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Your goal is to ask the fewest, most concise, and natural questions possible, one at a time. If information is missing or refused, make a reasonable assumption or broaden your scope. Iteratively ask questions and integrate responses until all required information for the {{task}} within the {{domain}} is gathered.\nRULES\nPrimary Goal: Ask single, concise questions to gather the minimum required information for the task, aiming for completion efficiently.\nDecision Point: After analyzing the conversation history, decide if you have enough information.If YES: Your immediate response MUST be the token \"COMPLETE\" followed by a single, minified JSON object of all gathered data. Do not ask for confirmation. Example: \\`COMPLETE\\`{\"party_date\":\"next Saturday\",\"guests\":50,\"location\":\"rented hall\"}\nIf NO: You are missing critical information. Proceed to the next rule.\nQuestion Formulation: If more info is needed, formulate the single most impactful and concise question.\nOutput Format (Questions): The question must be a short, direct interrogative. DO NOT add conversational filler, commentary, apologies, or instructions.\nCONTEXT\nTask: {{task}}\nDomain: {{domain}}\nConversation History:\n{{history}}`,\n PROMPT_REFINER:\n `You are a native JSON speaking prompt engineer. Your mission is to transmute occupational tasks into direct, end-to-end deliverables that align with *industry-standard formats* for that type of output within the defined industry.\n# CORE RULE:\nReturn a prompt with {{variables}} placeholders, but ensure that the output is a polished, fully formed artifact recognized in this field as a professional deliverable.\n# INSTRUCTIONS\n1. Persona: Start with \"You are a top 0.1% [Occupation]...\"\n2. Objective: Translate the Task into a directive that *produces the actual output* (e.g., for accountants → \"Audit Report\", for UX designers → \"Wireframe Specification\", for lawyers → \"Legal Agreement\").\n3. Context: Incorporate the Company Context to ground the deliverable in the business setting.\n4. Placeholders: Convert user-supplied inputs into descriptive {{snake_case_placeholders}}. Include at least 2.\n5. Delivery Rules:\n - Output must follow the **standard deliverable structure** in that domain (document sections, layout, terminology).\n - Exclude plans, commentary, or explanations.\n - If uncertain, assume the most common professional output format for that industry and task type.\n6. Output Format: The return prompt must define a clear final output type (e.g., “Return a structured PDF-ready report”).\n7. Mandate: End with a directive like “Produce the deliverable now.”\nReturn a JSON object:{\"refined_prompt\": \"...\"}`,\n MASTER_PROMPT_ENGINEER:\n `You are a native JSON speaking master prompt architect. Construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, conforming to **industry-standard deliverable formats**.\n# INSTRUCTIONS\n1. Deconstruct Task\n2. Define Expert Persona\n3. Contextualize Industry-Specific Deliverable Expectations\n4. Establish Logic and Negative Constraints\n5. Define Output Schema and Examples\n6. Conclude with Execution Command\nReturn the blueprint as markdown, no JSON wrapper.`,\n INDUSTRY_GENERATOR:\n `You are a native JSON speaking expert business analyst. Generate a list of professional industries most relevant to the given company.\nRULES\nAnalyze Context: Deeply analyze the provided company context (name, description, use case) to understand their core business.\nGenerate Industries: Produce a list of 10-12 diverse yet highly relevant industries.\nOutput Format: Your entire response MUST be a single, minified JSON object with one key: \"industries\". The value must be an array of strings.\nCOMPANY CONTEXT\n{{company_context}}`,\n DYNAMIC_LIST_GENERATOR:\n `You are a native JSON speaking expert AI that generates context-aware lists for professional applications. Each generated entity (occupation or task) must map directly to a tangible, industry-standard deliverable format used in that field.