[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_154":155,"_156":157},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":53,"_13":14,"_104":-5,"_11":105,"_106":107,"_108":109,"_110":111,"_112":113,"_20":-5,"_114":-5,"_115":-5,"_116":25,"_117":118,"_119":120,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",14,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","f240d034-6420-11f1-a269-1607ee4eb77e","name","fileType","http","content","/** Find Candidates Backend — Val Town HTTP handler */\n\nimport { Hono } from \"npm:hono\";\nimport { cors } from \"npm:hono/cors\";\n\nconst app = new Hono();\napp.use(\"*\", cors());\n\n// Public — no auth\napp.get(\"/api/health\", (c) => c.json({ ok: true, version: \"0.4.0\" }));\n\n// Bearer-token gate for all /api/* routes registered below\napp.use(\"/api/*\", async (c, next) => {\n const token = Deno.env.get(\"ACCESS_TOKEN\");\n if (!token) return next();\n const auth = c.req.header(\"Authorization\") ?? \"\";\n if (auth !== `Bearer ${token}`) {\n return c.json({ error: \"Unauthorized\" }, 401);\n }\n return next();\n});\n\n// ─── Types ────────────────────────────────────────────────────────────────────\n\ninterface Filters {\n title?: string;\n company?: string;\n seniority?: string;\n location?: string;\n skills?: string[];\n}\n\ninterface Candidate {\n name: string;\n html_url: string;\n where: string;\n why: string;\n signals: string[];\n gaps: string[];\n tier: number;\n source: string;\n}\n\n// ─── Claude helpers (raw HTTP — no SDK in Deno/Val Town) ──────────────────────\n\nconst ANTHROPIC_URL = \"https://api.anthropic.com/v1/messages\";\n\n/** Call Claude with tool_choice to force structured JSON output. */\nasync function callClaude(\n apiKey: string,\n model: string,\n maxTokens: number,\n tool: Record,\n userMessage: string,\n): Promise> {\n const toolName = tool.name as string;\n\n const res = await fetch(ANTHROPIC_URL, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"x-api-key\": apiKey,\n \"anthropic-version\": \"2023-06-01\",\n },\n body: JSON.stringify({\n model,\n max_tokens: maxTokens,\n tool_choice: { type: \"tool\", name: toolName },\n tools: [tool],\n messages: [{ role: \"user\", content: userMessage }],\n }),\n });\n\n if (!res.ok) {\n const text = await res.text();\n throw new Error(`Claude ${res.status}: ${text}`);\n }\n\n const data = await res.json() as {\n content: Array<\n { type: string; name: string; input: Record }\n >;\n };\n\n const block = data.content.find(\n (b) => b.type === \"tool_use\" && b.name === toolName,\n );\n if (!block) throw new Error(`Claude returned no ${toolName} call`);\n return block.input;\n}\n\n// ─── Step 1: parse brief → structured filters ─────────────────────────────────\n\nasync function parseBriefToFilters(\n brief: string,\n apiKey: string,\n): Promise {\n const result = await callClaude(\n apiKey,\n \"claude-sonnet-4-6\", // simple extraction — sonnet is sufficient\n 512,\n {\n name: \"extract_filters\",\n description:\n \"Extract structured search filters from a natural-language recruiting brief.\",\n input_schema: {\n type: \"object\",\n properties: {\n title: {\n type: \"string\",\n description:\n \"Full job title — always expand abbreviations (CFO → Chief Financial Officer, CTO → Chief Technology Officer, VP Sales → Vice President of Sales, etc.)\",\n },\n company: {\n type: \"string\",\n description:\n \"Target company name if the brief asks to source from a specific company\",\n },\n seniority: {\n type: \"string\",\n description:\n \"Seniority level, one of: cxo, vp, director, manager, senior, partner, owner, entry\",\n },\n location: {\n type: \"string\",\n description:\n \"City, metro, or region, e.g. 