Declarative AI Workflow Engine

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":57,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",12,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","424b236e-73ee-11f0-8547-0224a6c84d84","name","fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { cors } from \"npm:hono@4.4.12/cors\";\n// @ts-ignore\nimport Parser from \"npm:rss-parser@3.13.0\";\n// Hono's SSE helper for streaming live updates\n// @ts-ignore\nimport { streamSSE } from \"npm:hono@4.4.12/streaming\";\n\n// --- AI PROMPT LIBRARY ---\nconst PROMPTS = {\n ProductMarketFit:\n `You are an AI embodying a skeptical Venture Capitalist conducting due diligence. Your task is to use the provided live market data (news and Reddit comments) to construct a compelling, evidence-based argument for why this product is likely to fail due to poor market fit, saturation, or user indifference. Ground every point in the provided data.`,\n MarketEntry:\n `You are an AI embodying a geopolitical and economic risk analyst. Your task is to use the provided live economic data (from the World Bank) and news headlines to find factual, data-driven evidence that challenges the proposed market entry plan. Focus on macroeconomic headwinds and regulatory risks revealed in the data.`,\n TechnicalFeasibility:\n `You are an AI embodying a cynical, tenured Staff Engineer or Principal Engineer. Your task is to stress-test a technical proposal. Use the provided data (developer sentiment from Reddit) to find reasons why the proposed tech stack is a poor choice, why the assumptions are flawed, or why the project is likely to result in significant technical debt or failure. Be brutally honest about technical trade-offs.`,\n HiringPlan:\n `You are an AI embodying a veteran, cynical technical recruiter at a major tech company. Your job is to find flaws in a hiring plan. Use the provided data (news about tech hiring/layoffs, developer discussions on Reddit) to argue why this plan is unrealistic. Focus on extreme competition for talent, unrealistic skill combinations (i.e., 'unicorns'), below-market compensation signals, or negative sentiment around a particular technology or role.`,\n};\n\n// --- DATA FETCHING & ACTION LIBRARY ---\nconst countryCodes = {\n \"brazil\": \"br\",\n \"india\": \"in\",\n \"china\": \"cn\",\n \"germany\": \"de\",\n \"united states\": \"us\",\n \"united kingdom\": \"gb\",\n \"japan\": \"jp\",\n \"nigeria\": \"ng\",\n};\n\nconst ACTIONS = {\n FETCH_NEWS: async (params) => {\n const parser = new Parser();\n const RSS_URL = `https://news.google.com/rss/search?q=${\n encodeURIComponent(params.query)\n } when:30d&hl=en-US&gl=US&ceid=US:en`;\n try {\n const feed = await parser.parseURL(RSS_URL);\n return feed.items.slice(0, 7).map((item) => ({\n title: item.title,\n snippet: item.contentSnippet,\n }));\n } catch {\n return [{ title: \"Could not fetch news via RSS.\" }];\n }\n },\n // BUG FIX: The original version of this action was flawed.\n // This new version correctly parses the 'subreddits' string into an iterable array.\n FETCH_REDDIT_SENTIMENT: async (params) => {\n const allPosts = [];\n let subreddits = [];\n\n if (params.subreddits && typeof params.subreddits === \"string\") {\n subreddits = params.subreddits.replace(/[\\[\\]]/g, \"\").split(\",\").map(\n (s) => s.trim()\n );\n }\n\n if (subreddits.length === 0) {\n return [{ title: \"No subreddits provided or parsed correctly.