[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","volt","name","hatter","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_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":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_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":55},"id","c5f2fefe-a3ea-11f0-845a-0224a6c84d84","main.ts","fileId","6bc66a40-a3bf-11f0-b730-0224a6c84d84","parentId","branchId","6bc3fa30-a3bf-11f0-b730-0224a6c84d84","branchVersion",10,"latest",true,"createdAt",["D",1759888918701],9,"fileType","http","content","// @ts-nocheck\n// This is a comprehensive, single-file Hono application for \"Hatter\".\n// It includes the \"Co-Pilot\" extension for natural language task planning.\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/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n}\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\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 CO_PILOT_PLANNER:\n `You are a native JSON speaking AI expert at operational planning and task delegation, acting as the \"Hatter Co-Pilot\". Your primary function is to analyze a user's high-level goal and map it to a precise execution plan by selecting the best-fit specialist from a provided list.\n\n# RULES\n1. **Analyze Goal**: Deeply understand the user's stated 'goal'.\n2. **Review Specialists**: Examine the provided 'available_specialists' JSON, which lists industries and the occupations within them.\n3. **Select Best Fit**: Determine the single most appropriate 'industry' and 'occupation' from the list to handle the user's goal.\n4. **Define the Task**: Formulate a concise, action-oriented 'task_name' that accurately describes what the selected specialist needs to do. This task name should be a clear instruction. (e.g., \"Draft a Partnership Announcement Email\", \"Develop a Q3 Content Strategy Outline\").\n5. **Output Format**: Your entire response MUST be a single, minified JSON object with three keys: \"industry\", \"occupation\", and \"task_name\".\n\n# INPUT\n- User Goal: {{user_goal}}\n- Available Specialists: {{available_specialists}}`,\n VARIABLE_ASSUMER:\n `You are a native JSON speaking AI expert at making logical assumptions. Your task is to analyze a prompt directive that contains {{placeholders}} and, based on the provided company and task context, generate a JSON object with plausible, assumed values for every placeholder.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the provided 'company_context', 'task', and the 'refined_prompt' to understand the goal.\n2. **Identify Placeholders**: Extract all variable names from the {{placeholders}} in the 'refined_prompt'.\n3. **Generate Plausible Values**: For each placeholder, create a realistic and contextually appropriate value. For example, if the placeholder is {{quarterly_metric}} for a sales-focused SaaS company, a good value would be \"customer retention rate\".\n4. **Output Format**: Your entire response MUST be a single, minified JSON object.\n5. **JSON Keys**: The keys of the JSON object MUST exactly match the variable names found within the curly braces (e.g., for {{quarterly_metric}}, the key must be \"quarterly_metric\").\n\n# INPUT CONTEXT\n- Company: {{company_context}}\n- Task: {{task}}\n- Prompt with Placeholders:\n{{refined_prompt}}`,\n PROMPT_REFINER:\n `You are a native JSON speaking specialized prompt engineer. Your function is to transmute raw occupational data into a concise, forceful directive for a target AI that performs the task end-to-end and returns the finished deliverable(s) only—never a plan or instructions. You will be given an \"Occupation,\" \"Task,\" and \"Company Context.\" Your generated prompt must be a production-grade instruction set that directly yields the final artifact. CORE RULE: You MUST return a prompt that has {{variables}} in it. Your primary goal is to identify the most important, undefined entities within the user's Task description and convert them into descriptive, double-curly-braced {{placeholders}}. You MUST include at least 2 placeholder variables. INSTRUCTIONS: 1. Establish Persona: Begin with \"You are a top 0.1% [Occupation]...\" to set an expert executor tone. 2. Define Objective: Translate the given Task into a formal, actionable objective that implies completion (not planning). 3. Integrate Company Context: Use the provided 'Company Context' to make the objective specific and relevant (e.g., \"Your primary mandate for [Company Name] is to...\"). 