[{"_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","promptly","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":-5,"_18":55},"id","5b199296-b245-11f0-8fe5-0224a6c84d84","main.ts","fileId","501ddfb4-b245-11f0-aafc-0224a6c84d84","parentId","branchId","501b87d2-b245-11f0-aafc-0224a6c84d84","branchVersion",4,"latest",true,"createdAt",["D",1761467139909],3,"fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { Hono } from \"npm:hono@4.4.12\";\n\n// --- TYPE DEFINITIONS ---\ninterface Variable {\n name: string;\n description: string;\n type: \"text\" | \"number\" | \"boolean\" | \"list\" | \"json\";\n example: string;\n}\n\ninterface PromptConfig {\n taskDescription: string;\n format: \"conversational\" | \"json\" | \"structured\" | \"markdown\" | \"code\";\n jsonSchema?: string;\n structuredFormat?: string;\n responseStyle: \"default\" | \"concise\" | \"detailed\" | \"technical\" | \"creative\";\n language: string;\n tone: \"professional\" | \"casual\" | \"formal\" | \"friendly\";\n reasoning: boolean;\n citations: boolean;\n variables: Variable[];\n additionalInstructions?: string;\n}\n\ninterface StoredPrompt {\n id: string;\n config: PromptConfig;\n prompt: string;\n createdAt: string;\n}\n\ninterface TestInputs {\n [key: string]: string;\n}\n\n// --- PROMPT GENERATION SYSTEM ---\nconst PROMPT_ARCHITECT_SYSTEM =\n `You are an elite System Prompt Architect specializing in designing resilient, precise, and high-performance prompts for Large Language Models.\n\n# CORE METHODOLOGY: Test-Time Computation Framework\nExecute a multi-stage reasoning and verification process before generating the final prompt.\n\n## Stage 1: Deep Analysis & Constraint Mapping\n- Task Decomposition: Parse the input to identify primary objective, required output format, explicit constraints, success criteria, and edge cases\n- Reasoning Order Audit: Verify that all examples and instructions follow the \"Reasoning Before Conclusions\" principle\n- Structural Modeling: Generate architectural templates optimized for clarity, completeness, and performance\n- Constraint Extraction: Extract all implicit and explicit requirements\n\n## Stage 2: Verification & Iterative Refinement\n- Process Reward Scoring: Evaluate templates against comprehensive constraint checklist\n- Violation Detection: Identify structural gaps, ambiguities, logical inconsistencies\n- Targeted Rectification: Reconstruct deficient sections with precision\n- Cross-Validation: Ensure the refined prompt handles edge cases and maintains consistency\n\n# GENERATED PROMPT REQUIREMENTS\n\n## 1. Structural Architecture\n- Opening Directive: Begin with one concise sentence defining the model's role and primary objective\n- Hierarchical Organization: Use markdown headers (# for primary, ## for secondary, ### for tertiary sections)\n- Section Separators: Precede each major section with a horizontal rule (---) for visual clarity\n- Logical Flow: Order sections as: Role → Steps → Output Format → Examples → Notes/Constraints\n\n## 2. Process Definition\n- Granular Steps Section: Provide a numbered, sequential breakdown of the reasoning process\n- Reasoning Enforcement: Explicitly mandate that reasoning must precede any final answer\n- Decision Points: Highlight conditional logic, branching paths, or validation checkpoints\n- Error Handling: Include instructions for handling ambiguous inputs or missing data\n\n## 3. Output Specification\n- Format Precision: Define exact output structure including length constraints and syntax\n- Structured Data Bias: Default to JSON for any task involving classification, extraction, or scoring\n- Schema Definition: For JSON outputs, specify all keys, value types, required vs. optional fields\n- Quality Standards: Define criteria for completeness, accuracy, and formatting compliance\n\n## 4. Exemplification\n- Representative Examples: Include 2-3 examples demonstrating the full process from input to output\n- Placeholder Strategy: Use descriptive placeholders (e.g., [USER_QUERY], [REASONING_CHAIN], [FINAL_OUTPUT])\n- Input-Output Pairing: Clearly demarcate example inputs from expected outputs\n- Edge Case Coverage: Include at least one example handling an ambiguous or complex scenario\n\n## 5. Content Preservation & Enhancement\n- Fidelity: Retain all specific guidelines, rubrics, variables, terminology, and examples\n- Augmentation: Add missing structural elements while preserving original intent\n- Constraint Integration: Weave all implicit requirements into explicit instructions\n\n# OUTPUT FORMAT\nGenerate ONLY the complete system prompt. No meta-commentary, introductions, or explanations.\nBegin prompt generation now.`;\n\nfunction buildPromptGenerationRequest(config: PromptConfig): string {\n const variables = config.variables.map((v) =>\n `- Variable: {{${v.name}}} (${v.type}) - ${v.description}${\n v.example ? ` - Example: \"${v.example}\"` : \"\"\n }`\n ).join(\"\\n\");\n\n return `Generate a production-ready system prompt with the following specifications:\n\n# TASK DESCRIPTION\n${config.taskDescription}\n\n# VARIABLES\n${config.variables.length > 0 ? variables : \"No variables defined.\"}\n\n# RESPONSE FORMAT\n- Format Type: ${config.format}\n${\n config.format === \"json\" && config.jsonSchema\n ? `- JSON Schema:\\n${config.jsonSchema}`\n : \"\"\n }\n${\n config.format === \"structured\" && config.structuredFormat\n ? `- Structure: ${config.structuredFormat}`\n : \"\"\n }\n- Response Style: ${config.responseStyle}\n\n# CONSTRAINTS\n- Output Language: ${config.language}\n- Tone: ${config.tone}\n- Reasoning Required: ${\n config.reasoning ? \"Yes - must show reasoning before conclusions\" : \"No\"\n }\n- Citations Required: ${\n config.citations ? \"Yes - must cite sources for factual claims\" : \"No\"\n }\n\n# ADDITIONAL INSTRUCTIONS\n${config.additionalInstructions || \"None provided.\"}\n\nGenerate the complete system prompt now.`;\n}\n\n// --- SAMPLE INPUT GENERATION ---\nfunction generateSampleInputs(variables: Variable[]): TestInputs {\n const samples: TestInputs = {};\n\n variables.forEach((v) => {\n if (v.example) {\n samples[v.name] = v.example;\n return;\n }\n\n switch (v.type) {\n case \"text\":\n samples[v.name] = `Sample ${v.name} text`;\n break;\n case \"number\":\n samples[v.name] = \"42\";\n break;\n case \"boolean\":\n samples[v.name] = \"true\";\n break;\n case \"list\":\n samples[v.name] = \"item1, item2, item3\";\n break;\n case \"json\":\n samples[v.name] = '{\"key\": \"value\"}';\n break;\n }\n });\n\n return samples;\n}\n\n// --- HTML FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string): string {\n return `\n\n\n \n \n Advanced Prompt Studio\n \n \n \n \n\n\n