'New York', 'San Francisco Bay Area'\",\n },\n skills: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Key skills, industries, or keywords, e.g. ['private equity', 'SaaS', 'M&A']\",\n },\n },\n required: [],\n additionalProperties: false,\n },\n },\n `Extract search filters from this recruiting brief:\\n\\n${brief}`,\n ) as Filters;\n\n // Strip empty / zero-length values so buildPDLQuery gets clean input\n if (!result.title) delete result.title;\n if (!result.company) delete result.company;\n if (!result.seniority) delete result.seniority;\n if (!result.location) delete result.location;\n if (!result.skills?.length) delete result.skills;\n\n return result;\n}\n\n// ─── Step 2: rank PDL results with Claude ─────────────────────────────────────\n\ninterface RankEntry {\n index: number;\n tier: 1 | 2 | 3;\n why: string;\n gaps: string[];\n}\n\nasync function rankCandidates(\n candidates: Candidate[],\n brief: string,\n apiKey: string,\n): Promise {\n if (candidates.length === 0) return [];\n\n // Send a compact view so we don't waste tokens on raw PDL noise\n const slim = candidates.map((c, i) => ({\n index: i,\n name: c.name,\n current_role: c.why,\n location: c.where,\n signals: c.signals.slice(0, 5),\n }));\n\n const result = await callClaude(\n apiKey,\n \"claude-opus-4-8\", // quality judgment — use Opus\n 4096, // room for up to 25 ranked entries without clipping\n {\n name: \"rank_candidates\",\n description:\n \"Rank every candidate by fit for a role. Return all with tier, why, and gaps. Sort tier 1 → 2 → 3.\",\n input_schema: {\n type: \"object\",\n properties: {\n ranked: {\n type: \"array\",\n items: {\n type: \"object\",\n properties: {\n index: {\n type: \"integer\",\n description: \"Original 0-based index from the input list\",\n },\n tier: {\n type: \"integer\",\n enum: [1, 2, 3],\n description:\n \"1 = strong fit 2 = potential fit 3 = weak fit\",\n },\n why: {\n type: \"string\",\n description: \"1-2 sentences on fit with the brief\",\n },\n gaps: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Up to 3 missing qualifications or open questions\",\n },\n },\n required: [\"index\", \"tier\", \"why\", \"gaps\"],\n additionalProperties: false,\n },\n },\n },\n required: [\"ranked\"],\n additionalProperties: false,\n },\n },\n `You are an executive recruiter at a PE-focused search firm.\\n\\nROLE BRIEF:\\n${brief}\\n\\nCANDIDATES (${candidates.length} total):\\n${\n JSON.stringify(slim, null, 2)\n }\\n\\nReturn all ${candidates.length} candidates ranked by fit. Use tier 1 (strong), 2 (potential), 3 (weak). Sort best first.`,\n ) as { ranked: RankEntry[] };\n\n // Merge Claude's scores back onto the original PDL records\n const merged = (result.ranked ?? [])\n .filter((r) => r.index >= 0 && r.index < candidates.length)\n .map((r) => ({\n ...candidates[r.index],\n tier: r.tier,\n why: r.why,\n gaps: r.gaps ?? [],\n }));\n\n // Safety sort in case Claude didn't fully order them\n merged.sort((a, b) => a.tier - b.tier);\n return merged;\n}\n\n// ─── PDL adapter ──────────────────────────────────────────────────────────────\n\n// PDL's job_title_levels vocabulary — map common recruiter phrasing onto it\nconst LEVEL_MAP: Record = {\n \"c-suite\": \"cxo\",\n \"c_suite\": \"cxo\",\n \"c-level\": \"cxo\",\n \"c level\": \"cxo\",\n \"csuite\": \"cxo\",\n \"chief\": \"cxo\",\n \"executive\": \"cxo\",\n \"exec\": \"cxo\",\n \"cxo\": \"cxo\",\n \"vice president\": \"vp\",\n \"vice-president\": \"vp\",\n \"vp\": \"vp\",\n \"junior\": \"entry\",\n};\nfunction normalizeSeniority(s: string): string {\n const k = s.trim().toLowerCase();\n return LEVEL_MAP[k] ?? k.replace(/[\\s-]+/g, \"_\");\n}\n\nfunction buildPDLQuery(filters: Filters) {\n const must: unknown[] = [];\n const should: unknown[] = [];\n\n if (filters.title) must.push({ match: { job_title: filters.title } });\n if (filters.company) {\n must.push({ term: { job_company_name: filters.company.toLowerCase() } });\n }\n // Location goes in should, not must — PDL free tier redacts location_name (returns boolean),\n // so a must-match would eliminate all results. Should-boost still works on paid tier.\n if (filters.location) {\n should.push({ match: { location_name: filters.location } });\n }\n\n // Skip seniority when a specific title is provided — titles like \"CFO\" already imply seniority,\n // and stacking both over-constrains the query.\n if (filters.seniority && !filters.title) {\n must.push({\n term: { job_title_levels: normalizeSeniority(filters.seniority) },\n });\n }\n\n // Skills go in `should` (boost ranking) not `must` (hard filter) — PDL skill tags are\n // single words or short phrases; multi-word descriptors from Claude rarely match exactly.\n if (filters.skills?.length) {\n for (const s of filters.skills) {\n should.push({ term: { skills: s.toLowerCase() } });\n }\n }\n\n if (must.length === 0 && should.length === 0) return { match_all: {} };\n\n const boolQuery: Record = {};\n if (must.length) boolQuery.must = must;\n if (should.length) boolQuery.should = should;\n return { bool: boolQuery };\n}\n\n// PDL returns names lowercased — capitalize for display/CSV\nfunction titleCase(s: string): string {\n return s.replace(/\\b\\p{L}/gu, (ch) => ch.toUpperCase());\n}\n\nfunction normalizePerson(p: Record): Candidate {\n const profiles = (p.profiles as Array<{ network: string; url: string }>) ??\n [];\n const github = profiles.find((x) => x.network === \"github\")?.url ?? \"\";\n const twitter = profiles.find((x) => x.network === \"twitter\")?.url ?? \"\";\n\n const title = (p.job_title as string) ?? \"\";\n const company = (p.job_company_name as string) ?? \"\";\n const skills = ((p.skills as string[]) ?? []).slice(0, 4);\n\n const signals: string[] = [];\n if (title) signals.push(title);\n if (company) signals.push(`@ ${company}`);\n signals.push(...skills);\n\n const rawUrl = github || twitter || \"\"; // never surface LinkedIn\n\n return {\n name: titleCase((p.full_name as string) ?? \"Unknown\"),\n html_url: rawUrl && !/^https?:\\/\\//i.test(rawUrl)\n ? `https://${rawUrl}`\n : rawUrl,\n where: typeof p.location_name === \"string\" ? p.location_name : \"\",\n why: `${title}${company ? ` at ${company}` : \"\"}`,\n signals,\n gaps: [],\n tier: 2, // overwritten by rankCandidates\n source: \"pdl\",\n };\n}\n\nasync function searchPDL(filters: Filters, size = 10): Promise {\n const apiKey = Deno.env.get(\"PDL_API_KEY\");\n if (!apiKey) throw new Error(\"PDL_API_KEY not configured\");\n\n const res = await fetch(\"https://api.peopledatalabs.com/v5/person/search\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", \"X-Api-Key\": apiKey },\n body: JSON.stringify({\n query: buildPDLQuery(filters),\n size,\n dataset: \"all\",\n }),\n });\n\n // PDL returns 404 when no records match — not an error\n if (res.status === 404) return [];\n\n if (!res.ok) {\n const text = await res.text();\n throw new Error(`PDL ${res.status}: ${text}`);\n }\n\n const data = await res.json() as { data: Record[] };\n return (data.data ?? []).map(normalizePerson);\n}\n\n// ─── Routes ───────────────────────────────────────────────────────────────────\n\napp.post(\"/api/search\", async (c) => {\n const body = await c.req.json().catch(() => ({})) as {\n brief?: string;\n filters?: Filters;\n size?: number;\n };\n\n const filters: Filters = body.filters ?? {};\n const brief = (body.brief ?? \"\").trim();\n const hasFilters = Object.values(filters).some((v) =>\n v !