[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_155":156,"_157":158},"routes/_app.x.$handle.$name",{"_97":96,"_34":47,"_105":87,"_28":106,"_13":14,"_107":-5,"_11":108,"_109":110,"_111":112,"_113":114,"_115":116,"_20":-5,"_117":-5,"_118":-5,"_119":27,"_120":121,"_122":25,"_123":-5,"_124":-5,"_125":126},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_45":46,"_34":47,"_40":48,"_49":-5,"_50":27,"_51":52,"_53":52,"_54":-5,"_55":-5,"_18":56,"_57":58,"_59":25,"_60":25,"_61":-5,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":-5,"_69":-5,"_70":71,"_72":71,"_73":74,"_75":74,"_76":74,"_77":71,"_78":71,"_79":80,"_81":82,"_83":84,"_85":27},"version",37,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":27,"_28":29,"_30":31},"file",{"_32":33,"_34":29,"_35":36,"_37":39,"_40":41,"_42":14,"_43":44,"_24":27},"previous",{"_32":33,"_34":29,"_35":36,"_37":38},"deleted",true,"deletedHere",false,"path","main.tsx","latestPath","main.ts","fileId","ade1524c-a59e-11f0-92ff-0224a6c84d84","name","fileType","http","content","/** @jsx jsx */\nimport { Hono } from \"npm:hono@latest\";\nimport { jsx } from \"npm:hono@latest/jsx\";\nimport { jsxRenderer } from \"npm:hono@latest/jsx-renderer\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\n\n// --- 1. TYPES ---\ninterface ConfluenceOutput {\n section_title_a: string;\n revised_prompt_text: string;\n section_title_b: string;\n questions: string[];\n}\ninterface DomainTransfer {\n domain: string;\n rigorConstraint: string;\n reasoning: string;\n}\n\n// --- 2. PROMPTS ---\nconst getBaseSystemPrompt = (\n userGoal: string,\n transferFunction: DomainTransfer,\n): string => `\nYou are the ultimate System Prompt Guy, an Elite Prompt Engineering Confluence model, tasked with collaboratively refining a user's initial concept into the most resilient, precise, and effective final system prompt through an iterative, Test-Time Computation (TTC)-inspired design process.\n\nThe user's core task/goal for their prompt is: \"${userGoal}\"\n\n---\n# INJECTED LOGICAL RIGOR: Domain Transfer Function (DTF)\nThe task has been mapped to the high-rigor domain of **${transferFunction.domain}**.\nYou MUST ensure that the following novel constraint is integrated into the \"Prompt Engineering Constraints\" section of the Revised Prompt:\n- **DTF Rigor Injection:** You must integrate the following constraint: *${transferFunction.rigorConstraint}*.\n---\n\n# Instructions and Core Role\nYour core role is to serve as the system prompt engineer, applying a rigorous set of structural and logical constraints to iteratively draft and refine a Revised Prompt based on the user's concept. You must prioritize Reasoning Before Conclusions in every step of your process.\n\n---\n# Prompt Engineering Constraints\nWhen drafting the [REVISED_PROMPT_TEXT], you must ensure it rigorously adheres to the following engineering standards:\n\n1. **Direct Start (Yes):** Start with a concise, single-sentence instruction line defining the model's role and primary task.\n2. **Formatting (Yes):** Use markdown headings (#, ##) and structure for maximum clarity.\n3. **Separators (Yes):** Use a horizontal line (---) immediately preceding each main section heading.\n4. **Reasoning Order (Yes):** Explicitly enforce that the executing model must provide its reasoning/justification BEFORE the final conclusion or classification.\n5. **Clarity & Voice (Yes):** Use clear, specific, active voice language; avoid ambiguity and unnecessary statements.\n6. **Completeness (Yes):** Include all necessary sections: Instructions, Steps, and Output Format.\n7. **Preservation (Yes):** Retain all specific details, guidelines, variables, and constants from the user's input.\n8. **Structured Bias (Yes):** Bias toward JSON for any final output that involves classification, entity extraction, or structured data.\n9. **DTF Rigor Injection (Yes):** *${transferFunction.rigorConstraint}*\n\n---\n# Steps: Iterative Refinement Process\nExecute the following steps sequentially for every user turn to guarantee Adaptive Compute Allocation and 100% instruction adherence.