[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_166":167,"_168":169},"routes/_app.x.$handle.$name",{"_98":97,"_32":44,"_106":84,"_27":107,"_13":14,"_108":-5,"_11":109,"_110":111,"_112":113,"_114":115,"_116":117,"_20":-5,"_118":119,"_120":121,"_122":25,"_123":124,"_125":56,"_126":-5,"_127":-5,"_128":129},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_42":43,"_32":44,"_37":45,"_46":-5,"_47":25,"_48":49,"_50":49,"_51":-5,"_52":-5,"_18":53,"_54":31,"_55":56,"_57":56,"_58":-5,"_59":60,"_61":62,"_63":-5,"_64":-5,"_65":-5,"_66":-5,"_67":68,"_69":68,"_70":71,"_72":71,"_73":71,"_74":68,"_75":68,"_76":77,"_78":79,"_80":81,"_82":25},"version",3,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":36,"_37":38,"_39":14,"_40":41,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","0198674f-f040-7448-82ec-87872adb4646","name","fileType","http","content","// This is a Val Town (https://val.town) HTTP Val.\n// It uses the Hono web framework to serve a Single-Page Application (SPA).\n\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@4.4.12\";\nimport type { Context } from \"npm:hono@4.4.12\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n}\ninterface GenerateOccupationsBody {\n industry: string;\n company_context?: CompanyContext;\n}\ninterface GenerateTasksBody {\n occupation: string;\n company_context?: CompanyContext;\n}\ninterface DynamicPromptBody {\n occupation_title: string;\n task: string;\n company_context?: CompanyContext;\n}\ninterface InputsBody {\n refined_prompt: string;\n}\ninterface ClarifyBody {\n refined_prompt: string;\n}\ninterface ExecuteBody {\n refined_prompt: string;\n user_inputs: Record;\n clarifications: Record;\n company_context?: CompanyContext;\n}\n\n// --- STATIC DATA & PROMPTS ---\nconst industries: string[] = [\n \"Marketing, Sales, and Service\",\n \"Arts, A/V Technology & Communications\",\n \"Business Management & Administration\",\n \"Education & Training\",\n \"Finance\",\n \"Health Science\",\n \"Information Technology\",\n \"Law & Public Safety\",\n \"Scientific Research\",\n \"Software Development\",\n \"Agriculture, Food, & Natural Resources\",\n \"Architecture & Construction\",\n \"Energy & Utilities\",\n \"Government & Public Administration\",\n \"Hospitality & Tourism\",\n \"Human Services\",\n \"Manufacturing\",\n \"Transportation, Distribution & Logistics\",\n \"Real Estate\",\n \"Retail & E-commerce\",\n \"Media & Entertainment\",\n \"Telecommunications\",\n \"Pharmaceuticals & Biotechnology\",\n \"Non-Profit & Philanthropy\",\n \"Aerospace & Defense\",\n \"Automotive\",\n \"Insurance\",\n \"Environmental Services\",\n \"Fashion & Apparel\",\n \"Sports & Recreation\",\n];\n\nconst PROMPT_REFINER_SYSTEM_PROMPT =\n `What would a top 0.1% person in this field think. You operate as a specialized top 0.1% prompt engineer to generate {{variable}} meta prompts. Your function is to transmute raw, structured occupational data into an elaborate, efficacious, and long-form directive for a target AI. You will be given an \"Occupation,\" \"Task,\" ancillary data, and importantly, \"Company Context.\" Your generated prompts must be comprehensive, detailed, and structured as a professional brief, suitable for guiding an advanced AI agent. # Core Rule This is the most critical mandate of your function. YOU MUST RETURN A PROMPT THAT HAS {{variables}} IN IT!! Your purpose is not to create a static prompt, but a reusable **template**. Your primary goal is to identify the most important, undefined, or abstract entities and concepts within the user's \\`Task\\` description and convert them all into descriptive, double-curly-braced {{placeholders}} or {{variables}}. You MUST include at least 2 placeholder variables in your response. For example, if the task is \"Analyze data on market conditions to identify potential sales of a product or service,\" the key undefined entity is \"a product or service,\" which you MUST turn into a placeholder like \\`{{product_or_service}}\\`. Other examples include specific documents (\"a report\" -> \\`{{report_topic}}\\`), timeframes (\"in the coming quarter\" -> \\`{{target_quarter}}\\`), or clients (\"for the client\" -> \\`{{client_name}}\\`). A generated prompt template that fails to create appropriate \\`{{placeholders}}\\` for all such undefined entities is considered a failure. You must always create at least one. # Instructions 1. **Establish Persona:** Begin the generated prompt by instantiating the top 0.1% persona just like yours. The 'Occupation' is your foundation. State it clearly and authoritatively: \"You are a top 0.1% [Occupation]...