4. Insert Placeholders: Convert only USER SUPPLIED functional inputs into {{snake_case_variables}}. Do not invent extras. 5. Delivery Rules: Explicitly require the AI to: - Perform the task now and return only the finished deliverable(s). - Exclude plans, approaches, roadmaps, step-by-step methods, or methodology explanations. - Avoid meta commentary, caveats, or apologies. - If minor details are missing, make minimal, reasonable assumptions; optionally include a short \"Assumptions\" list (<=5 bullets) and proceed. - Use production-ready formatting appropriate to the task (e.g., ready-to-send copy; runnable code in fenced blocks with filenames; clean tables). - Do not include sections titled \"Plan\", \"Approach\", \"Methodology\", \"Steps\", or similar. 6. Output Format: Specify exactly what to output (sections, files, or structures) appropriate to the task. 7. Mandate a Starting Point: Conclude with a clear call to action like \"Produce the deliverable now.\" Your entire response MUST be a single, minified JSON object containing exactly one key, \"refined_prompt\". Do not include any other text, explanations, or markdown formatting.`,\n MASTER_PROMPT_ENGINEER:\n `You are a native JSON speaking master prompt architect, a meta-engineer responsible for creating exceptionally detailed, multi-page-equivalent prompts for other AI systems. Your objective is not to complete the task yourself, but to construct a comprehensive blueprint for another AI to execute flawlessly. The prompt you generate must be a self-contained, exhaustive directive.\n\n# INSTRUCTIONS\n1. **Deconstruct the Task:** Analyze the provided task and identify all explicit and implicit sub-tasks, required knowledge domains, constraints, and desired output formats.\n2. **Establish a Persona:** Define an expert persona for the target AI (e.g., \"You are a tenured professor of economics at an Ivy League institution...\").\n3. **Provide Deep Context:** Synthesize the company context into a rich narrative for the AI, explaining the strategic importance of the task.\n4. **Formulate Step-by-Step Logic:** Create a clear, logical sequence of operations for the AI to follow. Use headings, bullet points, and numbered lists to structure the thinking process.\n5. **Specify Negative Constraints:** Explicitly state what the AI should *not* do (e.g., \"Do not use informal language,\" \"Avoid referencing post-2023 data,\" \"Do not output a plan, only the final deliverable\").\n6. **Define the Output Schema:** Provide a meticulous definition of the final output's structure, including formatting, length, tone, and any required data schemas (e.g., JSON structure, Markdown tables).\n7. **Include Examples:** Where ambiguity may exist, provide concise examples of both good and bad outputs to guide the AI.\n8. **Final Mandate:** Conclude with a clear, final command to initiate execution.\n\nYour output must be the prompt itself, formatted as a clean, professional markdown document. Do not surround it in a JSON object or add any commentary.`,\n INDUSTRY_GENERATOR:\n `You are a native JSON speaking expert business analyst AI. Your task is to generate a list of professional industries that are most relevant to a given company.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the provided company context (name, description, use case) to understand their core business, target market, and operational domain.\n2. **Generate Industries**: Produce a list of 10-12 diverse yet highly relevant industries where the company's activities or products would fit. Cast a wide but focused net. For example, for a B2B SaaS for sales teams, relevant industries could include \"Marketing, Sales, and Service\", \"Information Technology\", \"Financial Services\", \"Retail Technology\", etc.\n3. **Output Format**: Your entire response MUST be a single, minified JSON object containing one key: \"industries\".\n4. **Value Format**: The value of \"industries\" 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 containing the company's context and the type of list to generate.\n\n# RULES\n1. **Analyze Context**: Deeply analyze the 'company_context' object to understand their business and objectives.\n2. **Generate List**: Create a list of 10-15 highly relevant items based on the 'generation_type'.\n - If 'generation_type' is \"occupations\", generate specific job titles within the given 'industry'.\n - If 'generation_type' is \"tasks\", generate specific, actionable tasks that the given 'occupation' would perform for the company.