[{"_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",63,"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.ts","latestPath","fileId","4b285790-a760-11f0-b29b-0224a6c84d84","name","fileType","http","content","// @ts-nocheck\n// Taskflow AI - Professional Autonomous Agent Factory\n// A modern, user-friendly platform for AI-powered task execution\n// STYLING APPLIED FROM: TaskflowEnterprise React Component\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { streamText } from \"npm:hono@4.4.12/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n industry?: string;\n}\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\n}\ninterface GatherBody {\n task: string;\n domain: string;\n history: ConversationHistory[];\n}\ninterface DynamicPromptBody {\n occupation_title: string;\n task: string;\n company_context: CompanyContext;\n}\ninterface ExecuteBody {\n refined_prompt: string;\n user_inputs: Record;\n company_context: CompanyContext;\n category: string;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n prompts: {\n INFORMATION_GATHERER:\n `You are a native JSON speaking expert information gatherer designed to acquire all necessary details for a given task and domain. 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.\n\n# RULES\n1. **Primary Goal**: Ask single, concise questions to gather the minimum required information for the task, aiming for completion efficiently.\n2. **Decision Point**: After analyzing the conversation history, decide if you have enough information.\n - **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\"}\n - **If NO**: You are missing critical information. Proceed to the next rule.\n3. **Question Formulation**: If more info is needed, formulate the single most impactful and concise question.\n4. **Output Format (Questions)**: The question must be a short, direct interrogative. DO NOT add conversational filler, commentary, apologies, or instructions.\n\n# CONTEXT\n- Task: {{task}}\n- Domain: {{domain}}\n- Conversation History:\n{{history}}`,\n PROMPT_REFINER:\n `You are a native JSON speaking prompt engineer. Transmute raw occupational data into a concise, forceful directive for a target AI. The AI must perform the task end-to-end and return only the finished deliverable(s)—never a plan. You will receive an \"Occupation,\" \"Task,\" and \"Company Context.\" Your prompt must be a production-grade instruction that directly yields the final artifact. CORE RULE: You MUST return a prompt with {{variables}}. Your goal is to identify important, undefined entities in the Task and convert them to descriptive, {{snake_case_placeholders}}. You MUST include at least 2 placeholders. INSTRUCTIONS: 1. Persona: Start with \"You are a top 0.1% [Occupation]...\" for an expert tone. 2. Objective: Translate the Task into a formal, actionable objective implying completion. 3. Context: Use the 'Company Context' to make the objective specific (e.g., \"Your mandate for [Company Name] is to...\"). 4. Placeholders: Convert only USER-SUPPLIED functional inputs into {{snake_case_variables}}. 5. Delivery Rules: Require the AI to: - Return only the finished deliverable(s) now. - Exclude plans, methods, or explanations. - Avoid meta-commentary, caveats, or apologies. - If details are missing, make minimal, reasonable assumptions and proceed, optionally adding a short \"Assumptions\" list (<=5 bullets). - Use production-ready formatting (e.g., ready-to-send copy; runnable code in fenced blocks). - Never include sections like \"Plan\", \"Approach\", \"Methodology\". 6. Output Format: Specify the exact output structure. 7. Mandate: Conclude with a clear call to action like \"Produce the deliverable now.\" Your entire response MUST be a single, minified JSON object with one key, \"refined_prompt\". No other text or markdown.`,\n MASTER_PROMPT_ENGINEER:\n `You are a native JSON speaking master prompt architect. Your objective is to construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, not to do it yourself.\n\n# INSTRUCTIONS\n1. **Deconstruct Task:** Analyze the task to identify sub-tasks, knowledge domains, constraints, and desired output formats.\n2. **Establish Persona:** Define an expert persona for the target AI (e.g., \"You are a tenured professor of economics...\").\n3. **Provide Context:** Synthesize company context into a rich narrative explaining the task's strategic importance.\n4. **Formulate Logic:** Create a clear, logical sequence of operations using headings and lists.\n5. **Set Negative Constraints:** Explicitly state what the AI should *not* do (e.g., \"Do not use informal language,\" \"Do not output a plan\").\n6. **Define Output Schema:** Meticulously define the final output's structure, format, length, and tone.