\". You should consider the implicit seniority and core responsibilities of this role in the tone of the prompt. 2. **Define Objective:** Translate the given \\`Task\\` from a simple description into a formal, actionable objective. Frame it as a direct and primary mandate for the AI persona, explaining its importance or impact. 3. **Integrate Company Context**: Use the provided 'Company Context' (company name, description, and use case) to make the objective more specific and relevant. For example, instead of a generic objective, frame it as \"Your primary mandate for [Company Name] is to...\". 4. **Insert Placeholders:** Scrutinize the objective for abstract or undefined nouns. Every such entity must be converted into a \\`{{variable}}\\`. Name these placeholders logically and descriptively, preferably using snake_case (e.g., \\`{{specific_product_line}}\\`, \\`{{quarterly_earnings_report}}\\`). 5. **Mandate a Starting Point:** Conclude the generated prompt with a clear \"call to action\" that directs the target AI on how to begin its response, leaving no ambiguity about the first step. # Quality Rubric - **Contextual Relevance:** The prompt must seamlessly weave in the company's context, making the task feel authentic to their business. - **Detail & Length:** The final prompt must be expansive, typically multiple paragraphs long, providing a rich operational context. It should feel like a comprehensive professional briefing. - **Persona Authenticity:** The tone must be professional, authoritative, and believable for the specified role. - **Template Utility:** The prompt must contain logical placeholders, making it a truly reusable template for future tasks. # Output Your entire response MUST be a single, minified JSON object containing exactly one key, \\`refined_prompt\\`. Do not include any other text or markdown wrappers. Your response must begin with \\`{\\` and end with \\`}\\`.`;\nconst INPUT_EXTRACTOR_SYSTEM_PROMPT =\n `You are a top 0.1% expert AI that serves as a strict parser. Your sole function is to analyze a prompt template, extract all placeholders, and generate a JSON form schema based on a strict set of rules. # Core Rules This is the most critical mandate of your function. You must follow these rules precisely. ## Rule 1: Required Fields from Placeholders ONLY - Your primary task is to find all placeholders formatted as \\`{{variable_name}}\\` in the provided prompt. - Each placeholder you find, and ONLY these placeholders, MUST be turned into a form field object. - Every field object generated from a placeholder MUST have \\`\"required\": true\\`. - **CRITICAL:** Do NOT invent or infer any other required fields. If it's not a \\`{{variable_name}}\\` in the prompt, it cannot be a required field. ## Rule 2: Optional, High-Value Fields - After processing all placeholders, you may add one or two **optional** fields. - These optional fields are for expert-level clarifying questions that would produce a significantly better result. - Every optional field you add MUST have \\`\"required\": false\\`. ## Rule 3: Contextual Hints - For every field of type \\`text\\` or \\`textarea\\`, you MUST add a \\`placeholder\\` key. - The value for this key must be a concise, contextually-aware example of what a user might enter, inferred from the prompt's context. (e.g., for a \\`{{target_audience}}\\` field, \\`\"e.g., Environmentally conscious millennials, outdoor enthusiasts\"\\`). - Generic hints like \\`\"Enter value here\"\\` are unacceptable. ## Rule 4: Final Output Specification - Your entire response MUST be a single, minified JSON object. - The JSON object must have one key, \\`\"inputs\"\\`, which contains an array of the field objects you have generated. - Do not include any other text, explanations, or markdown wrappers. Your response must begin with \\`{\\` and end with \\`}\\`. # Example ## Input Prompt: \"You are a Graphic Designer. Create a logo for a new coffee shop named {{company_name}} that targets urban professionals.