⚡ Advanced Prompt Studio

Generate production-ready prompts with variables, format control, and dynamic testing

\n\n

\n \n

⚙️ Configuration

\n \n

\n Task Description\n \n

\n\n

\n Response Format\n \n

\n\n

\n JSON Schema (optional)\n \n

\n\n

\n Structure Format\n \n

\n\n

\n Response Style\n \n

\n\n

\n Language\n \n

\n Tone\n \n

\n\n

\n \n Require reasoning before conclusions\n

\n\n

\n \n Require citations\n

\n\n

\n Additional Instructions\n \n

\n\n \n

📝 Variables

\n \n

\n\n

Add Variable

\n \n

\n\n \n

\n \n

📄 Generated Prompt

\n \n

\n Generate a prompt to see it here\n

\n\n \n

🧪 Test Prompt

\n \n

\n\n \n\n

Result:

\n\n \n

💾 Saved Prompts

No prompts saved yet

\n\n \n\n`;\n}\n\n// --- HONO APP SETUP ---\nconst app = new Hono();\n\n// Serve HTML frontend\napp.get(\"/\", (c) => {\n const url = new URL(c.req.url);\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return c.html(generateHtml(sourceUrl));\n});\n\n// Generate prompt endpoint\napp.post(\"/api/generate\", async (c) => {\n try {\n const config: PromptConfig = await c.req.json();\n\n if (!config.taskDescription || config.taskDescription.trim().length < 10) {\n return c.json({\n error: \"Task description must be at least 10 characters\",\n }, 400);\n }\n\n const openai = new OpenAI();\n const userRequest = buildPromptGenerationRequest(config);\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: PROMPT_ARCHITECT_SYSTEM },\n { role: \"user\", content: userRequest },\n ],\n temperature: 0.7,\n });\n\n const prompt = completion.choices[0].message.content;\n\n return c.json({ prompt });\n } catch (error) {\n console.error(\"Error generating prompt:\", error);\n return c.json(\n { error: \"Failed to generate prompt: \" + error.message },\n 500,\n );\n }\n});\n\n// Test prompt endpoint\napp.post(\"/api/test\", async (c) => {\n try {\n const { prompt, inputs } = await c.req.json();\n\n if (!prompt) {\n return c.json({ error: \"Prompt is required\" }, 400);\n }\n\n const openai = new OpenAI();\n\n // Replace variables in the prompt with actual values\n let finalPrompt = prompt;\n Object.entries(inputs).forEach(([key, value]) => {\n const regex = new RegExp(`\\\\{\\\\{${key}\\\\}\\\\}`, \"g\");\n finalPrompt = finalPrompt.replace(regex, value as string);\n });\n\n // Build user message from inputs\n const userMessage = Object.entries(inputs)\n .map(([key, value]) => `${key}: ${value}`)\n .join(\"\\n\");\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: finalPrompt },\n { role: \"user\", content: userMessage || \"Process this request.\" },\n ],\n temperature: 0.7,\n });\n\n const result = completion.choices[0].message.content;\n\n return c.json({ result });\n } catch (error) {\n console.error(\"Error testing prompt:\", error);\n return c.json({ error: \"Failed to test prompt: \" + error.message }, 500);\n }\n});\n\nexport default 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