== undefined && v !== \"\"\n );\n\n if (!hasFilters && !brief) {\n return c.json(\n {\n error:\n \"Provide at least one filter (title, location, skills) or a brief\",\n },\n 400,\n );\n }\n\n const anthropicKey = Deno.env.get(\"ANTHROPIC_KEY\");\n\n try {\n let effectiveFilters: Filters;\n\n if (!hasFilters && brief && anthropicKey) {\n // ── Phase 3a: parse natural-language brief → structured PDL filters ──\n effectiveFilters = await parseBriefToFilters(brief, anthropicKey);\n\n // Safety: if Claude extracted nothing useful, fall back to keyword title search\n const hasAny = Object.values(effectiveFilters).some(\n (v) =>\n v !== undefined && v !== \"\" && (!Array.isArray(v) || v.length > 0),\n );\n if (!hasAny) effectiveFilters = { title: brief };\n } else {\n effectiveFilters = hasFilters ? filters : { title: brief };\n }\n\n const size = Math.max(1, Math.min(25, Number(body.size) || 10));\n const raw = await searchPDL(effectiveFilters, size);\n\n if (brief && raw.length > 0 && anthropicKey) {\n // ── Phase 3b: rank results with Claude ──────────────────────────────\n const ranked = await rankCandidates(raw, brief, anthropicKey);\n return c.json({ candidates: ranked, filters_used: effectiveFilters });\n }\n\n return c.json({ candidates: raw, filters_used: effectiveFilters });\n } catch (e) {\n return c.json({ error: e instanceof Error ? e.message : String(e) }, 500);\n }\n});\n\n// POST /api/outreach — Claude-drafted first-touch message\napp.post(\"/api/outreach\", async (c) => {\n const body = await c.req.json().catch(() => ({})) as {\n candidate?: Candidate;\n brief?: string;\n };\n if (!body.candidate?.name) {\n return c.json({ error: \"candidate required\" }, 400);\n }\n const apiKey = Deno.env.get(\"ANTHROPIC_KEY\");\n if (!apiKey) return c.json({ error: \"ANTHROPIC_KEY not configured\" }, 500);\n\n try {\n const result = await callClaude(\n apiKey,\n \"claude-opus-4-8\",\n 1024,\n {\n name: \"draft_outreach\",\n description: \"Draft a first-touch recruiting email to a candidate.\",\n input_schema: {\n type: \"object\",\n properties: {\n subject: {\n type: \"string\",\n description: \"Short email subject line\",\n },\n message: {\n type: \"string\",\n description: \"The email body, 4-6 sentences, plain text\",\n },\n },\n required: [\"subject\", \"message\"],\n additionalProperties: false,\n },\n },\n `You are an executive recruiter writing a short, warm, honest first-touch message.\\n\\nROLE BRIEF:\\n${\n body.brief || \"(not provided)\"\n }\\n\\nCANDIDATE (real data — do not invent facts beyond it):\\n${\n JSON.stringify(body.candidate)\n }\\n\\nRules: reference ONE specific signal from the candidate's data; 4-6 sentences; no hype, no fake familiarity; be clear you're a recruiter; close with a low-pressure invitation to chat.`,\n );\n return c.json(result);\n } catch (e) {\n return c.json({ error: e instanceof Error ? e.message : String(e) }, 500);\n }\n});\n\n// GET / — serve the frontend (same origin → no CORS, no keys in the page)\napp.get(\"/\", (c) => c.html(INDEX_HTML));\n\n// >>> INDEX_HTML — generated from index.html by embed.py; do not edit by hand\nconst INDEX_HTML = `\n\n\n\n\nFind Candidates — people search\n\n\n\n