\n# RULES\n1. **Analyze Context:** Deeply analyze 'company_context' and, if available, 'industry' or 'occupation' to determine what outputs are considered professional and standard for that domain (e.g., for Marketing → \"Campaign Brief\", for Software → \"Technical Specification\", for Legal → \"Contract Draft\").\n2. **Generate List:**\n - If 'generation_type' is \"occupations\", list specific professional roles relevant to that industry.\n - If 'generation_type' is \"tasks\", ONLY generate tasks that culminate in a clear, recognized output (e.g., \"Create a press release\", \"Develop a system architecture diagram\", \"Draft a privacy policy\").\n3. **Output Standardization:** Each task must inherently correspond to a recognized document, presentation, report, or artifact with a conventional structure in that field.\n4. **Output Format:** Strict JSON — one key \"generated_list\", value = array of objects with:\n - \"name\" (string): concise, professional task or role title\n - \"description\" (string): one line describing the standard deliverable expected\n5. **Relevance and Specificity:** Avoid abstract or open-ended tasks (e.g., \"Think about\", \"Plan for\", \"Brainstorm\"). Use only tasks where a complete deliverable output exists.\n6. **Input is JSON:** You must parse the JSON input before generating.`,\n EVALUATOR_AGENT:\n `You are a native JSON speaking, meticulous, and impartial AI QA agent. Given a task briefing, success criteria, and AI output, you will:\nScore: Score the output 1-10 on each criterion.\nCritique: Provide a concise, professional critique of its strengths and weaknesses.\nSCORING RULES\nGut Check: Before all else, if the output has obvious placeholders ('example.com', '[Insert Name Here]') or is extremely generic, penalize heavily on \"Plausibility and Specificity.\"\nPenalize Non-Deliverables: If the output is a plan or commentary instead of the final deliverable, penalize heavily (scores <= 3) on the relevant criterion.\nOUTPUT FORMAT\nYour entire response MUST be a single, minified JSON object: {\"scores\": [{\"criterion\": \"...\", \"score\": ...}], \"critique\": \"...\"}. You MUST write the critique in the requested language. Add no other text or markdown.`,\n REFINER_AGENT:\n `You are a top-tier \"Refiner\" agent. You have your original task briefing, your first draft, and a critique from a QA agent. Your SOLE mission is to rewrite the first draft to fully address every point in the critique, elevating it to a 10/10 standard.\nRULES\nAnalyze and Integrate: Apply all suggested changes from the critique to the first draft.\nNo Commentary: Do not apologize, explain changes, or include meta commentary.\nFinal Deliverable Only: Your output must be only the new, improved, final version.\nLanguage: Write the final output in the requested language.`,\n SUCCESS_CRITERIA_GENERATOR:\n `You are a native JSON speaking expert QA analyst. Your task is to generate a concise list of success criteria for evaluating an AI-generated document.\nRULES\nAnalyze Task: Deeply analyze the task briefing to understand the desired output's purpose and requirements.\nGenerate Criteria: Create 4-6 specific, measurable, relevant success criteria as short strings.\nOutput Format: Your response MUST be a single, minified JSON object: {\"criteria\": [\"...\", \"...\", ...]}.\nTASK BRIEFING\n{{task_briefing}}`,\n },\n seedTemplates: {\n \"default\":\n `You are a top-tier professional expert. Your task is to produce a complete, industry-standard deliverable.`,\n \"marketing\":\n `You are an expert marketing strategist. Produce a professional deliverable ready for a client presentation. The output must adhere to standard marketing document formats (e.g., brief, proposal, copy deck).`,\n \"development\":\n `You are a senior software architect. Produce the technical deliverable requested. The output must be a complete, well-structured technical artifact (e.g., specification, API documentation, architecture plan) using industry-standard formatting (like Markdown).`,\n \"operations\":\n `You are a director of operations. Produce a formal business document for the task. This must be a structured report, plan, or policy, ready for internal distribution and executive review.