\" }];\n }\n\n for (const subreddit of subreddits) {\n try {\n const url = `https://www.reddit.com/r/${subreddit}/search.json?q=${\n encodeURIComponent(params.query)\n }&sort=relevance&t=month&restrict_sr=on`;\n const r = await fetch(url, {\n headers: { \"User-Agent\": \"Mozilla/5.0\" },\n });\n const d = await r.json();\n allPosts.push(\n ...(d.data?.children?.map((p) => ({\n title: p.data.title,\n subreddit: p.data.subreddit,\n })) || []),\n );\n } catch (e) {\n console.error(`Failed to fetch from subreddit: ${subreddit}`, e);\n }\n }\n return allPosts.slice(0, 15);\n },\n INVOKE_AI: async (params) => {\n if (!params.user_prompt) {\n throw new Error(\"INVOKE_AI action requires a 'user' prompt.\");\n }\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: params.system_prompt || \"You are a helpful AI assistant.\",\n },\n { role: \"user\", content: params.user_prompt },\n ],\n temperature: parseFloat(params.temperature) || 0.4,\n });\n return completion.choices[0].message.content;\n },\n};\n\n// --- CORE ENGINE ---\nfunction parseDeclarativePlan(text) {\n const lines = text.split(\"\\n\").filter((line) => !line.trim().startsWith(\"#\"));\n const plan = { VARS: {}, PROMPTS: {}, WORKFLOW: [] };\n let currentSection = null;\n let currentPromptKey = null;\n let currentPromptPart = null;\n let currentWorkflowStep = null;\n for (const line of lines) {\n const indent = line.match(/^\\s*/)[0].length;\n const trimmed = line.trim();\n if (!trimmed) continue;\n if (indent === 0) {\n currentSection = trimmed.replace(\":\", \"\");\n currentPromptKey = null;\n currentWorkflowStep = null;\n continue;\n }\n switch (currentSection) {\n case \"VARS\":\n const [varKey, ...varValueParts] = trimmed.split(\":\");\n plan.VARS[varKey.trim()] = varValueParts.join(\":\").trim();\n break;\n case \"PROMPTS\":\n if (indent === 2) {\n currentPromptKey = trimmed.replace(\":\", \"\");\n plan.PROMPTS[currentPromptKey] = {};\n currentPromptPart = null;\n } else if (indent === 4 && currentPromptKey) {\n const partKey = trimmed.split(\":\")[0].trim();\n if (partKey === \"system\" || partKey === \"user\") {\n currentPromptPart = partKey;\n plan.PROMPTS[currentPromptKey][currentPromptPart] = trimmed\n .substring(partKey.length + 1).replace(/>$/, \"\").trim();\n }\n } else if (indent > 4 && currentPromptKey && currentPromptPart) {\n plan.PROMPTS[currentPromptKey][currentPromptPart] += \"\\n\" + trimmed;\n }\n break;\n case \"WORKFLOW\":\n if (trimmed.startsWith(\"- id:\")) {\n if (currentWorkflowStep) plan.WORKFLOW.push(currentWorkflowStep);\n currentWorkflowStep = { params: {} };\n const [wfKey, ...wfValueParts] = trimmed.substring(2).split(\":\");\n currentWorkflowStep[wfKey.trim()] = wfValueParts.join(\":\").trim();\n } else if (currentWorkflowStep) {\n const [paramKey, ...paramValueParts] = trimmed.split(\":\");\n const value = paramValueParts.join(\":\").trim();\n if (\n paramKey.trim() === \"action\" || paramKey.trim() === \"prompt_key\" ||\n paramKey.trim() === \"temperature\"\n ) {\n currentWorkflowStep[paramKey.trim()] = value;\n } else {\n currentWorkflowStep.params[paramKey.trim()] = value;\n }\n }\n break;\n }\n }\n if (currentWorkflowStep) plan.WORKFLOW.push(currentWorkflowStep);\n return plan;\n}\n\nfunction resolveVariables(template, state) {\n if (typeof template !== \"string\") return template;\n return template.replace(/\\{\\{(.*?)\\}\\}/g, (match, key) => {\n const keys = key.trim().split(\".\");\n let value = state;\n for (const k of keys) {\n value = value?.