\n7. **Include Examples:** Where helpful, provide concise examples of good and bad outputs.\n8. **Final Mandate:** Conclude with a clear command to initiate execution.\n\nYour output must be the prompt itself, as a clean markdown document. Do not wrap it in JSON or add commentary.`,\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.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the provided company context (name, description, use case) to understand their core business.\n2. **Generate Industries**: Produce a list of 10-12 diverse yet highly relevant industries.\n3. **Output Format**: Your entire response MUST be a single, minified JSON object with one key: \"industries\". The value must be an array of strings.\n\n# COMPANY 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. You will receive a JSON object with company context and the list type to generate.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the 'company_context' to understand their business.\n2. **Generate List**: Create a list of 10-15 relevant items based on 'generation_type'.\n - If 'generation_type' is \"occupations\", generate specific job titles within the 'industry'.\n - If 'generation_type' is \"tasks\", generate specific, actionable tasks for the 'occupation'.\n3. **Output Format**: Your response MUST be a single, minified JSON object with one key: \"generated_list\".\n4. **List Structure**: The value must be an array of objects, each with \"name\" (string) and \"description\" (concise string).\n5. **Relevance is Key**: Items must be sharply focused and directly applicable to the company's use case.\n6. **Input is JSON**: You must parse the JSON input to get the context.`,\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:\n1. **Score:** Score the output 1-10 on each criterion.\n2. **Critique:** Provide a concise, professional critique of its strengths and weaknesses.\n# SCORING RULES\n1. **Gut 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.\"\n2. **Penalize Non-Deliverables:** If the output is a plan or commentary instead of the final deliverable, penalize heavily (scores <= 3) on the relevant criterion.\n# OUTPUT 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.\n\n# RULES\n1. **Analyze and Integrate:** Apply all suggested changes from the critique to the first draft.\n2. **No Commentary:** Do not apologize, explain changes, or include meta commentary.\n3. **Final Deliverable Only:** Your output must be only the new, improved, final version.\n4. **Language:** 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.\n# RULES\n1. **Analyze Task:** Deeply analyze the task briefing to understand the desired output's purpose and requirements.\n2. **Generate Criteria:** Create 4-6 specific, measurable, relevant success criteria as short strings.\n3. **Output Format:** Your response MUST be a single, minified JSON object: {\"criteria\": [\"...\", \"...\", ...]}.\n\n# TASK BRIEFING\n{{task_briefing}}`,\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: \"Create detailed, expert-level prompts for AI systems.\",\n icon: \"🛠️\",\n },\n {\n id: \"document_creation\",\n name: \"Professional Documents\",\n description:\n \"Produce polished, ready-to-use documents with quality refinement.\",\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\": \"es-ES\",\n \"French\": \"fr-FR\",\n \"German\": \"de-DE\",\n \"Japanese\": \"ja-JP\",\n \"Chinese (Simplified)\": \"zh-CN\",\n };\n const languageOptions = Object.entries(languages).map(([name, code]) =>\n ``\n ).join(\"\");\n const taskCategoriesJson = JSON.stringify(taskCategories);\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\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\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: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n});\n\napp.post(\"/api/gather\", async (c: Context) => {\n const { task, domain, history } = await c.req.json();\n const historyString = history.map((h) =>\n `${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\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 };\n return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {\n c,\n isJson: true,\n });\n});\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 = `Company Context: ${\n JSON.stringify(\n company_context,\n )\n }\\n\\nOccupation: ${occupation_title}\\n\\nTask: ${task}`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, company_context, category } = await c.req\n .json();\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n let systemPrompt = \"\";\n let userPrompt = \"\";\n switch (category) {\n case \"thinking\":\n case \"document_creation\": {\n let finalUserPrompt = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalUserPrompt = finalUserPrompt.