\n3. **Output Format**: Your entire response MUST be a single, minified JSON object. This object must contain exactly one key: \"generated_list\".\n4. **List Structure**: The value of \"generated_list\" must be an array of objects. Each object must have two keys: \"name\" (a string) and \"description\" (a concise, one-sentence string).\n5. **Relevance is Key**: The generated items must be sharply focused and directly applicable to the company's described business and use case. Avoid generic or broad items.\n6. **Input is JSON**: The user input will be a JSON object. You must parse it to get the context.`,\n EVALUATOR_AGENT:\n `You are a native JSON speaking meticulous and impartial AI Quality Assurance agent. You will be given an AI's task briefing, a list of success criteria, and the raw output from the AI.\n# YOUR TASKS\n1. **Score:** Score the output from 1-10 on each criterion.\n2. **Critique:** Provide a concise, professional critique summarizing the output's strengths and weaknesses.\n# SCORING RULES\n1. **Gut Check (Common Sense):** Before any other check, do a gut check. If the text contains obvious placeholders (e.g., 'example.com', '[Insert Name Here]', 555 phone numbers) or is extremely generic and templated, penalize heavily on \"Plausibility and Specificity\" or similar criteria. This is your primary filter for low-quality output.\n2. **Penalize Non-Deliverables:** If the output contains plans 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 with keys \"scores\" (an array of objects with \"criterion\" and \"score\") and \"critique\". You MUST write your critique in the requested language. Do not include any other text, explanations, or markdown formatting.`,\n REFINER_AGENT:\n `You are a top-tier expert, a \"Refiner\" agent. You have been provided with your original task briefing, your first draft attempt, and a detailed critique from a Quality Assurance agent. Your SOLE mission is to rewrite your first draft to fully address every point in the critique, elevating the work to a 10/10 standard.\n\n# RULES\n1. **Analyze and Integrate:** Carefully analyze the critique and apply all suggested changes to the first draft.\n2. **No Commentary:** Do not write an apology, explain your changes, or include any meta commentary.\n3. **Final Deliverable Only:** Your entire output must be only the new, improved, and final version of the deliverable. Do not include titles like \"Refined Version\".\n4. **Language:** Write the final output in the requested language.`,\n SUCCESS_CRITERIA_GENERATOR:\n `You are a native JSON speaking expert Quality Assurance analyst. Your task is to generate a concise list of success criteria for evaluating an AI-generated document.\n# RULES\n1. **Analyze the Task:** Deeply analyze the provided task briefing to understand the desired output's purpose, format, and key requirements.\n2. **Generate Criteria:** Create a list of 4-6 specific, measurable, and relevant success criteria. Each criterion should be a short string.\n3. **Output Format:** Your entire response MUST be a single, minified JSON object with one key: \"criteria\". The value must be an array of strings.\n\n# TASK BRIEFING\n{{task_briefing}}`,\n },\n};\n\n// --- STATIC DATA ---\nconst taskCategories = [\n {\n id: \"thinking\",\n name: \"Strategic Analysis\",\n description: \"Generate strategies, outlines, or brainstorming notes.\",\n icon: \"🧠\",\n },\n {\n id: \"prompt_building\",\n name: \"Directive Engineering\",\n description: \"Create a detailed, expert-level prompt for another AI.\",\n icon: \"🛠️\",\n },\n {\n id: \"document_creation\",\n name: \"Deliverable Creation\",\n description:\n \"Produce a finished, formatted document like a report, email, or article.\",\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; // Hono context for streaming\n } = {},\n ) {\n const {\n model = config.models.major,\n isJson = false,\n stream = false,\n c = null,\n } = options;\n\n const openai = new OpenAI();\n const messages: any[] = [\n { role: \"system\", content: systemPrompt },\n {\n role: \"user\",\n content: typeof userContent === \"string\"\n ? userContent\n : JSON.stringify(userContent),\n },\n ];\n\n const requestPayload: any = { model, messages, stream };\n if (isJson) requestPayload.response_format = { type: \"json_object\" };\n\n try {\n const completion = await openai.chat.completions.create(requestPayload);\n\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\n const taskCategoriesJson = JSON.stringify(taskCategories);\n\n return `\n\n\n \n \n Hatter | Autonomous AI Specialist Factory\n \n \n \n \n \n\n\n\n\n\n