[k];\n }\n if (typeof value === \"object\") {\n return JSON.stringify(value, null, 2);\n }\n return value !== undefined ? value : match;\n });\n}\n\nasync function executePlan(plan, stream, isDebug) {\n const state = { VARS: plan.VARS, PROMPTS: plan.PROMPTS };\n let finalOutput =\n \"Workflow completed, but the final step produced no output.\";\n\n const log = async (message) => {\n if (isDebug && stream) {\n await stream.writeSSE({ data: JSON.stringify({ type: \"log\", message }) });\n }\n };\n\n for (const step of plan.WORKFLOW) {\n const action = ACTIONS[step.action];\n if (!action) throw new Error(`Unknown action: ${step.action}`);\n\n await log(`[BEGIN] Executing step: ${step.id} (Action: ${step.action})`);\n\n let resolvedParams = {};\n if (step.action === \"INVOKE_AI\") {\n const promptTemplate = state.PROMPTS[step.prompt_key];\n if (!promptTemplate) {\n throw new Error(\n `Prompt key not found in PROMPTS section: '${step.prompt_key}'`,\n );\n }\n\n await log(` > Resolving prompt template: ${step.prompt_key}`);\n resolvedParams = {\n system_prompt: resolveVariables(promptTemplate.system, state),\n user_prompt: resolveVariables(promptTemplate.user, state),\n temperature: step.temperature || \"0.4\",\n };\n await log(` > Invoking AI model...`);\n } else {\n await log(` > Resolving parameters...`);\n for (const key in step.params) {\n resolvedParams[key] = resolveVariables(step.params[key], state);\n }\n await log(` > Fetching external data...`);\n }\n\n const result = await action(resolvedParams);\n state[step.id] = { result };\n finalOutput = result;\n await log(`[DONE] Step ${step.id} complete.`);\n }\n\n return finalOutput;\n}\n\n// --- HTML & FRONTEND ---\nfunction generateHtml() {\n const examplePlan = `# Plan: 'Wickerbrook Vale' Go/No-Go Analysis\nVARS:\n game_idea: \"A cozy life-simulation game called 'Wickerbrook Vale', similar to Stardew Valley but with a witch-themed narrative and potion-crafting system.\"\n genre: \"Cozy life-simulation game\"\n platform: \"Nintendo Switch\"\n competitors: \"Stardew Valley, Animal Crossing, Wylde Flowers\"\n\nPROMPTS:\n market_synthesis_prompt:\n system: >\n You are a Senior Market Analyst for a major video game publisher. Your task is to synthesize the provided news articles and raw player sentiment from Reddit into a concise \"State of the Market\" report for the cozy game genre.\n Identify:\n 1. Key opportunities (e.g., features players are begging for).\n 2. Major threats (e.g., market saturation, player burnout).\n user: >\n RECENT NEWS ARTICLES:\n {{FETCH_MARKET_NEWS.result}}\n\n RAW PLAYER SENTIMENT FROM REDDIT:\n {{FETCH_PLAYER_SENTIMENT.result}}\n\n red_team_critique_prompt:\n system: >\n You are a skeptical, data-driven Executive Producer (Red Team). Your job is to find reasons this game will fail. Using the provided market analysis, write a sharp critique of the game idea. Connect every weakness you identify directly back to a threat or a lack of opportunity mentioned in the market data.\n user: >\n GAME IDEA:\n {{VARS.game_idea}}\n\n STATE OF THE MARKET REPORT:\n {{SYNTHESIZE_MARKET.result}}\n\n blue_team_pitch_prompt:\n system: >\n You are an optimistic, player-focused Marketing Lead (Blue Team). Your job is to create a compelling pitch for this game. Using the provided market analysis, write an enthusiastic pitch that shows how the game's features perfectly align with the opportunities and address the player complaints found in the market data.