Find Candidates · people search

Natural-language brief → ranked candidates from structured people data. All keys stay on the server. No LinkedIn, no scraping.

\n\n

⚙︎ Settings (stored only in this browser)

Access token (required)\n

Backend URL (leave blank when using this page from the backend itself)\n

Results per search (each result uses 1 PDL credit)\n

All API keys (PDL, Anthropic) live on the backend. This page stores only the backend address and your access token in localStorage.

\n\n

\n Describe who you're looking for\n \n\n

⛃ Structured filters (optional — used instead of parsing the brief; the brief still drives ranking)

Job title (write it out in full)

Company

Location

Seniority\n

\n Skills / keywords (comma-separated)\n \n

\n\n

\n \n \n

\n CFO with PE-backed company experience in New York\n VP of Sales for a B2B SaaS company in Austin\n Head of Operations at a logistics company in Chicago\n

\n\n \n

\n\n

\n ★ 0 saved\n \n \n

\n\n\n\n\n`;\n// <<< INDEX_HTML\n\nexport default app.fetch;","/** Find Candidates Backend — Val Town HTTP handler */\n\nimport { Hono } from \"npm:hono\";\nimport { cors } from \"npm:hono/cors\";\n\nconst app = new Hono();\napp.use(\"*\", cors());\n\n// Public — no auth\napp.get(\"/api/health\", (c) => c.json({ ok: true, version: \"0.5.0\" }));\n\n// Bearer-token gate for all /api/* routes registered below\napp.use(\"/api/*\", async (c, next) => {\n const token = Deno.env.get(\"ACCESS_TOKEN\");\n if (!token) return next();\n const auth = c.req.header(\"Authorization\") ?? \"\";\n if (auth !== `Bearer ${token}`) {\n return c.json({ error: \"Unauthorized\" }, 401);\n }\n return next();\n});\n\n// ─── Types ────────────────────────────────────────────────────────────────────\n\ninterface Filters {\n title?: string;\n company?: string;\n past_companies?: string[];\n seniority?: string;\n location?: string;\n industries?: string[];\n company_sizes?: string[];\n skills?: string[];\n}\n\ninterface Candidate {\n name: string;\n html_url: string;\n where: string;\n why: string;\n signals: string[];\n gaps: string[];\n tier: number;\n source: string;\n}\n\n// ─── Claude helpers (raw HTTP — no SDK in Deno/Val Town) ──────────────────────\n\nconst ANTHROPIC_URL = \"https://api.anthropic.com/v1/messages\";\n\n/** Call Claude with tool_choice to force structured JSON output. */\nasync function callClaude(\n apiKey: string,\n model: string,\n maxTokens: number,\n tool: Record,\n userMessage: string,\n): Promise> {\n const toolName = tool.name as string;\n\n const res = await fetch(ANTHROPIC_URL, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"x-api-key\": apiKey,\n \"anthropic-version\": \"2023-06-01\",\n },\n body: JSON.stringify({\n model,\n max_tokens: maxTokens,\n tool_choice: { type: \"tool\", name: toolName },\n tools: [tool],\n messages: [{ role: \"user\", content: userMessage }],\n }),\n });\n\n if (!res.ok) {\n const text = await res.text();\n throw new Error(`Claude ${res.status}: ${text}`);\n }\n\n const data = await res.json() as {\n content: Array<\n { type: string; name: string; input: Record }\n >;\n };\n\n const block = data.content.find(\n (b) => b.type === \"tool_use\" && b.name === toolName,\n );\n if (!block) throw new Error(`Claude returned no ${toolName} call`);\n return block.input;\n}\n\n// ─── Step 1: parse brief → structured filters ─────────────────────────────────\n\nasync function parseBriefToFilters(\n brief: string,\n apiKey: string,\n): Promise {\n const result = await callClaude(\n apiKey,\n \"claude-sonnet-4-6\", // simple extraction — sonnet is sufficient\n 512,\n {\n name: \"extract_filters\",\n description:\n \"Extract structured search filters from a natural-language recruiting brief.\",\n input_schema: {\n type: \"object\",\n properties: {\n title: {\n type: \"string\",\n description:\n \"Full job title — always expand abbreviations (CFO → Chief Financial Officer, CTO → Chief Technology Officer, VP Sales → Vice President of Sales, etc.)\",\n },\n company: {\n type: \"string\",\n description:\n \"Target company name if the brief asks to source from a specific company\",\n },\n seniority: {\n type: \"string\",\n description:\n \"Seniority level, one of: cxo, vp, director, manager, senior, partner, owner, entry\",\n },\n location: {\n type: \"string\",\n description:\n \"City or region — use the simple common form, e.g. 