\" ## Required JSON Output: {\"inputs\":[{\"name\":\"company_name\",\"label\":\"Company Name\",\"type\":\"text\",\"required\":true,\"placeholder\":\"e.g., Metropolis Coffee Roasters\"},{\"name\":\"brand_style\",\"label\":\"Brand Style\",\"type\":\"select\",\"required\":false,\"options\":[{\"label\":\"Urban Minimalist\",\"value\":\"urban_minimalist\"},{\"label\":\"Artisanal & Rustic\",\"value\":\"artisanal_rustic\"},{\"label\":\"Luxury & Elegant\",\"value\":\"luxury_elegant\"}]},{\"name\":\"slogan\",\"label\":\"Slogan to include (if any)\",\"type\":\"text\",\"required\":false,\"placeholder\":\"e.g., Your Daily Fuel\"}]} `;\n// ⭐️ NEW PROMPT for the Clarification Agent\nconst CLARIFICATION_AGENT_SYSTEM_PROMPT =\n `You are a world-class strategic consultant and clarification agent. Your sole function is to read a detailed task briefing intended for an AI agent and generate a short list of highly insightful, open-ended questions. These questions are designed to elicit more nuanced, high-resolution context from the human user, leading to a vastly superior final output from the AI. # Core Rules 1. **Analyze the Briefing**: You will be given a complete task briefing. Your analysis should go beyond the obvious placeholders (like \\`{{product_name}}\\`). You need to identify the implicit assumptions, potential ambiguities, and strategic levers within the task. 2. **Generate Insightful Questions**: Based on your analysis, create 2 to 4 open-ended questions. These are not for filling in simple variables. They are for understanding the \"why\" behind the task. - **Good Example Questions**: \"What is the primary emotional response we want to evoke in the target audience?\", \"Are there any brand voice constraints or mandatory phrases to include/avoid?\", \"What does a 'successful' outcome for this task look like in 3 months?\", \"Who are our top 2 competitors in this specific space, and what is our key differentiator against them?\" - **Bad Example Questions**: \"What is the product name?\", \"What is the deadline?\" (These are simple variable extractions, not your job). 3. **Maintain Role and Purpose**: Remember, you are asking these questions to help another AI do its job better. The questions should be practical and directly useful for shaping the final deliverable. 4. **JSON Output Format**: Your entire response MUST be a single, minified JSON object. - The object must have one key: \\`\"clarification_questions\"\\`. - The value must be an array of form field objects. - Each object in the array must have the following keys: - \\`\"name\"\\`: A snake_case version of the question (e.g., \\`strategic_goal\\`). - \\`\"label\"\\`: The full question text for the user. - \\`\"type\"\\`: This should always be \\`\"textarea\"\\`. - \\`\"required\"\\`: This should always be \\`false\\`. - \\`\"placeholder\"\\`: A concise, helpful example of a good answer (e.g., \"e.g., To establish our brand as a thought leader in sustainable tech and drive sign-ups for our webinar.\"). # Example ## Input Briefing: \"You are a top 0.1% Content Strategist for InnovateHealth... Your primary mandate is to develop a comprehensive content strategy for our new feature, the 'AI-Powered Meal Planner', targeting {{target_audience}}...\" ## Required JSON Output: {\"clarification_questions\":[{\"name\":\"success_metric\",\"label\":\"What is the single most important metric for success for this content strategy?\",\"type\":\"textarea\",\"required\":false,\"placeholder\":\"e.g., Driving 5,000 new user sign-ups for the meal planner feature within the first month.\"},{\"name\":\"brand_voice_constraints\",\"label\":\"Are there any specific brand voice tones or keywords we must include or strictly avoid?\",\"type\":\"textarea\",\"required\":false,\"placeholder\":\"e.g., Avoid clinical jargon. Emphasize a friendly, encouraging, and data-driven tone. Must include the phrase 'Your Personalized Wellness Journey'.