\n user: >\n GAME IDEA:\n {{VARS.game_idea}}\n\n STATE OF THE MARKET REPORT:\n {{SYNTHESIZE_MARKET.result}}\n\n final_memo_prompt:\n system: >\n You are the Head of Studio. You have just read the Red Team critique and the Blue Team pitch. Your task is to write the final Go/No-Go decision memo.\n 1. Briefly summarize the core conflict between the Red and Blue team perspectives.\n 2. Make a final \"Go\" or \"No-Go\" decision.\n 3. If \"Go\", list the 2-3 critical success factors that must be prioritized during development to mitigate the risks raised by the Red Team.\n user: >\n RED TEAM CRITIQUE:\n {{RED_TEAM_ANALYSIS.result}}\n\n BLUE TEAM PITCH:\n {{BLUE_TEAM_PITCH.result}}\n\nWORKFLOW:\n - id: FETCH_MARKET_NEWS\n action: FETCH_NEWS\n params:\n query: \"{{VARS.genre}} on {{VARS.platform}}\"\n\n - id: FETCH_PLAYER_SENTIMENT\n action: FETCH_REDDIT_SENTIMENT\n params:\n query: \"{{VARS.competitors}}\"\n subreddits: \"[CozyGamers, NintendoSwitch]\"\n\n - id: SYNTHESIZE_MARKET\n action: INVOKE_AI\n prompt_key: market_synthesis_prompt\n temperature: 0.2\n\n - id: RED_TEAM_ANALYSIS\n action: INVOKE_AI\n prompt_key: red_team_critique_prompt\n temperature: 0.6\n\n - id: BLUE_TEAM_PITCH\n action: INVOKE_AI\n prompt_key: blue_team_pitch_prompt\n temperature: 0.6\n\n - id: FINAL_RECOMMENDATION\n action: INVOKE_AI\n prompt_key: final_memo_prompt\n temperature: 0.3\n`;\n\n return `\n\n\n \n \n Declarative AI Workflow Engine\n \n \n \n \n\n\n\n

\n \n

Execution Log

\n\n\n`;\n}\n\n// --- HONO ROUTER & MAIN HANDLER ---\nconst app = new Hono();\napp.use(\"*\", cors());\napp.get(\"/\", (c) => c.html(generateHtml()));\n\napp.get(\"/api/execute-stream\", (c) => {\n return streamSSE(c, async (stream) => {\n try {\n const isDebug = c.req.query(\"debug\") === \"true\";\n const encodedPlan = c.req.query(\"plan\");\n if (!encodedPlan) {\n await stream.writeSSE({\n data: JSON.stringify({\n type: \"error\",\n message: \"Plan not provided.\",\n }),\n });\n return;\n }\n\n const planText = atob(encodedPlan);\n const plan = parseDeclarativePlan(planText);\n\n if (!plan.WORKFLOW || plan.WORKFLOW.length === 0) {\n await stream.writeSSE({\n data: JSON.stringify({\n type: \"error\",\n message: \"Invalid or empty workflow.\",\n }),\n });\n return;\n }\n\n const finalResult = await executePlan(plan, stream, isDebug);\n await stream.writeSSE({\n data: JSON.stringify({ type: \"result\", data: finalResult }),\n });\n } catch (error) {\n console.error(\"Workflow Execution Error:\", error);\n await stream.writeSSE({\n data: JSON.stringify({\n type: \"error\",\n message: error.message || \"An internal server error occurred.\",\n }),\n });\n }\n });\n});\n\nexport default app.fetch;","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { cors } from \"npm:hono@4.4.12/cors\";\n// @ts-ignore\nimport Parser from \"npm:rss-parser@3.13.0\";\n// @ts-ignore\nimport { streamSSE } from \"npm:hono@4.4.12/streaming\";\n\n// --- AI PROMPT LIBRARY ---\nconst PROMPTS = {\n ProductMarketFit:\n `You are an AI embodying a skeptical Venture Capitalist conducting due diligence. Your task is to use the provided live market data (news and Reddit comments) to construct a compelling, evidence-based argument for why this product is likely to fail due to poor market fit, saturation, or user indifference. Ground every point in the provided data.`,\n MarketEntry:\n `You are an AI embodying a geopolitical and economic risk analyst. Your task is to use the provided live economic data (from the World Bank) and news headlines to find factual, data-driven evidence that challenges the proposed market entry plan. Focus on macroeconomic headwinds and regulatory risks revealed in the data.