'New York', 'San Francisco' (not 'SF Bay Area')\",\n },\n past_companies: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Previous employers the person must have on their resume — only when the brief asks, e.g. 'ex-Stripe' → ['Stripe'], 'Big 4 background' → ['Deloitte', 'PwC', 'EY', 'KPMG']\",\n },\n industries: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Industries of the candidate's CURRENT company, lowercase LinkedIn/PDL taxonomy. Give 2-4 plausible variants so one wrong guess doesn't zero out the search, e.g. B2B SaaS → ['computer software', 'information technology and services', 'internet']. Only set when the brief states or clearly implies an industry.\",\n },\n company_sizes: {\n type: \"array\",\n items: {\n type: \"string\",\n enum: [\n \"1-10\",\n \"11-50\",\n \"51-200\",\n \"201-500\",\n \"501-1000\",\n \"1001-5000\",\n \"5001-10000\",\n \"10001+\",\n ],\n },\n description:\n \"Employee-count buckets for the current company — only when the brief implies scale, e.g. 'startup' → ['1-10', '11-50', '51-200']. Include adjacent buckets for recall.\",\n },\n skills: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Skills or keywords likely to appear in a professional skills list, e.g. ['private equity', 'financial modeling', 'fundraising']\",\n },\n },\n required: [],\n additionalProperties: false,\n },\n },\n `Extract search filters from this recruiting brief:\\n\\n${brief}`,\n ) as Filters;\n\n // Strip empty / zero-length values so buildPDLQuery gets clean input\n if (!result.title) delete result.title;\n if (!result.company) delete result.company;\n if (!result.past_companies?.length) delete result.past_companies;\n if (!result.seniority) delete result.seniority;\n if (!result.location) delete result.location;\n if (!result.industries?.length) delete result.industries;\n if (!result.company_sizes?.length) delete result.company_sizes;\n if (!result.skills?.length) delete result.skills;\n\n return result;\n}\n\n// ─── Step 2: rank PDL results with Claude ─────────────────────────────────────\n\ninterface RankEntry {\n index: number;\n tier: 1 | 2 | 3;\n why: string;\n gaps: string[];\n}\n\nasync function rankCandidates(\n candidates: Candidate[],\n dossiers: Record[],\n brief: string,\n apiKey: string,\n): Promise {\n if (candidates.length === 0) return [];\n\n // Full dossiers (work history, education, skills, company metadata) — the more\n // evidence the ranker sees, the less it has to guess\n const numbered = dossiers.map((d, i) => ({ index: i, ...d }));\n\n const result = await callClaude(\n apiKey,\n \"claude-opus-4-8\", // quality judgment — use Opus\n 4096, // room for up to 25 ranked entries without clipping\n {\n name: \"rank_candidates\",\n description:\n \"Rank every candidate by fit for a role. Return all with tier, why, and gaps. Sort tier 1 → 2 → 3.\",\n input_schema: {\n type: \"object\",\n properties: {\n ranked: {\n type: \"array\",\n items: {\n type: \"object\",\n properties: {\n index: {\n type: \"integer\",\n description: \"Original 0-based index from the input list\",\n },\n tier: {\n type: \"integer\",\n enum: [1, 2, 3],\n description:\n \"1 = strong fit 2 = potential fit 3 = weak fit\",\n },\n why: {\n type: \"string\",\n description: \"1-2 sentences on fit with the brief\",\n },\n gaps: {\n type: \"array\",\n items: { type: \"string\" },\n description:\n \"Up to 3 missing qualifications or open questions\",\n },\n },\n required: [\"index\", \"tier\", \"why\", \"gaps\"],\n additionalProperties: false,\n },\n },\n },\n required: [\"ranked\"],\n additionalProperties: false,\n },\n },\n `You are an executive recruiter at a PE-focused search firm.\\n\\nROLE BRIEF:\\n${brief}\\n\\nCANDIDATES (${candidates.length} total, real records from a people-data provider):\\n${\n JSON.stringify(numbered)\n }\\n\\nReturn all ${candidates.length} candidates ranked by fit. Use tier 1 (strong), 2 (potential), 3 (weak). Weigh work history and trajectory, not just the current title. Use your own knowledge of the companies named (sector, scale, ownership, PE backing) to inform fit — but keep verified data separate from inference: anything the data doesn't confirm belongs in gaps, not in why. Sort best first.