\n \n \n\n

\n \n

Hatter

\n\n\n\n`;\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/co-pilot/plan\", async (c: Context) => {\n const { goal, company_context } = await c.req.json();\n if (!goal || !company_context) {\n return c.json({ error: \"Goal and company context are required\" }, 400);\n }\n\n try {\n // Step 1: Fetch all available industries (leverages existing logic)\n const industriesRes = await services.callOpenAI(\n config.prompts.INDUSTRY_GENERATOR,\n `Company Context: ${JSON.stringify(company_context)}`,\n { model: config.models.minor, isJson: true },\n );\n const industries =\n JSON.parse(industriesRes.choices[0].message.content).industries;\n\n // Step 2: Fetch occupations for each industry (leverages existing logic)\n let available_specialists = {};\n for (const industry of industries.slice(0, 5)) { // Limit to top 5 for speed\n const occupationsRes = await services.callOpenAI(\n config.prompts.DYNAMIC_LIST_GENERATOR,\n { generation_type: \"occupations\", company_context, industry },\n { model: config.models.major, isJson: true },\n );\n const occupations =\n JSON.parse(occupationsRes.choices[0].message.content).generated_list;\n available_specialists[industry] = occupations.map((o) => o.name);\n }\n\n // Step 3: Call the new Planner Agent\n const plannerPrompt = config.prompts.CO_PILOT_PLANNER\n .replace(\"{{user_goal}}\", goal)\n .replace(\n \"{{available_specialists}}\",\n JSON.stringify(available_specialists, null, 2),\n );\n\n return services.callOpenAI(\n \"You are a helpful assistant following strict JSON output rules.\",\n plannerPrompt,\n { c, isJson: true, model: config.models.major },\n );\n } catch (e) {\n console.error(\"Co-Pilot planning failed:\", e);\n return c.json(\n { error: `Co-Pilot failed to generate a plan. ${e.message}` },\n 500,\n );\n }\n});\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(\n config.prompts.INDUSTRY_GENERATOR,\n userContent,\n {\n c,\n isJson: true,\n model: config.models.minor,\n },\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\n return services.callOpenAI(\n config.prompts.DYNAMIC_LIST_GENERATOR,\n payload,\n { c, 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: ${task}`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\napp.post(\"/api/assume-variables\", async (c: Context) => {\n const { refined_prompt, task, company_context } = await c.req.json();\n if (!refined_prompt || !task || !company_context) {\n return c.json({ error: \"Required fields missing for assumption\" }, 400);\n }\n\n const prompt = config.prompts.VARIABLE_ASSUMER\n .replace(\"{{company_context}}\", JSON.stringify(company_context))\n .replace(\"{{task}}\", task)\n .replace(\"{{refined_prompt}}\", refined_prompt);\n\n return services.callOpenAI(\n \"You are a helpful assistant following strict JSON output rules.\",\n prompt,\n { c, isJson: true, model: config.models.minor },\n );\n});\n\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const {\n refined_prompt,\n user_inputs,\n company_context,\n category,\n } = await c.req.json();\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n let promptWithVariables = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n promptWithVariables = promptWithVariables.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n promptWithVariables = promptWithVariables.replace(\n /\\{\\{.*?\\}\\}/g,\n \"[Information not provided]\",\n );\n\n let systemPrompt = \"\";\n let userPrompt = \"\";\n\n switch (category) {\n case \"thinking\":\n case \"document_creation\":\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: ${company_context.language}\\n---\\n# YOUR TASK BRIEFING\\n${promptWithVariables}\\nYou MUST write your entire response in the requested language: ${company_context.language}.`;\n userPrompt = \"Proceed with the task.\";\n break;\n\n case \"prompt_building\":\n systemPrompt = config.prompts.MASTER_PROMPT_ENGINEER;\n userPrompt =\n `Generate a master prompt for the following task, considering the company's context.\\n\\nTask: \"${promptWithVariables}\"\\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\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\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\n const userContent = {\n task_briefing: briefing,\n success_criteria: success_criteria,\n raw_output: first_draft,\n };\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. 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Welcome to Hatter

Code