`,\n TechnicalFeasibility:\n `You are an AI embodying a cynical, tenured Staff Engineer or Principal Engineer. Your task is to stress-test a technical proposal. Use the provided data (developer sentiment from Reddit) to find reasons why the proposed tech stack is a poor choice, why the assumptions are flawed, or why the project is likely to result in significant technical debt or failure. Be brutally honest about technical trade-offs.`,\n HiringPlan:\n `You are an AI embodying a veteran, cynical technical recruiter at a major tech company. Your job is to find flaws in a hiring plan. Use the provided data (news about tech hiring/layoffs, developer discussions on Reddit) to argue why this plan is unrealistic. Focus on extreme competition for talent, unrealistic skill combinations (i.e., 'unicorns'), below-market compensation signals, or negative sentiment around a particular technology or role.`,\n};\n\n// --- DATA FETCHING & ACTION LIBRARY ---\nconst countryCodes = {\n \"brazil\": \"br\",\n \"india\": \"in\",\n \"china\": \"cn\",\n \"germany\": \"de\",\n \"united states\": \"us\",\n \"united kingdom\": \"gb\",\n \"japan\": \"jp\",\n \"nigeria\": \"ng\",\n};\nconst ACTIONS = {\n FETCH_NEWS: async (params) => {\n const parser = new Parser();\n const RSS_URL = `https://news.google.com/rss/search?q=${\n encodeURIComponent(params.query)\n } when:30d&hl=en-US&gl=US&ceid=US:en`;\n try {\n const feed = await parser.parseURL(RSS_URL);\n return feed.items.slice(0, 7).map((item) => ({\n title: item.title,\n snippet: item.contentSnippet,\n }));\n } catch {\n return [{ title: \"Could not fetch news via RSS.\" }];\n }\n },\n FETCH_REDDIT_SENTIMENT: async (params) => {\n const allPosts = [];\n let subreddits = [];\n if (params.subreddits && typeof params.subreddits === \"string\") {\n subreddits = params.subreddits.replace(/[\\[\\]]/g, \"\").split(\",\").map(\n (s) => s.trim()\n );\n }\n if (subreddits.length === 0) {\n return [{ title: \"No subreddits provided or parsed correctly.\" }];\n }\n for (const subreddit of subreddits) {\n try {\n const url = `https://www.reddit.com/r/${subreddit}/search.json?q=${\n encodeURIComponent(params.query)\n }&sort=relevance&t=month&restrict_sr=on`;\n const r = await fetch(url, {\n headers: { \"User-Agent\": \"Mozilla/5.0\" },\n });\n const d = await r.json();\n allPosts.push(\n ...(d.data?.children?.map((p) => ({\n title: p.data.title,\n subreddit: p.data.subreddit,\n })) || []),\n );\n } catch (e) {}\n }\n return allPosts.slice(0, 15);\n },\n INVOKE_AI: async (params) => {\n if (!params.user_prompt) {\n throw new Error(\"INVOKE_AI action requires a 'user' prompt.\");\n }\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: params.system_prompt || \"You are a helpful AI assistant.\",\n },\n { role: \"user\", content: params.user_prompt },\n ],\n temperature: parseFloat(params.temperature) || 0.4,\n });\n return completion.choices[0].message.content;\n },\n};\n\n// --- CORE ENGINE ---\n\n// BUG FIX: The previous line-by-line parser was fragile. This new block-based parser is much more robust.\nfunction parseDeclarativePlan(text) {\n const plan = { VARS: {}, PROMPTS: {}, WORKFLOW: [] };\n const cleanText = text.split(\"\\n\").filter((line) =>\n !line.trim().startsWith(\"#\")\n ).join(\"\\n\");\n\n const sections = {\n VARS: cleanText.match(/^VARS:\\s*([\\s\\S]*?)(?