\n\n1. **Core Analysis & Drafting (TTC Reasoning):** Based on the user's latest input, draft the [REVISED_PROMPT_TEXT] by rigorously applying all constraints from the **Prompt Engineering Constraints** section.\n2. **Identify Gaps (Reasoning):** Analyze the initial input and the drafted [REVISED_PROMPT_TEXT] to determine what specific, additional information is required to maximize the prompt's precision.\n3. **Formulate Questions (Conclusion):** Based on the gaps identified, formulate a list of specific, highly relevant questions to ask the user.\n4. **Generate Output:** Present the output in the exact JSON format specified in the **Output Format Specification** section.\n\n---\n# Output Format Specification\nYour final, iterative output must be a single, valid JSON object. DO NOT include any text, commentary, or markdown outside of the JSON block.\n\n\\`\\`\\`json\n{\n \"section_title_a\": \"Revised Prompt\",\n \"revised_prompt_text\": \"[REVISED_PROMPT_TEXT]\",\n \"section_title_b\": \"Questions\",\n \"questions\": [\n \"[QUESTION_1]\",\n \"[QUESTION_2]\"\n ]\n}\n\\`\\`\\`\n`;\nconst getDtfPrompt = (userGoal: string): string => `\nYou are the Domain Transfer Agent (DTA). Your sole purpose is to increase the rigor of a given professional task by mapping its core logical demands to a high-stakes, highly disciplined professional domain.\n---\n# Instructions\n1. **Analyze the User Goal:** Identify the core logical functions required (e.g., speed, accuracy, risk assessment, sequential protocol, data synthesis).\n2. **Identify a High-Rigor Domain:** Select a domain (e.g., aerospace, surgical medicine, military protocol, financial regulation) that shares this logical function but operates under extreme stress or constraint.\n3. **Synthesize a Rigor Constraint:** Create a single, concise, and highly demanding constraint based on the selected domain's *modus operandi*.\n4. **Output Format:** Respond STRICTLY with a single, minified JSON object.\n---\n# Output Format Specification\n\\`\\`\\`json\n{\n \"domain\": \"[Selected High-Rigor Domain]\",\n \"rigorConstraint\": \"[The New Prompt Constraint to Inject]\",\n \"reasoning\": \"[Brief justification for the domain mapping]\"\n}\n\\`\\`\\`\n---\n# User Goal\nThe professional task the user wants to accomplish with their prompt is: \"${userGoal}\"\n`;\n\n// --- 3. AI LOGIC ---\nasync function getDomainTransfer(\n openai: OpenAI,\n userGoal: string,\n model: string,\n): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: model,\n messages: [{ role: \"system\", content: getDtfPrompt(userGoal) }],\n response_format: { type: \"json_object\" },\n });\n const result: DomainTransfer = JSON.parse(\n completion.choices[0].message.content,\n );\n if (!result.domain || !result.rigorConstraint || !result.reasoning) {\n throw new Error(\"DTF response missing essential keys.\");\n }\n console.log(\n `DTF Log - Domain: ${result.domain}, Reasoning: ${result.reasoning}`,\n );\n return result;\n } catch (e) {\n console.error(\"DTF Execution or Parsing Error:\", e);\n return {\n domain: \"Nuclear Reactor Protocol\",\n rigorConstraint: \"All output must be auditable and traceable.\",\n reasoning: \"Fallback due to AI parsing error.\",\n };\n }\n}\nasync function runPromptRefinement(\n openai: OpenAI,\n userGoal: string,\n model: string,\n): Promise {\n const transferFunction = await getDomainTransfer(openai, userGoal, model);\n const eliteSystemPrompt = getBaseSystemPrompt(userGoal, transferFunction);\n const completion = await openai.chat.completions.create({\n model: model,\n messages: [\n { role: \"system\", content: eliteSystemPrompt },\n {\n role: \"user\",\n content: `My initial prompt concept is: \"${userGoal}\". Begin.