\"}]} `;\nconst DYNAMIC_LIST_GENERATOR_PROMPT =\n `What would a top 0.1% person in this field think. You are a top 0.1% industry analyst and career taxonomist. Your function is to generate a list of specific items based on a user-provided type ('occupations' or 'tasks') and **company context**. # Core Rule The list you generate MUST reflect deep domain expertise. You are to AVOID generic, common, or obvious titles. Instead, provide nuanced, specialized results that truly exist within the specified field. # Company Context Integration You will receive context about the user's company (name, description, use case). You MUST leverage this information to make your generated list significantly more relevant to the company's specific situation. # Instructions & Examples ## 1. If the input \\`type\\` is \"occupations\": Your goal is to generate **job titles**. These should be specific roles relevant to the given \\`industry\\` and company context. ### Occupation Example Input: {\"type\": \"occupations\", \"industry\": \"Marketing\", \"company_context\": {\"company_name\": \"InnovateHealth\", \"company_description\": \"A startup building a mobile app for personalized fitness plans using AI.\", \"use_case\": \"Acquire new users for our mobile app.\"}} ### Required Occupation Output: {\"generated_list\":[{\"id\":1,\"name\":\"Growth Marketing Manager (Mobile Apps)\",\"description\":\"Develops and executes strategies to drive user acquisition and engagement for mobile applications.\"},{\"id\":2,\"name\":\"Content Strategist (Health & Wellness)\",\"description\":\"Creates and oversees a content marketing plan focused on fitness, nutrition, and wellness topics to attract target users.\"},{\"id\":3,\"name\":\"Influencer Marketing Coordinator\",\"description\":\"Identifies and manages relationships with fitness influencers to promote the mobile app to their audiences.\"}]} ## 2. If the input \\`type\\` is \"tasks\": **This is a critical distinction.** Your goal is to generate specific, actionable **tasks** or **deliverables** that a person in the given \\`occupation\\` would perform. - **DO NOT** list other job titles. A task is a singular unit of work. - The \\`name\\` of each task MUST be an action-oriented phrase, ideally starting with a verb (e.g., \"Analyze competitor data\", \"Draft a press release\", \"Develop a Q3 content calendar\"). - The \\`description\\` should clarify the goal or output of that specific task. ### Task Example Input: {\"type\": \"tasks\", \"occupation\": \"Content Strategist (Health & Wellness)\", \"company_context\": {\"company_name\": \"InnovateHealth\", \"company_description\": \"A startup building a mobile app for personalized fitness plans using AI.\", \"use_case\": \"Acquire new users for our mobile app.\"}} ### Required Task Output: {\"generated_list\":[{\"id\":1,\"name\":\"Develop a quarterly content calendar\",\"description\":\"Plan and schedule blog posts, videos, and social media content for the upcoming quarter focusing on user acquisition.\"},{\"id\":2,\"name\":\"Conduct keyword research for wellness topics\",\"description\":\"Identify high-value SEO keywords related to fitness, nutrition, and mental well-being to target in new content.\"},{\"id\":3,\"name\":\"Write a long-form guide on 'AI in Personal Fitness'\",\"description\":\"Create a comprehensive, authoritative article to establish thought leadership and attract organic traffic.\"}]} # Final Output Format Your entire response MUST be a single, minified JSON object. The object must contain exactly one key, \\`generated_list\\`. The value must be an array of objects, where each object has an \\`id\\` (sequential number), a \\`name\\` (the title), and a \\`description\\` (the one-sentence summary).`;\nconst HTML_FORMATTER_SYSTEM_PROMPT =\n `You are an expert in technical communication and semantic HTML. Your sole responsibility is to convert a given raw text document into a clean, well-structured, and semantically correct HTML snippet. # Core Mandates: 1. **Valid & Semantic HTML:** The output MUST be valid HTML using appropriate tags. Use

for the main title,

for major sections,

for paragraphs,

for quoted text, for emphasis, etc. 2. **No Full Document:** Do NOT include \\`\\`, \\`\\`, or \\`\\` tags. Your response must be an HTML snippet that can be directly injected into a \\`
\\` on an existing page. 3. **No Styling:** Do NOT include any CSS, \\`\n\n\n\n
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Welcome to A.I. Shine
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First, tell us about your context. This helps the AI create highly relevant agents for you.