=\\n^[A-Z]+:|$)/m),\n PROMPTS: cleanText.match(/^PROMPTS:\\s*([\\s\\S]*?)(?=\\n^[A-Z]+:|$)/m),\n WORKFLOW: cleanText.match(/^WORKFLOW:\\s*([\\s\\S]*?)(?=\\n^[A-Z]+:|$)/m),\n };\n\n // Parse VARS\n if (sections.VARS) {\n sections.VARS[1].trim().split(\"\\n\").forEach((line) => {\n const [key, ...valueParts] = line.trim().split(\":\");\n if (key) plan.VARS[key.trim()] = valueParts.join(\":\").trim();\n });\n }\n\n // Parse PROMPTS\n if (sections.PROMPTS) {\n const promptBlocks = sections.PROMPTS[1].trim().split(/^\\s{2}(?=\\w+:)/m);\n promptBlocks.forEach((block) => {\n if (!block.trim()) return;\n const lines = block.trim().split(\"\\n\");\n const promptKey = lines[0].replace(\":\", \"\").trim();\n const body = lines.slice(1).join(\"\\n\");\n const systemMatch = body.match(\n /\\s{4}system:\\s*>\\s*([\\s\\S]*?)(?=\\n\\s{4}user:|$)/m,\n );\n const userMatch = body.match(/\\s{4}user:\\s*>\\s*([\\s\\S]*?)(?=\\n|$)/m);\n plan.PROMPTS[promptKey] = {\n system: systemMatch ? systemMatch[1].replace(/^\\s+/gm, \"\") : \"\",\n user: userMatch ? userMatch[1].replace(/^\\s+/gm, \"\") : \"\",\n };\n });\n }\n\n // Parse WORKFLOW\n if (sections.WORKFLOW) {\n const stepBlocks = sections.WORKFLOW[1].trim().split(/^\\s*-\\s*(?=id:)/m);\n stepBlocks.forEach((block) => {\n if (!block.trim()) return;\n const step = { params: {} };\n block.trim().split(\"\\n\").forEach((line) => {\n const [key, ...valueParts] = line.trim().split(\":\");\n const value = valueParts.join(\":\").trim();\n if (\n key === \"id\" || key === \"action\" || key === \"prompt_key\" ||\n key === \"temperature\"\n ) {\n step[key] = value;\n } else if (key) {\n step.params[key] = value;\n }\n });\n plan.WORKFLOW.push(step);\n });\n }\n\n return plan;\n}\n\nfunction resolveVariables(template, state) {\n if (typeof template !== \"string\") return template;\n return template.replace(/\\{\\{(.*?)\\}\\}/g, (match, key) => {\n const keys = key.trim().split(\".\");\n let value = state;\n for (const k of keys) {\n value = value?.[k];\n }\n if (typeof value === \"object\") {\n return JSON.stringify(value, null, 2);\n }\n return value !== undefined ? value : match;\n });\n}\n\nasync function executePlan(plan, stream, isDebug) {\n const state = { VARS: plan.VARS, PROMPTS: plan.PROMPTS };\n let finalOutput =\n \"Workflow completed, but the final step produced no output.\";\n\n const log = async (message) => {\n if (isDebug && stream) {\n await stream.writeSSE({ data: JSON.stringify({ type: \"log\", message }) });\n }\n };\n\n for (const step of plan.WORKFLOW) {\n const action = ACTIONS[step.action];\n if (!action) throw new Error(`Unknown action: ${step.action}`);\n\n await log(`[BEGIN] Executing step: ${step.id} (Action: ${step.action})`);\n\n let resolvedParams = {};\n if (step.action === \"INVOKE_AI\") {\n const promptTemplate = state.PROMPTS[step.prompt_key];\n if (!promptTemplate) {\n throw new Error(\n `Prompt key not found in PROMPTS section: '${step.prompt_key}'`,\n );\n }\n\n await log(` > Resolving prompt template: ${step.prompt_key}`);\n resolvedParams = {\n system_prompt: resolveVariables(promptTemplate.system, state),\n user_prompt: resolveVariables(promptTemplate.user, state),\n temperature: step.temperature || \"0.4\",\n };\n await log(` > Invoking AI model...`);\n } else {\n await log(` > Resolving parameters...`);\n for (const key in step.params) {\n resolvedParams[key] = resolveVariables(step.params[key], state);\n }\n await log(` > Fetching external data...