`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n const refinedOutput: ConfluenceOutput = JSON.parse(\n completion.choices[0].message.content,\n );\n return { ...refinedOutput, domainTransfer: transferFunction };\n}\n\n// --- 4. UI & STYLES ---\nconst styles = `\n :root {\n --bg-color: #0F172A; --text-primary: #F8FAFC; --text-secondary: #94A3B8; --accent-color: #6366F1;\n --accent-dark: #4F46E5; --card-bg: #1E293B; --border-color: #334155; --code-bg: #121A2C;\n --shadow-color: rgba(0,0,0,0.5); --error-bg: #450a0a; --error-border: #ef4444;\n }\n *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }\n body {\n background-color: var(--bg-color); color: var(--text-primary); font-family: 'Inter', sans-serif;\n min-height: 100vh; padding: 20px;\n }\n .container { width: 100%; max-width: 1400px; margin: 0 auto; display: grid; grid-template-columns: 1fr; gap: 24px; }\n @media (min-width: 1200px) { .container { grid-template-columns: 1fr 2fr; } }\n header { grid-column: 1 / -1; text-align: center; padding-bottom: 16px; }\n header h1 { font-size: 2.5rem; font-weight: 700; color: var(--accent-color); }\n header p { color: var(--text-secondary); margin-top: 8px; max-width: 800px; margin-inline: auto; }\n .panel {\n background: var(--card-bg); border: 1px solid var(--border-color); border-radius: 12px;\n padding: 24px; box-shadow: 0 4px 15px var(--shadow-color);\n }\n .panel h2 {\n font-size: 1.5rem; font-weight: 600; margin-bottom: 16px; border-bottom: 1px solid var(--border-color);\n padding-bottom: 8px; display: flex; justify-content: space-between; align-items: center;\n }\n .main-content { display: grid; gap: 24px; grid-template-columns: 1fr; }\n @media (min-width: 900px) { .main-content { grid-template-columns: 1fr 1fr; } }\n #input-panel { grid-column: 1 / -1; }\n #input-form { display: flex; flex-direction: column; gap: 16px; }\n .form-group { display: flex; flex-direction: column; gap: 8px; }\n .form-group label { font-weight: 500; color: var(--text-secondary); }\n #user-goal-input, #model-select {\n width: 100%; padding: 12px; font-size: 1rem; border-radius: 8px; border: 1px solid var(--border-color);\n background: var(--code-bg); color: var(--text-primary); font-family: 'Inter', sans-serif;\n }\n #user-goal-input:focus, #model-select:focus { outline: none; border-color: var(--accent-color); }\n #submit-btn, .copy-btn {\n padding: 12px 20px; border: none; background: var(--accent-color); color: var(--text-primary);\n font-size: 1rem; font-weight: 600; border-radius: 8px; cursor: pointer; transition: background-color .2s;\n }\n #submit-btn:hover:not(:disabled), .copy-btn:hover { background: var(--accent-dark); }\n #submit-btn:disabled { background: #475569; cursor: not-allowed; }\n .copy-btn { font-size: 0.8rem; padding: 6px 12px; }\n .error-panel {\n grid-column: 1 / -1; background-color: var(--error-bg); border: 1px solid var(--error-border);\n color: var(--text-primary); padding: 16px; border-radius: 8px; font-family: 'JetBrains Mono', monospace;\n }\n #revised-prompt-output {\n border: 1px solid var(--border-color); padding: 16px; border-radius: 8px; background: var(--code-bg);\n white-space: pre-wrap; font-family: 'JetBrains Mono', monospace; font-size: 0.85rem; min-height: 200px;\n }\n #dtf-output { background: var(--code-bg); border: 1px solid #7C3AED; padding: 16px; border-radius: 8px; }\n #dtf-output h3 { color: #C4B5FD; margin-bottom: 8px; }\n #dtf-output p { font-size: 0.9rem; color: var(--text-secondary); margin-bottom: 4px; }\n #dtf-output code { display: block; background: #1D1D30; padding: 8px; border-radius: 4px; color: #D8B4FE; }\n #questions-output li { background: #0D94881A; border-left: 3px solid #0D9488; padding: 12px; margin-bottom: 12px; border-radius: 4px; }\n .spinner { border: 4px solid var(--border-color); border-top: 4px solid var(--accent-color); border-radius: 50%; width: 40px; height: 40px; animation: spin 1s linear infinite; margin: 20px auto; }\n @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }\n .hidden { display: none !important; }\n #history-panel .history-item {\n background-color: var(--code-bg); border: 1px solid var(--border-color); padding: 12px; border-radius: 8px; margin-bottom: 12px;\n }\n #history-panel .history-item p { margin: 0; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; color: var(--text-secondary); }\n #history-panel .history-item strong { color: var(--text-primary); }\n`;\n\n// --- 5. JSX LAYOUT COMPONENT ---\nconst Layout = (props: { sourceUrl: string; children?: any }) => (\n \n \n \n \n Prompt Confluence 🌊\n \n \n \n \n \n \n