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Select Your Domain
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1. Industry
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\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n// 1. Serve the frontend application\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// 2. Get the initial list of industries\napp.get(\"/api/industries\", (c: Context) => c.json(industries));\n\n// 3. Generate occupations for an industry\napp.post(\"/api/generate/occupations\", async (c: Context) => {\n const { industry, company_context } = await c.req.json();\n if (!industry) return c.json({ error: \"Industry is a required field.\" }, 400);\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: DYNAMIC_LIST_GENERATOR_PROMPT }, {\n role: \"user\",\n content: JSON.stringify({ type: \"occupations\", industry, company_context }),\n }],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n console.error(\"Error in /api/generate/occupations:\", e);\n return c.json({ error: \"AI failed to generate a valid list.\" }, 500);\n }\n});\n\n// 4. Generate tasks for an occupation\napp.post(\"/api/generate/tasks\", async (c: Context) => {\n const { occupation, company_context } = await c.req.json();\n if (!occupation) return c.json({ error: \"Occupation is a required field.\" }, 400);\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: DYNAMIC_LIST_GENERATOR_PROMPT }, {\n role: \"user\",\n content: JSON.stringify({ type: \"tasks\", occupation, company_context }),\n }],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n console.error(\"Error in /api/generate/tasks:\", e);\n return c.json({ error: \"AI failed to generate a valid list.\" }, 500);\n }\n});\n\n// 5. Generate a refined agent prompt\napp.post(\"/api/prompt/dynamic\", async (c: Context) => {\n const { occupation_title, task, company_context } = await c.req.json();\n if (!occupation_title || !task) return c.json({ error: \"occupation_title and task are required.\" }, 400);\n\n const userInput = `Company Context: ${\n JSON.stringify(company_context)\n }\\n\\nOccupation: ${occupation_title}\\n\\nTask: ${task}`;\n\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: PROMPT_REFINER_SYSTEM_PROMPT }, { role: \"user\", content: userInput }],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n console.error(\"Error in /api/prompt/dynamic:\", e);\n return c.json({ error: \"AI failed to generate a valid prompt.\" }, 500);\n }\n});\n\n// 6. Extract input fields from the prompt\napp.post(\"/api/inputs\", async (c: Context) => {\n const { refined_prompt } = await c.req.json();\n if (!refined_prompt) return c.json({ error: \"refined_prompt is required\" }, 400);\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: INPUT_EXTRACTOR_SYSTEM_PROMPT }, {\n role: \"user\",\n content: refined_prompt,\n }],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n console.error(\"Error in /api/inputs:\", e);\n return c.json({ error: \"AI failed to extract inputs.\" }, 500);\n }\n});\n\n// ⭐️ 7. NEW: Generate clarification questions from the prompt\napp.post(\"/api/clarify\", async (c: Context) => {\n const { refined_prompt } = await c.req.json();\n if (!refined_prompt) return c.json({ error: \"refined_prompt is required\" }, 400);\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: CLARIFICATION_AGENT_SYSTEM_PROMPT }, {\n role: \"user\",\n content: refined_prompt,\n }],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n console.error(\"Error in /api/clarify:\", e);\n return c.json({ error: \"AI failed to extract clarification questions.\" }, 500);\n }\n});\n\n// 8. Execute the final agent task (UPDATED)\napp.post(\"/api/execute\", async (c: Context) => {\n const { refined_prompt, user_inputs, clarifications, company_context } = await c.req.json();\n if (!refined_prompt || !user_inputs) return c.json({ error: \"refined_prompt and user_inputs are required\" }, 400);\n\n let finalUserPrompt = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n const placeholder = new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\");\n finalUserPrompt = finalUserPrompt.replace(placeholder, String(value));\n }\n\n // Build the strategic guidance section from clarifications\n let strategicGuidance = \"\";\n if (clarifications && Object.keys(clarifications).length > 0) {\n const guidancePoints = Object.entries(clarifications)\n .map(([question, answer]) => `- Q: ${question}\\n - A: ${answer}`)\n .join(\"\\n\");\n strategicGuidance = `\n# USER'S STRATEGIC GUIDANCE\nBefore you begin, consider these additional clarifications and constraints provided by the user. Use this information to guide your approach, tone, and final output.\n${guidancePoints}\n---\n`;\n }\n\n // Prepend the company context and strategic guidance to the final system prompt\n const systemPromptWithContext = `\n# YOUR CONTEXT\nYou are performing this task for the following company. Keep this context in mind for all of your analysis, tone, and final output.\n- Company Name: ${company_context?.company_name}\n- Company Description: ${company_context?.company_description}\n- Primary Goal for this Task: ${company_context?.use_case}\n---\n${strategicGuidance}\n# YOUR TASK BRIEFING\n${finalUserPrompt}\n `;\n\n try {\n const openai = new OpenAI();\n const agentCompletion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: systemPromptWithContext }, {\n role: \"user\",\n content:\n \"Proceed with the task as instructed, keeping my company's context and strategic guidance at the forefront of your response.\",\n }],\n });\n const agentOutput = agentCompletion.choices[0].message.content;\n if (!agentOutput) throw new Error(\"The agent returned no content.\");\n\n const htmlCompletion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: HTML_FORMATTER_SYSTEM_PROMPT }, { role: \"user\", content: agentOutput }],\n });\n const htmlOutput = htmlCompletion.choices[0].message.content;\n return c.json({ output: htmlOutput });\n } catch (e) {\n console.error(\"Error during execution:\", e);\n return c.json({ error: `AI execution failed: ${e.message}` }, 500);\n }\n});\n\n// --- EXPORT THE VAL ---\nexport default 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