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n systemPrompt =\n `# YOUR CONTEXT\\n- Company Name: ${company_context.company_name}\\n- Company Description: ${company_context.company_description}\\n- Primary Goal: ${company_context.use_case}\\n- Language for Output: ${company_context.language}\\n---\\n# YOUR TASK BRIEFING\\n${finalUserPrompt}\\nYou MUST write your entire response in the requested language: ${company_context.language}.`;\n userPrompt = \"Proceed with the task.\";\n break;\n }\n case \"prompt_building\": {\n systemPrompt = config.prompts.MASTER_PROMPT_ENGINEER;\n userPrompt =\n `Generate a master prompt for the following task, considering the company's context.\\n\\nTask: \"${refined_prompt}\"\\n\\nCompany Context: ${\n JSON.stringify(\n company_context,\n )\n }`;\n break;\n }\n default:\n return c.json({ error: \"Invalid execution category\" }, 400);\n }\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n});\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\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: ${language}.\n\n## Original Briefing\n${briefing}\n\n## Your First Draft\n${first_draft}\n\n## QA Critique\n${critique}`;\n return services.callOpenAI(config.prompts.REFINER_AGENT, userContent, {\n c,\n stream: true,\n });\n});\n\nexport default app.fetch; \"rou","// @ts-nocheck\n// Taskflow AI - Professional Autonomous Agent Factory\n// A modern, user-friendly platform for AI-powered task execution\n\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { streamText } from \"npm:hono@4.4.12/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n industry?: string;\n}\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\n}\ninterface GatherBody {\n task: string;\n domain: string;\n history: ConversationHistory[];\n}\ninterface DynamicPromptBody {\n occupation_title: string;\n task: string;\n company_context: CompanyContext;\n}\ninterface ExecuteBody {\n refined_prompt: string;\n user_inputs: Record;\n company_context: CompanyContext;\n category: string;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n prompts: {\n INFORMATION_GATHERER:\n `You are a native JSON speaking expert information gatherer designed to acquire all necessary details for a given task and domain. 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.\n\n# RULES\n1. **Primary Goal**: Ask single, concise questions to gather the minimum required information for the task, aiming for completion efficiently.\n2. **Decision Point**: After analyzing the conversation history, decide if you have enough information.\n - **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\"}\n - **If NO**: You are missing critical information. Proceed to the next rule.\n3. **Question Formulation**: If more info is needed, formulate the single most impactful and concise question.\n4. **Output Format (Questions)**: The question must be a short, direct interrogative. DO NOT add conversational filler, commentary, apologies, or instructions.\n\n# CONTEXT\n- Task: {{task}}\n- Domain: {{domain}}\n- Conversation History:\n{{history}}`,\n PROMPT_REFINER:\n `You are a native JSON speaking prompt engineer. Transmute raw occupational data into a concise, forceful directive for a target AI. The AI must perform the task end-to-end and return only the finished deliverable(s)—never a plan. You will receive an \"Occupation,\" \"Task,\" and \"Company Context.\" Your prompt must be a production-grade instruction that directly yields the final artifact. CORE RULE: You MUST return a prompt with {{variables}}. Your goal is to identify important, undefined entities in the Task and convert them to descriptive, {{snake_case_placeholders}}. You MUST include at least 2 placeholders. INSTRUCTIONS: 1. Persona: Start with \"You are a top 0.1% [Occupation]...\" for an expert tone. 2. Objective: Translate the Task into a formal, actionable objective implying completion. 3. Context: Use the 'Company Context' to make the objective specific (e.g., \"Your mandate for [Company Name] is to...\"). 4. Placeholders: Convert only USER-SUPPLIED functional inputs into {{snake_case_variables}}. 5. Delivery Rules: Require the AI to: - Return only the finished deliverable(s) now. - Exclude plans, methods, or explanations. - Avoid meta-commentary, caveats, or apologies. - If details are missing, make minimal, reasonable assumptions and proceed, optionally adding a short \"Assumptions\" list (<=5 bullets). - Use production-ready formatting (e.g., ready-to-send copy; runnable code in fenced blocks). - Never include sections like \"Plan\", \"Approach\", \"Methodology\". 6. Output Format: Specify the exact output structure. 7. Mandate: Conclude with a clear call to action like \"Produce the deliverable now.