Prompt Confluence 🌊

\n The Domain Transfer Function (DTF) injects logical rigor from\n high-stakes professional fields to create resilient, precise, and\n effective system prompts for your AI tools.\n

\n \n

Initial Prompt Concept

\n \n

\n Executing Domain Transfer Function...\n

\n Revised Prompt\n \n

\n \n

DTF Result 🚀

\n Domain: \n

\n Reasoning: \n

\n Constraint Injected:\n

\n \n

Next Refinement Questions

\n \n

\n \n \n\n {/* CORRECTED: Use dangerouslySetInnerHTML to prevent HTML-escaping of quotes */}\n \n\n \n \n \n);\n\n// --- 6. SERVER ---\nconst app = new Hono();\n\napp.use(\n \"*\",\n jsxRenderer((props, c) => {\n const url = new URL(c.req.url);\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n const fullSourceUrl = sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`;\n return ;\n }),\n);\n\napp.get(\"/\", (c) => {\n return c.render();\n});\n\napp.post(\"/refine\", async (c) => {\n try {\n const { userGoal, model } = await c.req.json();\n const allowedModels = [\"gpt-4o\", \"gpt-4-turbo\", \"gpt-3.5-turbo\"];\n\n if (\n !userGoal || typeof userGoal !== \"string\" || userGoal.trim().length < 10\n ) {\n return c.json({ error: \"The user goal must be a valid string.\" }, 400);\n }\n if (!model || !allowedModels.includes(model)) {\n return c.json({ error: \"A valid AI model must be selected.\" }, 400);\n }\n\n const openai = new OpenAI();\n const result = await runPromptRefinement(openai, userGoal, model);\n return c.json(result);\n } catch (error) {\n console.error(\"Critical Error in /refine:\", error);\n return c.json(\n { error: \"Server processing error.\", reason: error.message },\n 500,\n );\n }\n});\n\nexport default app.fetch;","/** @jsx jsx */\nimport { Hono } from \"npm:hono@latest\";\nimport { jsx } from \"npm:hono@latest/jsx\";\nimport { jsxRenderer } from \"npm:hono@latest/jsx-renderer\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\n\n// --- 1. TYPES ---\ninterface ConfluenceOutput {\n section_title_a: string;\n revised_prompt_text: string;\n section_title_b: string;\n questions: string[];\n}\ninterface DomainTransfer {\n domain: string;\n rigorConstraint: string;\n reasoning: string;\n}\n\n// --- 2. PROMPTS ---\nconst getBaseSystemPrompt = (\n userGoal: string,\n transferFunction: DomainTransfer,\n): string => `\nYou are the ultimate System Prompt Guy, an Elite Prompt Engineering Confluence model, tasked with collaboratively refining a user's initial concept into the most resilient, precise, and effective final system prompt through an iterative, Test-Time Computation (TTC)-inspired design process.\n\nThe user's core task/goal for their prompt is: \"${userGoal}\"\n\n---\n# INJECTED LOGICAL RIGOR: Domain Transfer Function (DTF)\nThe task has been mapped to the high-rigor domain of **${transferFunction.domain}**.\nYou MUST ensure that the following novel constraint is integrated into the \"Prompt Engineering Constraints\" section of the Revised Prompt:\n- **DTF Rigor Injection:** You must integrate the following constraint: *${transferFunction.rigorConstraint}*.\n---\n\n# Instructions and Core Role\nYour core role is to serve as the system prompt engineer, applying a rigorous set of structural and logical constraints to iteratively draft and refine a Revised Prompt based on the user's concept. You must prioritize Reasoning Before Conclusions in every step of your process.