`,\n \"general\":\n `You are a versatile business professional. Produce a polished, well-structured document that directly fulfills the task requirements, formatted for immediate professional use.`,\n \"prompt_building\":\n `You are a native JSON speaking master prompt architect. Construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, conforming to industry-standard deliverable formats.`,\n },\n};\n\n// --- STATIC DATA ---\nconst taskCategories = [\n {\n id: \"thinking\",\n name: \"Strategic Planning\",\n description: \"Generate strategies, outlines, or comprehensive analyses.\",\n icon: \"🧠\",\n },\n {\n id: \"prompt_building\",\n name: \"Prompt Engineering\",\n description:\n \"Create detailed, expert-level prompts for AI systems that yield production-grade outputs.\",\n icon: \"🛠️\",\n },\n {\n id: \"document_creation\",\n name: \"Professional Documents\",\n description:\n \"Produce structured, industry-standard documents or artifacts ready for immediate professional use.\",\n icon: \"📄\",\n },\n];\n\n// --- BACKEND SERVICES ---\nconst services = {\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: {\n model?: string;\n isJson?: boolean;\n stream?: boolean;\n c?: Context;\n } = {},\n ) {\n const {\n model = config.models.major,\n isJson = false,\n stream = false,\n c = null,\n } = options;\n const openai = new OpenAI();\n const messages: any[] = [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: typeof userContent === \"string\"\n ? userContent\n : JSON.stringify(userContent),\n }];\n const requestPayload: any = { model, messages, stream };\n if (isJson) requestPayload.response_format = { type: \"json_object\" };\n try {\n const completion = await openai.chat.completions.create(requestPayload);\n if (stream && c) {\n return streamText(c, async (s) => {\n for await (const part of completion) {\n await s.write(part.choices[0]?.delta?.content || \"\");\n }\n });\n }\n if (isJson && c) {\n return c.json(JSON.parse(completion.choices[0].message.content));\n }\n return completion;\n } catch (e) {\n console.error(`Error calling OpenAI: ${e.message}`);\n throw new Error(\"AI service failed.\");\n }\n },\n};\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string): string {\n const languages = {\n \"English\": \"en-US\",\n \"Spanish (Espa\\\\u00f1ol)\": \"es-ES\",\n \"French (Fran\\\\u00e7ais)\": \"fr-FR\",\n \"German (Deutsch)\": \"de-DE\",\n \"Japanese (\\\\u65e5\\\\u672c\\\\u8a9e)\": \"ja-JP\",\n \"Chinese Simplified (\\\\u7b80\\\\u4f53\\\\u4e2d\\\\u6587)\": \"zh-CN\",\n \"Chinese Traditional (\\\\u7e41\\\\u9ad4\\\\u4e2d\\\\u6587)\": \"zh-TW\",\n \"Korean (\\\\ud55c\\\\uad6d\\\\uc5b4)\": \"ko-KR\",\n \"Russian (\\\\u0420\\\\u0443\\\\u0441\\\\u0441\\\\u043a\\\\u0438\\\\u0439)\": \"ru-RU\",\n \"Arabic (\\\\u0627\\\\u0644\\\\u0639\\\\u0631\\\\u0628\\\\u064a\\\\u0629)\": \"ar-SA\",\n \"Hindi (\\\\u0939\\\\u093f\\\\u0928\\\\u094d\\\\u0926\\\\u0940)\": \"hi-IN\",\n \"Portuguese (Portugu\\\\u00eas)\": \"pt-PT\",\n \"Italian (Italiano)\": \"it-IT\",\n \"Dutch (Nederlands)\": \"nl-NL\",\n \"Polish (Polski)\": \"pl-PL\",\n \"Turkish (T\\\\u00fcrk\\\\u00e7e)\": \"tr-TR\",\n \"Vietnamese (Ti\\\\u1ebfng Vi\\\\u1ec7t)\": \"vi-VN\",\n \"Thai (\\\\u0e44\\\\u0e17\\\\u0e22)\": \"th-TH\",\n \"Navajo (Din\\\\u00e9)\": \"nv\",\n \"Cherokee (\\\\u13e3\\\\u13b3\\\\u13a9)\": \"chr\",\n \"Lakota (Lak\\\\u021f\\\\u00f3tiyapi)\": \"lkt\",\n \"Ojibwe (Anishinaabemowin)\": \"oji\",\n \"Apache (Nd\\\\u00e9\\\\u00e9)\": \"apw\",\n \"Choctaw (Chahta)\": \"cho\",\n \"Creek (Mvskoke)\": \"mus\",\n \"Hopi (Hopilavayi)\": \"hop\",\n \"Inuktitut (\\\\u1403\\\\u14c4\\\\u1483\\\\u144e\\\\u1450\\\\u1466)\": \"iku\",\n \"Mohawk (Kanien'k\\\\u00e9ha)\": \"moh\",\n \"Sioux (Dak\\\\u021f\\\\u00f3tiyapi)\": \"sio\",\n \"Zuni (Shiwi'ma)\": \"zun\",\n };\n const languageOptions = Object.entries(languages).map(([name, code]) =>\n ``\n ).join(\"\");\n const taskCategoriesJson = JSON.stringify(taskCategories);\n\n return `Taskflow AI | Intelligent Task Automation

Great job!

You're making excellent progress.