\" Your entire response MUST be a single, minified JSON object with one key, \"refined_prompt\". No other text or markdown.`,\n MASTER_PROMPT_ENGINEER:\n `You are a native JSON speaking master prompt architect. Your objective is to construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, not to do it yourself.\n\n# INSTRUCTIONS\n1. **Deconstruct Task:** Analyze the task to identify sub-tasks, knowledge domains, constraints, and desired output formats.\n2. **Establish Persona:** Define an expert persona for the target AI (e.g., \"You are a tenured professor of economics...\").\n3. **Provide Context:** Synthesize company context into a rich narrative explaining the task's strategic importance.\n4. **Formulate Logic:** Create a clear, logical sequence of operations using headings and lists.\n5. **Set Negative Constraints:** Explicitly state what the AI should *not* do (e.g., \"Do not use informal language,\" \"Do not output a plan\").\n6. **Define Output Schema:** Meticulously define the final output's structure, format, length, and tone.\n7. **Include Examples:** Where helpful, provide concise examples of good and bad outputs.\n8. **Final Mandate:** Conclude with a clear command to initiate execution.\n\nYour output must be the prompt itself, as a clean markdown document. Do not wrap it in JSON or add commentary.`,\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.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the provided company context (name, description, use case) to understand their core business.\n2. **Generate Industries**: Produce a list of 10-12 diverse yet highly relevant industries.\n3. **Output Format**: Your entire response MUST be a single, minified JSON object with one key: \"industries\". The value must be an array of strings.\n\n# COMPANY 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. You will receive a JSON object with company context and the list type to generate.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the 'company_context' to understand their business.\n2. **Generate List**: Create a list of 10-15 relevant items based on 'generation_type'.\n - If 'generation_type' is \"occupations\", generate specific job titles within the 'industry'.\n - If 'generation_type' is \"tasks\", generate specific, actionable tasks for the 'occupation'.\n3. **Output Format**: Your response MUST be a single, minified JSON object with one key: \"generated_list\".\n4. **List Structure**: The value must be an array of objects, each with \"name\" (string) and \"description\" (concise string).\n5. **Relevance is Key**: Items must be sharply focused and directly applicable to the company's use case.\n6. **Input is JSON**: You must parse the JSON input to get the context.`,\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:\n1. **Score:** Score the output 1-10 on each criterion.\n2. **Critique:** Provide a concise, professional critique of its strengths and weaknesses.\n# SCORING RULES\n1. **Gut 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.\"\n2. **Penalize Non-Deliverables:** If the output is a plan or commentary instead of the final deliverable, penalize heavily (scores <= 3) on the relevant criterion.\n# OUTPUT 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.\n\n# RULES\n1. **Analyze and Integrate:** Apply all suggested changes from the critique to the first draft.\n2. **No Commentary:** Do not apologize, explain changes, or include meta commentary.\n3. **Final Deliverable Only:** Your output must be only the new, improved, final version.\n4. **Language:** 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.\n# RULES\n1. **Analyze Task:** Deeply analyze the task briefing to understand the desired output's purpose and requirements.\n2. **Generate Criteria:** Create 4-6 specific, measurable, relevant success criteria as short strings.\n3. **Output Format:** Your response MUST be a single, minified JSON object: {\"criteria\": [\"...\", \"...\", ...]}.\n\n# TASK BRIEFING\n{{task_briefing}}`,\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: \"Create detailed, expert-level prompts for AI systems.\",\n icon: \"🛠️\",\n },\n {\n id: \"document_creation\",\n name: \"Professional Documents\",\n description:\n \"Produce polished, ready-to-use documents with quality refinement.\",\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\": \"es-ES\",\n \"French\": \"fr-FR\",\n \"German\": \"de-DE\",\n \"Japanese\": \"ja-JP\",\n \"Chinese (Simplified)\": \"zh-CN\",\n };\n const languageOptions = Object.entries(languages).map(([name, code]) =>\n ``\n ).join(\"\");\n const taskCategoriesJson = JSON.stringify(taskCategories);\n return `Taskflow AI | Intelligent Task Automation

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