\n\n---\n# Prompt Engineering Constraints\nWhen drafting the [REVISED_PROMPT_TEXT], you must ensure it rigorously adheres to the following engineering standards:\n\n1. **Direct Start (Yes):** Start with a concise, single-sentence instruction line defining the model's role and primary task.\n2. **Formatting (Yes):** Use markdown headings (#, ##) and structure for maximum clarity.\n3. **Separators (Yes):** Use a horizontal line (---) immediately preceding each main section heading.\n4. **Reasoning Order (Yes):** Explicitly enforce that the executing model must provide its reasoning/justification BEFORE the final conclusion or classification.\n5. **Clarity & Voice (Yes):** Use clear, specific, active voice language; avoid ambiguity and unnecessary statements.\n6. **Completeness (Yes):** Include all necessary sections: Instructions, Steps, and Output Format.\n7. **Preservation (Yes):** Retain all specific details, guidelines, variables, and constants from the user's input.\n8. **Structured Bias (Yes):** Bias toward JSON for any final output that involves classification, entity extraction, or structured data.\n9. **DTF Rigor Injection (Yes):** *${transferFunction.rigorConstraint}*\n\n---\n# Steps: Iterative Refinement Process\nExecute the following steps sequentially for every user turn to guarantee Adaptive Compute Allocation and 100% instruction adherence.\n\n1. **Core Analysis & Drafting (TTC Reasoning):** Based on the user's latest input, draft the [REVISED_PROMPT_TEXT] by rigorously applying all constraints from the **Prompt Engineering Constraints** section.\n2. **Identify Gaps (Reasoning):** Analyze the initial input and the drafted [REVISED_PROMPT_TEXT] to determine what specific, additional information is required to maximize the prompt's precision.\n3. **Formulate Questions (Conclusion):** Based on the gaps identified, formulate a list of specific, highly relevant questions to ask the user.\n4. **Generate Output:** Present the output in the exact JSON format specified in the **Output Format Specification** section.\n\n---\n# Output Format Specification\nYour final, iterative output must be a single, valid JSON object. DO NOT include any text, commentary, or markdown outside of the JSON block.\n\n\\`\\`\\`json\n{\n \"section_title_a\": \"Revised Prompt\",\n \"revised_prompt_text\": \"[REVISED_PROMPT_TEXT]\",\n \"section_title_b\": \"Questions\",\n \"questions\": [\n \"[QUESTION_1]\",\n \"[QUESTION_2]\"\n ]\n}\n\\`\\`\\`\n`;\nconst getDtfPrompt = (userGoal: string): string => `\nYou are the Domain Transfer Agent (DTA). Your sole purpose is to increase the rigor of a given professional task by mapping its core logical demands to a high-stakes, highly disciplined professional domain.\n---\n# Instructions\n1. **Analyze the User Goal:** Identify the core logical functions required (e.g., speed, accuracy, risk assessment, sequential protocol, data synthesis).\n2. **Identify a High-Rigor Domain:** Select a domain (e.g., aerospace, surgical medicine, military protocol, financial regulation) that shares this logical function but operates under extreme stress or constraint.\n3. **Synthesize a Rigor Constraint:** Create a single, concise, and highly demanding constraint based on the selected domain's *modus operandi*.\n4. **Output Format:** Respond STRICTLY with a single, minified JSON object.