`,\n ) as { ranked: RankEntry[] };\n\n // Merge Claude's scores back onto the original PDL records\n const merged = (result.ranked ?? [])\n .filter((r) => r.index >= 0 && r.index < candidates.length)\n .map((r) => ({\n ...candidates[r.index],\n tier: r.tier,\n why: r.why,\n gaps: r.gaps ?? [],\n }));\n\n // Safety sort in case Claude didn't fully order them\n merged.sort((a, b) => a.tier - b.tier);\n return merged;\n}\n\n// ─── PDL adapter ──────────────────────────────────────────────────────────────\n\n// PDL's job_title_levels vocabulary — map common recruiter phrasing onto it\nconst LEVEL_MAP: Record = {\n \"c-suite\": \"cxo\",\n \"c_suite\": \"cxo\",\n \"c-level\": \"cxo\",\n \"c level\": \"cxo\",\n \"csuite\": \"cxo\",\n \"chief\": \"cxo\",\n \"executive\": \"cxo\",\n \"exec\": \"cxo\",\n \"cxo\": \"cxo\",\n \"vice president\": \"vp\",\n \"vice-president\": \"vp\",\n \"vp\": \"vp\",\n \"junior\": \"entry\",\n};\nfunction normalizeSeniority(s: string): string {\n const k = s.trim().toLowerCase();\n return LEVEL_MAP[k] ?? k.replace(/[\\s-]+/g, \"_\");\n}\n\nfunction buildPDLQuery(filters: Filters, strict = true) {\n const must: unknown[] = [];\n const should: unknown[] = [];\n // Strict mode: location/industry/size are hard filters. The relaxed retry (used when a\n // strict search returns nothing) demotes them to ranking boosts, so a too-narrow parse\n // or a field this plan can't filter on can't zero out the whole search.\n const firmOrSoft = strict ? must : should;\n\n if (filters.title) must.push({ match: { job_title: filters.title } });\n if (filters.company) {\n must.push({ term: { job_company_name: filters.company.toLowerCase() } });\n }\n\n // Past employers — phrase match so \"Goldman Sachs\" doesn't loose-match \"Goldman Properties\"\n for (const pc of filters.past_companies ?? []) {\n must.push({\n match_phrase: { \"experience.company.name\": pc.toLowerCase() },\n });\n }\n\n // Location matched three ways: full location string, city, or state/region\n if (filters.location) {\n const loc = filters.location.toLowerCase();\n firmOrSoft.push({\n bool: {\n should: [\n { match_phrase: { location_name: loc } },\n { term: { location_locality: loc } },\n { term: { location_region: loc } },\n ],\n minimum_should_match: 1,\n },\n });\n }\n\n // terms = match ANY value in the list, so the parse step's 2-4 industry variants keep recall\n if (filters.industries?.length) {\n firmOrSoft.push({\n terms: {\n job_company_industry: filters.industries.map((i) => i.toLowerCase()),\n },\n });\n }\n if (filters.company_sizes?.length) {\n firmOrSoft.push({ terms: { job_company_size: filters.company_sizes } });\n }\n\n // Skip seniority when a specific title is provided — titles like \"CFO\" already imply seniority,\n // and stacking both over-constrains the query.\n if (filters.seniority && !filters.title) {\n must.push({\n term: { job_title_levels: normalizeSeniority(filters.seniority) },\n });\n }\n\n // Skills go in `should` (boost ranking) not `must` (hard filter) — PDL skill tags are\n // single words or short phrases; multi-word descriptors from Claude rarely match exactly.\n if (filters.skills?.length) {\n for (const s of filters.skills) {\n should.push({ term: { skills: s.toLowerCase() } });\n }\n }\n\n if (must.length === 0 && should.length === 0) return { match_all: {} };\n\n const boolQuery: Record = {};\n if (must.length) boolQuery.must = must;\n if (should.length) boolQuery.should = should;\n return { bool: boolQuery };\n}\n\n// PDL returns names lowercased — capitalize for display/CSV\nfunction titleCase(s: string): string {\n return s.replace(/\\b\\p{L}/gu, (ch) => ch.toUpperCase());\n}\n\n// Free-tier PDL redacts some fields by returning booleans — only accept real strings\nfunction str(v: unknown): string {\n return typeof v === \"string\" ? v : \"\";\n}\n\nfunction normalizePerson(p: Record): Candidate {\n const profiles = (p.profiles as Array<{ network: string; url: string }>) ??