Taskflow AI

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n/* ----------------------------------------\n Base Route\n----------------------------------------- */\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n/* ----------------------------------------\n Industry Generation\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: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n});\n\n/* ----------------------------------------\n Information Gatherer\n----------------------------------------- */\napp.post(\"/api/gather\", async (c: Context) => {\n const { task, domain, history } = await c.req.json();\n const historyString = history.map((h) =>\n `${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\n/* ----------------------------------------\n Dynamic Generation (Occupations / Tasks)\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 constraints: {\n deliverable_focus:\n \"Only generate tasks that culminate in recognized, professional deliverables or artifacts for that industry (reports, specifications, proposals, contracts, policies, etc.)\",\n reject_types: [\n \"brainstorming\",\n \"ideation\",\n \"planning-only\",\n \"summarization\",\n \"discussion\",\n ],\n },\n };\n return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Dynamic Prompt Creation\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 =\n `Company Context: ${JSON.stringify(company_context)}\\n\\n` +\n `Occupation: ${occupation_title}\\n\\n` +\n `Task: ${task}\\n\\n` +\n `MANDATE: The refined prompt MUST yield a tangible, industry-standard deliverable typical for this role and domain — e.g., structured reports, specifications, legal documents, marketing assets, or similar professional artifacts. Exclude anything conceptual or partial.`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Execute Stream (Task Execution)\n----------------------------------------- */\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, company_context, category } = await c.req\n .json();\n\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n let systemPrompt = \"\";\n let userPrompt = \"\";\n\n // Inject user inputs into the refined task briefing\n let finalTaskBriefing = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalTaskBriefing = finalTaskBriefing.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n\n // --- TEMPLATE SELECTION LOGIC ---\n let baseTemplate = \"\";\n if (category === \"prompt_building\") {\n baseTemplate = config.seedTemplates.prompt_building;\n // For prompt building, the user prompt IS the task\n userPrompt =\n `Create an expert execution prompt for the task below, fully aligned with standard deliverable formats of this industry.\\n\\n` +\n `Task: \"${finalTaskBriefing}\"\\n\\n` +\n `Company Context: ${JSON.stringify(company_context)}`;\n } else {\n // For all other categories, select template based on company's use case\n baseTemplate = config.seedTemplates[company_context.use_case] ||\n config.seedTemplates.default;\n\n userPrompt = \"Proceed with task execution now.\";\n }\n\n // Construct the final System Prompt\n systemPrompt = `# BASE DIRECTIVE\\n${baseTemplate}\\n\\n` +\n `# COMPANY CONTEXT\\n` +\n `- Company: ${company_context.company_name}\\n` +\n `- Description: ${company_context.company_description}\\n` +\n `- Use Case: ${company_context.use_case}\\n` +\n `- Language for Output: ${company_context.language}\\n` +\n `---\\n` +\n `# TASK BRIEFING\\n${finalTaskBriefing}\\n\\n` +\n `# FINAL INSTRUCTIONS\\n` +\n `1. Deliver a complete, well-structured, **industry-standard artifact** based *only* on the TASK BRIEFING.\\n` +\n `2. Do not include conversation, apologies, or explanations.\\n` +\n `3. Write the entire response in the requested language: **${company_context.language}**.`;\n\n // Use 'prompt_building' logic for its specific user prompt, otherwise use the default.\n // Note: Both branches call the same service, but this structure is kept\n // in case the userPrompt logic diverges more significantly later.\n if (category === \"prompt_building\") {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n } else {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n }\n});\n\n/* ----------------------------------------\n Evaluation\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\n/* ----------------------------------------\n Refinement\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: ${language}.\n## Original Briefing\n${briefing}\n## Your First Draft\n${first_draft}\n## QA Critique\n${critique}`;\n return services.callOpenAI(config.prompts.REFINER_AGENT, userContent, {\n c,\n stream: true,\n });\n});\n\nexport default app.fetch;","deleted","label",[126],"pulls",1,"forks","envVars",{"_148":55,"_150":55},"connections",{"_148":55,"_149":55},"count","unfilledCount","emptyCount",{"_20":125,"_13":126,"_127":49,"_128":-5,"_129":21,"_130":30,"_131":51,"_22":152,"_29":133,"_134":135,"_136":137,"_138":40,"_139":-5,"_153":154,"_93":155},["D",1761155487082],"children",[],[126],["D",1761127702925],{"_11":12,"_91":87},{"_11":12,"_85":-5,"_86":87,"_46":47,"_88":89,"_90":40},{"_29":30,"_13":26,"_20":21,"_27":28,"_31":32,"_33":34,"_78":42,"_79":80,"_81":82,"_83":14,"_84":12},"ctx",{"_71":-5,"_115":193,"_194":195,"_196":197,"_198":40,"_199":40,"_200":40,"_201":51,"_202":-5,"_203":-7,"_204":-7,"_205":55,"_206":40,"_207":40,"_208":-5,"_209":40,"_210":40,"_211":40,"_212":-5},"ENV",{"_164":165,"_166":167,"_168":26,"_169":170,"_171":172,"_173":174,"_175":176,"_177":178,"_179":180,"_181":182,"_183":184,"_185":186,"_187":188,"_189":190,"_191":192},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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