Taskflow AI

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n/* ----------------------------------------\n Base Route\n----------------------------------------- */\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n/* ----------------------------------------\n Industry Generation\n----------------------------------------- */\napp.post(\"/api/industries\", async (c: Context) => {\n const { company_context } = await c.req.json();\n if (!company_context) {\n return c.json({ error: \"Company context is required\" }, 400);\n }\n const userContent = `Company Context: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n});\n\n/* ----------------------------------------\n Information Gatherer\n----------------------------------------- */\napp.post(\"/api/gather\", async (c: Context) => {\n const { task, domain, history } = await c.req.json();\n const historyString = history.map((h) =>\n `${h.role === \"assistant\" ? \"AI\" : \"User\"}: ${h.content}`\n ).join(\"\\n\");\n const prompt = config.prompts.INFORMATION_GATHERER.replace(\"{{task}}\", task)\n .replace(\"{{domain}}\", domain).replace(\n \"{{history}}\",\n historyString ||\n \"No history yet. Start with your most important question.\",\n );\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.choices[0].message.content.trim();\n if (responseText.startsWith(\"COMPLETE\")) {\n const jsonPart = responseText.replace(\"COMPLETE\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Gathering agent failed:\", e);\n return c.json({\n status: \"in-progress\",\n question:\n \"I'm having a little trouble with that. Could you please rephrase your last answer?\",\n }, 500);\n }\n});\n\n/* ----------------------------------------\n Dynamic Generation (Occupations / Tasks)\n----------------------------------------- */\napp.post(\"/api/generate/:type\", async (c: Context) => {\n const { type } = c.req.param();\n if (type !== \"occupations\" && type !== \"tasks\") {\n return c.json({ error: \"Invalid generation type\" }, 400);\n }\n const body = await c.req.json();\n const payload = {\n generation_type: type,\n company_context: body.company_context,\n ...(type === \"occupations\" && { industry: body.industry }),\n ...(type === \"tasks\" && { occupation: body.occupation }),\n constraints: {\n deliverable_focus:\n \"Only generate tasks that culminate in recognized, professional deliverables or artifacts for that industry (reports, specifications, proposals, contracts, policies, etc.)\",\n reject_types: [\n \"brainstorming\",\n \"ideation\",\n \"planning-only\",\n \"summarization\",\n \"discussion\",\n ],\n },\n };\n return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Dynamic Prompt Creation\n----------------------------------------- */\napp.post(\"/api/prompt/dynamic\", async (c: Context) => {\n const { occupation_title, task, company_context } = await c.req.json<\n DynamicPromptBody\n >();\n const userContent =\n `Company Context: ${JSON.stringify(company_context)}\\n\\n` +\n `Occupation: ${occupation_title}\\n\\n` +\n `Task: ${task}\\n\\n` +\n `MANDATE: The refined prompt MUST yield a tangible, industry-standard deliverable typical for this role and domain — e.g., structured reports, specifications, legal documents, marketing assets, or similar professional artifacts. Exclude anything conceptual or partial.`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Execute Stream (Task Execution)\n----------------------------------------- */\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, company_context, category } = await c.req\n .json();\n\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n let systemPrompt = \"\";\n let userPrompt = \"\";\n\n // Inject user inputs into the refined task briefing\n let finalTaskBriefing = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalTaskBriefing = finalTaskBriefing.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n\n // --- TEMPLATE SELECTION LOGIC ---\n let baseTemplate = \"\";\n if (category === \"prompt_building\") {\n baseTemplate = config.seedTemplates.prompt_building;\n // For prompt building, the user prompt IS the task\n userPrompt =\n `Create an expert execution prompt for the task below, fully aligned with standard deliverable formats of this industry.\\n\\n` +\n `Task: \"${finalTaskBriefing}\"\\n\\n` +\n `Company Context: ${JSON.stringify(company_context)}`;\n } else {\n // For all other categories, select template based on company's use case\n baseTemplate = config.seedTemplates[company_context.use_case] ||\n config.seedTemplates.default;\n\n userPrompt = \"Proceed with task execution now.\";\n }\n\n // Construct the final System Prompt\n systemPrompt = `# BASE DIRECTIVE\\n${baseTemplate}\\n\\n` +\n `# COMPANY CONTEXT\\n` +\n `- Company: ${company_context.company_name}\\n` +\n `- Description: ${company_context.company_description}\\n` +\n `- Use Case: ${company_context.use_case}\\n` +\n `- Language for Output: ${company_context.language}\\n` +\n `---\\n` +\n `# TASK BRIEFING\\n${finalTaskBriefing}\\n\\n` +\n `# FINAL INSTRUCTIONS\\n` +\n `1. Deliver a complete, well-structured, **industry-standard artifact** based *only* on the TASK BRIEFING.