution. Commission structure to determine (per kickoff, $/sub or rev-share TBD). Soft-launch with the 5 — treat as pilot cohort, gather feedback, refine before opening applications.\\n\\n**Move 2 — Build the share-after-shift moment.**\\nThe Mira post-session reflection (see Retention) is the natural moment to surface a share prompt. After a user reports a felt shift, offer: *\\\"Want to share Quietude with someone who needs this?\\\"* Single-line, never pushy. Most powerful WOM mechanism: gift-a-month flow where the recipient gets a discounted or free intro.\\n\\n**Move 3 — Founder amplification (Alex + Sam as ambassador-zero).**\\nAlex mentioning the fCMO engagement in fundraise pitches (permission granted). Reciprocal mentions in fCMO-side content. Sam's clinical network → practitioner ambassador pool.\\n\\n**Move 4 — Quietude Guides cert pilot (long-term, Q3+).**\\nThe Guides program is the Phase-2 referral compound (per seed deck). 500–1,000 Guides across 50+ cities by Y3–5. First cert pilot: 3–5 hosts who run live sessions, get a rev-share + co-marketing. Builds local SEO + earned media + ambassador-of-ambassadors flywheel. Hold until paid + lifecycle are firing — Guides is a multi-quarter build.\\n\\n**Move 5 — Eye mask gifting flow.**\\nHardware referral is rare and powerful. *\\\"Send a friend an Quietude eye mask. They get the mask + a free 3-month Premium. You get a credit toward your next thing.\\\"* Holiday/gifting peak windows are the test.\\n\\n### 90-day referral moves\\n\\n- Weeks 1–4: Ambassador program scoped, commission structure decided, per-ambassador landing page template built, 5 inbound onboarded.\\n- Weeks 5–8: First ambassador-driven sales tracked via Dub. Attribution and payout flow validated.\\n- Weeks 9–12: Open applications for next 10–15 ambassadors. Begin Quietude Guides scoping.\\n\\n### 12-month referral outlook\\n\\n- Q1: Ambassador program live with 5–10 active.\\n- Q2: 15–25 active ambassadors. Share-after-shift moment in production (post-Mira reflection).\\n- Q3: Guides cert pilot launched (3–5 hosts). Eye mask gifting flow live for holiday peak.\\n- Q4: 50+ ambassadors + 5–10 Guides. Referral driving 15–25% of new D2C subs.\\n\\n### Skills + tools\\n\\n- **Skills:** `referrals`, `social`, `copywriting`, `marketing-website-design` (per-ambassador landing pages)\\n- **MCPs / APIs:** Dub.co (attribution — already in stack), Stripe MCP (commission accounting + payouts), GitHub MCP (landing page deployment in `quietude-promo` or new `quietude-ambassadors` repo), Customer.io MCP (ambassador lifecycle: onboarding, monthly performance digest, payout notification)\\n\\n---\\n\\n## 8. Revenue\\n\\n> *\\\"What do we charge, who pays, and how does that compound?\\\"*\\n\\n### Current state\\n\\n| Product | Price | Volume signal |\\n|---|---|---|\\n| Quietude App + Mira | ~$30/mo | 145 paid subs (App Store snapshot 2026-05-16) |\\n| Quietude Eye Mask | ~$45 | 5K in stock, longevity-influencer PR-driven sales |\\n| Quietude Audio (speakers) | ~$7,500 | Niche, founder-led |\\n| Quietude Spaces (B2B install) | $50–200K | Aurora flagship in-flight (~€250K), pipeline of 4 venues |\\n| Quietude Experiences (events) | Varies | 15K+ historical participants |\\n| Quietude Guides | Rev share | Not yet operational |\\n\\n**Revenue to date: ~$500K on ~$250K raised.** Capital-efficient. Hardware + B2B + app subs all contributing.\\n\\n**MRR (App Store snapshot): $592.** Beta-throttled, not steady-state. The implied ~$4/sub/mo against $30/mo list suggests heavy annual plan adoption (

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