`);\n }\n\n const result = await action(resolvedParams);\n state[step.id] = { result };\n finalOutput = result;\n await log(`[DONE] Step ${step.id} complete.`);\n }\n\n return finalOutput;\n}\n\n// --- HTML & FRONTEND ---\nfunction generateHtml() {\n const examplePlan = `# Plan: 'Wickerbrook Vale' Go/No-Go Analysis\nVARS:\n game_idea: \"A cozy life-simulation game called 'Wickerbrook Vale', similar to Stardew Valley but with a witch-themed narrative and potion-crafting system.\"\n genre: \"Cozy life-simulation game\"\n platform: \"Nintendo Switch\"\n competitors: \"Stardew Valley, Animal Crossing, Wylde Flowers\"\n\nPROMPTS:\n market_synthesis_prompt:\n system: >\n You are a Senior Market Analyst for a major video game publisher. Your task is to synthesize the provided news articles and raw player sentiment from Reddit into a concise \"State of the Market\" report for the cozy game genre.\n Identify:\n 1. Key opportunities (e.g., features players are begging for).\n 2. Major threats (e.g., market saturation, player burnout).\n user: >\n RECENT NEWS ARTICLES:\n {{FETCH_MARKET_NEWS.result}}\n\n RAW PLAYER SENTIMENT FROM REDDIT:\n {{FETCH_PLAYER_SENTIMENT.result}}\n\n red_team_critique_prompt:\n system: >\n You are a skeptical, data-driven Executive Producer (Red Team). Your job is to find reasons this game will fail. Using the provided market analysis, write a sharp critique of the game idea. Connect every weakness you identify directly back to a threat or a lack of opportunity mentioned in the market data.\n user: >\n GAME IDEA:\n {{VARS.game_idea}}\n\n STATE OF THE MARKET REPORT:\n {{SYNTHESIZE_MARKET.result}}\n\n blue_team_pitch_prompt:\n system: >\n You are an optimistic, player-focused Marketing Lead (Blue Team). Your job is to create a compelling pitch for this game. Using the provided market analysis, write an enthusiastic pitch that shows how the game's features perfectly align with the opportunities and address the player complaints found in the market data.\n user: >\n GAME IDEA:\n {{VARS.game_idea}}\n\n STATE OF THE MARKET REPORT:\n {{SYNTHESIZE_MARKET.result}}\n\n final_memo_prompt:\n system: >\n You are the Head of Studio. You have just read the Red Team critique and the Blue Team pitch. Your task is to write the final Go/No-Go decision memo.\n 1. Briefly summarize the core conflict between the Red and Blue team perspectives.\n 2. Make a final \"Go\" or \"No-Go\" decision.\n 3. If \"Go\", list the 2-3 critical success factors that must be prioritized during development to mitigate the risks raised by the Red Team.\n user: >\n RED TEAM CRITIQUE:\n {{RED_TEAM_ANALYSIS.result}}\n\n BLUE TEAM PITCH:\n {{BLUE_TEAM_PITCH.result}}\n\nWORKFLOW:\n - id: FETCH_MARKET_NEWS\n action: FETCH_NEWS\n params:\n query: \"{{VARS.genre}} on {{VARS.platform}}\"\n\n - id: FETCH_PLAYER_SENTIMENT\n action: FETCH_REDDIT_SENTIMENT\n params:\n query: \"{{VARS.competitors}}\"\n subreddits: \"[CozyGamers, NintendoSwitch]\"\n\n - id: SYNTHESIZE_MARKET\n action: INVOKE_AI\n prompt_key: market_synthesis_prompt\n temperature: 0.2\n\n - id: RED_TEAM_ANALYSIS\n action: INVOKE_AI\n prompt_key: red_team_critique_prompt\n temperature: 0.6\n\n - id: BLUE_TEAM_PITCH\n action: INVOKE_AI\n prompt_key: blue_team_pitch_prompt\n temperature: 0.6\n\n - id: FINAL_RECOMMENDATION\n action: INVOKE_AI\n prompt_key: final_memo_prompt\n temperature: 0.3\n`;\n\n return `\n\n\n \n \n Declarative AI Workflow Engine\n \n \n \n \n\n\n\n

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Execution Log

Diff · v12