\\n` +\n `2. Do not include conversation, apologies, or explanations.\\n` +\n `3. Write the entire response in the requested language: **${company_context.language}**.`;\n\n // Use 'prompt_building' logic for its specific user prompt, otherwise use the default.\n // Note: Both branches call the same service, but this structure is kept\n // in case the userPrompt logic diverges more significantly later.\n if (category === \"prompt_building\") {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n } else {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n }\n});\n\n/* ----------------------------------------\n Evaluation\n----------------------------------------- */\napp.post(\"/api/evaluate\", async (c: Context) => {\n const { briefing, first_draft, company_context } = await c.req.json();\n if (!briefing || !first_draft || !company_context) {\n return c.json({\n error: \"Briefing, first_draft, and company_context are required\",\n }, 400);\n }\n try {\n const criteriaPrompt = config.prompts.SUCCESS_CRITERIA_GENERATOR.replace(\n \"{{task_briefing}}\",\n briefing,\n );\n const criteriaCompletion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n criteriaPrompt,\n { model: config.models.minor, isJson: true },\n );\n const success_criteria =\n JSON.parse(criteriaCompletion.choices[0].message.content).criteria;\n const userContent = {\n task_briefing: briefing,\n success_criteria: success_criteria,\n raw_output: first_draft,\n };\n return services.callOpenAI(config.prompts.EVALUATOR_AGENT, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n } catch (e) {\n console.error(\"Evaluation failed:\", e);\n return c.json(\n { error: `Failed to evaluate the document. ${e.message}` },\n 500,\n );\n }\n});\n\n/* ----------------------------------------\n Refinement\n----------------------------------------- */\napp.post(\"/api/refine\", async (c: Context) => {\n const { briefing, first_draft, critique, language } = await c.req.json();\n const userContent = `# YOUR TASK\nRewrite your first draft to fully address every point in the critique, elevating the work to a 10/10 standard. Return only the improved final deliverable. Do not write an apology, explanation, or any meta-commentary. Produce the new, superior output in the requested language: ${language}.\n## Original Briefing\n${briefing}\n## Your First Draft\n${first_draft}\n## QA Critique\n${critique}`;\n return services.callOpenAI(config.prompts.REFINER_AGENT, userContent, {\n c,\n stream: true,\n });\n});\n\nexport default app.fetch;","deleted",false,"label",[35],{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":57,"_17":47,"_48":49,"_50":51,"_52":53,"_54":14,"_58":59,"_18":60},["D",1761369411938],"children",[],[35],"4b2573f4-a760-11f0-b29b-0224a6c84d84",["D",1760269246788],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","userId","0199b749-1e1c-7399-b093-a7ba99382572","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","0199b749-1e1c-7399-b093-a97ac9fff69e","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_11":12,"_114":110},"org",{"_11":12,"_108":-5,"_109":110,"_70":71,"_111":112,"_113":53},"branch",{"_17":42,"_13":16,"_33":40,"_100":61,"_101":-5,"_102":-5,"_103":-5,"_104":-5,"_105":-5,"_106":14,"_107":12},"isOwned","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzMzZm1qMllGY2JhQmhhWFYwVXVvQ05aT2tJciJ9","tier","free","canRestrictVals","profileImageUrl","adoptOrgId",{"_33":61,"_13":14,"_45":148,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":149,"_95":150,"_97":151,"_99":53},[145],"files",[145],"counts",{"_135":77,"_136":137,"_138":77,"_31":139,"_140":141},[133],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":14,"_18":134},[35],"pulls","branches",1,"forks",{"_142":77,"_144":77},"connections",{"_142":77,"_143":77},"count","unfilledCount","emptyCount",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":57,"_17":47,"_48":49,"_50":51,"_52":53,"_54":14,"_58":146,"_18":147},[],[35],["D",1760269246788],{"_11":12,"_114":110},{"_11":12,"_108":-5,"_109":110,"_70":71,"_111":112,"_113":53},{"_17":42,"_13":16,"_33":40,"_100":61,"_101":-5,"_102":-5,"_103":-5,"_104":-5,"_105":-5,"_106":14,"_107":12},"ctx",{"_93":-5,"_130":185,"_186":187,"_188":189,"_190":53,"_191":53,"_192":53,"_193":44,"_194":-5,"_195":-7,"_196":-7,"_197":77,"_198":53,"_199":53,"_200":-5,"_201":53,"_202":53,"_203":53,"_204":-5},"ENV",{"_156":157,"_158":159,"_160":16,"_161":162,"_163":164,"_165":166,"_167":168,"_169":170,"_171":172,"_173":174,"_175":176,"_177":178,"_179":180,"_181":182,"_183":184},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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