\n---\n# Output Format Specification\n\\`\\`\\`json\n{\n \"domain\": \"[Selected High-Rigor Domain]\",\n \"rigorConstraint\": \"[The New Prompt Constraint to Inject]\",\n \"reasoning\": \"[Brief justification for the domain mapping]\"\n}\n\\`\\`\\`\n---\n# User Goal\nThe professional task the user wants to accomplish with their prompt is: \"${userGoal}\"\n`;\n\n// --- 3. AI LOGIC ---\nasync function getDomainTransfer(\n openai: OpenAI,\n userGoal: string,\n model: string,\n): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: model,\n messages: [{ role: \"system\", content: getDtfPrompt(userGoal) }],\n response_format: { type: \"json_object\" },\n });\n const result: DomainTransfer = JSON.parse(\n completion.choices[0].message.content,\n );\n if (!result.domain || !result.rigorConstraint || !result.reasoning) {\n throw new Error(\"DTF response missing essential keys.\");\n }\n console.log(\n `DTF Log - Domain: ${result.domain}, Reasoning: ${result.reasoning}`,\n );\n return result;\n } catch (e) {\n console.error(\"DTF Execution or Parsing Error:\", e);\n return {\n domain: \"Nuclear Reactor Protocol\",\n rigorConstraint: \"All output must be auditable and traceable.\",\n reasoning: \"Fallback due to AI parsing error.\",\n };\n }\n}\nasync function runPromptRefinement(\n openai: OpenAI,\n userGoal: string,\n model: string,\n): Promise {\n const transferFunction = await getDomainTransfer(openai, userGoal, model);\n const eliteSystemPrompt = getBaseSystemPrompt(userGoal, transferFunction);\n const completion = await openai.chat.completions.create({\n model: model,\n messages: [\n { role: \"system\", content: eliteSystemPrompt },\n {\n role: \"user\",\n content: `My initial prompt concept is: \"${userGoal}\". Begin.`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n const refinedOutput: ConfluenceOutput = JSON.parse(\n completion.choices[0].message.content,\n );\n return { ...refinedOutput, domainTransfer: transferFunction };\n}\n\n// --- 4. UI & STYLES ---\nconst styles = `\n :root {\n --bg-color: #0F172A; --text-primary: #F8FAFC; --text-secondary: #94A3B8; --accent-color: #6366F1;\n --accent-dark: #4F46E5; --card-bg: #1E293B; --border-color: #334155; --code-bg: #121A2C;\n --shadow-color: rgba(0,0,0,0.5); --error-bg: #450a0a; --error-border: #ef4444;\n }\n *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }\n body {\n background-color: var(--bg-color); color: var(--text-primary); font-family: 'Inter', sans-serif;\n min-height: 100vh; padding: 20px;\n }\n .container { width: 100%; max-width: 1400px; margin: 0 auto; display: grid; grid-template-columns: 1fr; gap: 24px; }\n @media (min-width: 1200px) { .container { grid-template-columns: 1fr 2fr; } }\n header { grid-column: 1 / -1; text-align: center; padding-bottom: 16px; }\n header h1 { font-size: 2.5rem; font-weight: 700; color: var(--accent-color); }\n header p { color: var(--text-secondary); margin-top: 8px; max-width: 800px; margin-inline: auto; }\n .panel {\n background: var(--card-bg); border: 1px solid var(--border-color); border-radius: 12px;\n padding: 24px; box-shadow: 0 4px 15px var(--shadow-color);\n }\n .panel h2 {\n font-size: 1.5rem; font-weight: 600; margin-bottom: 16px; border-bottom: 1px solid var(--border-color);\n padding-bottom: 8px; display: flex; justify-content: space-between; align-items: center;\n }\n .main-content { display: grid; gap: 24px; grid-template-columns: 1fr; }\n @media (min-width: 900px) { .main-content { grid-template-columns: 1fr 1fr; } }\n #input-panel { grid-column: 1 / -1; }\n #input-form { display: flex; flex-direction: column; gap: 16px; }\n .form-group { display: flex; flex-direction: column; gap: 8px; }\n .form-group label { font-weight: 500; color: var(--text-secondary); }\n #user-goal-input, #model-select {\n width: 100%; padding: 12px; font-size: 1rem; border-radius: 8px; border: 1px solid var(--border-color);\n background: var(--code-bg); color: var(--text-primary); font-family: 'Inter', sans-serif;\n }\n #user-goal-input:focus, #model-select:focus { outline: none; border-color: var(--accent-color); }\n #submit-btn, .copy-btn {\n padding: 12px 20px; border: none; background: var(--accent-color); color: var(--text-primary);\n font-size: 1rem; font-weight: 600; border-radius: 8px; cursor: pointer; transition: background-color .2s;\n }\n #submit-btn:hover:not(:disabled), .copy-btn:hover { background: var(--accent-dark); }\n #submit-btn:disabled { background: #475569; cursor: not-allowed; }\n .copy-btn { font-size: 0.8rem; padding: 6px 12px; }\n .error-panel {\n grid-column: 1 / -1; background-color: var(--error-bg); border: 1px solid var(--error-border);\n color: var(--text-primary); padding: 16px; border-radius: 8px; font-family: 'JetBrains Mono', monospace;\n }\n #revised-prompt-output {\n border: 1px solid var(--border-color); padding: 16px; border-radius: 8px; background: var(--code-bg);\n white-space: pre-wrap; font-family: 'JetBrains Mono', monospace; font-size: 0.85rem; min-height: 200px;\n }\n #dtf-output { background: var(--code-bg); border: 1px solid #7C3AED; padding: 16px; border-radius: 8px; }\n #dtf-output h3 { color: #C4B5FD; margin-bottom: 8px; }\n #dtf-output p { font-size: 0.9rem; color: var(--text-secondary); margin-bottom: 4px; }\n #dtf-output code { display: block; background: #1D1D30; padding: 8px; border-radius: 4px; color: #D8B4FE; }\n #questions-output li { background: #0D94881A; border-left: 3px solid #0D9488; padding: 12px; margin-bottom: 12px; border-radius: 4px; }\n .spinner { border: 4px solid var(--border-color); border-top: 4px solid var(--accent-color); border-radius: 50%; width: 40px; height: 40px; animation: spin 1s linear infinite; margin: 20px auto; }\n @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }\n .hidden { display: none !important; }\n #history-panel .history-item {\n background-color: var(--code-bg); border: 1px solid var(--border-color); padding: 12px; border-radius: 8px; margin-bottom: 12px;\n }\n #history-panel .history-item p { margin: 0; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; color: var(--text-secondary); }\n #history-panel .history-item strong { color: var(--text-primary); }\n`;\n\n// --- 5. JSX LAYOUT COMPONENT ---\nconst Layout = (props: { sourceUrl: string; children?: any }) => (\n \n \n \n \n Prompt Confluence 🌊\n \n \n \n \n \n \n

Prompt Confluence 🌊

\n The Domain Transfer Function (DTF) injects logical rigor from\n high-stakes professional fields to create resilient, precise, and\n effective system prompts for your AI tools.\n

\n \n

Initial Prompt Concept

\n \n

\n Executing Domain Transfer Function...\n

\n Revised Prompt\n \n

\n \n

DTF Result 🚀

\n Domain: \n

\n Reasoning: \n

\n Constraint Injected:\n

\n \n

Next Refinement Questions

\n \n

Initial Prompt Concept

\n Revised Prompt\n Copy\n

DTF Result 🚀

Next Refinement Questions

Initial Prompt Concept

\n Revised Prompt\n Copy\n

DTF Result 🚀

Next Refinement Questions

\n Revised Prompt\n \n

\n Revised Prompt\n \n

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