\n [];\n const github = profiles.find((x) => x.network === \"github\")?.url ?? \"\";\n const twitter = profiles.find((x) => x.network === \"twitter\")?.url ?? \"\";\n\n const title = str(p.job_title);\n const company = str(p.job_company_name);\n const industry = str(p.job_company_industry);\n const size = str(p.job_company_size);\n const skills = ((p.skills as string[]) ?? []).filter((s) =>\n typeof s === \"string\"\n );\n\n const signals: string[] = [];\n if (title) signals.push(title);\n if (company) signals.push(`@ ${company}`);\n if (industry) signals.push(industry);\n if (size) signals.push(`${size} employees`);\n signals.push(...skills.slice(0, 3));\n\n const rawUrl = github || twitter || \"\"; // never surface LinkedIn\n const where = str(p.location_name) ||\n [str(p.location_locality), str(p.location_region)].filter(Boolean).join(\n \", \",\n );\n\n return {\n name: titleCase(str(p.full_name) || \"Unknown\"),\n html_url: rawUrl && !/^https?:\\/\\//i.test(rawUrl)\n ? `https://${rawUrl}`\n : rawUrl,\n where,\n why: `${title}${company ? ` at ${company}` : \"\"}`,\n signals,\n gaps: [],\n tier: 2, // overwritten by rankCandidates\n source: \"pdl\",\n };\n}\n\n// Everything potentially useful for ranking, pulled from the raw PDL record.\n// Redacted fields drop out automatically (str() returns \"\" → undefined → omitted by JSON).\nfunction buildDossier(p: Record): Record {\n const exp = ((p.experience as Array>) ?? [])\n .slice(0, 6)\n .map((e) => ({\n title: str((e.title as Record | undefined)?.name),\n company: str((e.company as Record | undefined)?.name),\n start: str(e.start_date) || undefined,\n end: str(e.end_date) || \"present\",\n }))\n .filter((e) => e.title || e.company);\n\n const edu = ((p.education as Array>) ?? [])\n .slice(0, 3)\n .map((e) => ({\n school: str((e.school as Record | undefined)?.name),\n degrees: ((e.degrees as string[]) ?? []).filter((d) =>\n typeof d === \"string\"\n ),\n }))\n .filter((e) => e.school);\n\n const skills = ((p.skills as string[]) ?? []).filter((s) =>\n typeof s === \"string\"\n ).slice(0, 25);\n\n return {\n name: titleCase(str(p.full_name) || \"Unknown\"),\n title: str(p.job_title) || undefined,\n company: str(p.job_company_name) || undefined,\n company_industry: str(p.job_company_industry) || undefined,\n company_size: str(p.job_company_size) || undefined,\n location: str(p.location_name) || undefined,\n years_experience: typeof p.inferred_years_experience === \"number\"\n ? p.inferred_years_experience\n : undefined,\n summary: str(p.summary).slice(0, 400) || undefined,\n skills: skills.length ? skills : undefined,\n work_history: exp.length ? exp : undefined,\n education: edu.length ? edu : undefined,\n };\n}\n\ninterface SearchResult {\n candidates: Candidate[];\n dossiers: Record[];\n}\n\nasync function searchPDL(\n filters: Filters,\n size = 10,\n strict = true,\n): Promise {\n const apiKey = Deno.env.get(\"PDL_API_KEY\");\n if (!apiKey) throw new Error(\"PDL_API_KEY not configured\");\n\n const res = await fetch(\"https://api.peopledatalabs.com/v5/person/search\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", \"X-Api-Key\": apiKey },\n body: JSON.stringify({\n query: buildPDLQuery(filters, strict),\n size,\n dataset: \"all\",\n }),\n });\n\n // PDL returns 404 when no records match — not an error\n if (res.status === 404) return { candidates: [], dossiers: [] };\n\n if (!res.ok) {\n const text = await res.text();\n throw new Error(`PDL ${res.status}: ${text}`);\n }\n\n const data = await res.json() as { data: Record[] };\n const people = data.data ?? [];\n return {\n candidates: people.map(normalizePerson),\n dossiers: people.map(buildDossier),\n };\n}\n\n// ─── Routes ───────────────────────────────────────────────────────────────────\n\napp.post(\"/api/search\", async (c) => {\n const body = await c.req.json().catch(() => ({})) as {\n brief?: string;\n filters?: Filters;\n size?: number;\n };\n\n const filters: Filters = body.filters ?? {};\n const brief = (body.brief ?? \"\").trim();\n const hasFilters = Object.values(filters).some((v) =>\n v !== undefined && v !== \"\"\n );\n\n if (!hasFilters && !brief) {\n return c.json(\n {\n error:\n \"Provide at least one filter (title, location, skills) or a brief\",\n },\n 400,\n );\n }\n\n const anthropicKey = Deno.env.get(\"ANTHROPIC_KEY\");\n\n try {\n let effectiveFilters: Filters;\n\n if (!hasFilters && brief && anthropicKey) {\n // ── Phase 3a: parse natural-language brief → structured PDL filters ──\n effectiveFilters = await parseBriefToFilters(brief, anthropicKey);\n\n // Safety: if Claude extracted nothing useful, fall back to keyword title search\n const hasAny = Object.values(effectiveFilters).some(\n (v) =>\n v !== undefined && v !== \"\" && (!Array.isArray(v) || v.length > 0),\n );\n if (!hasAny) effectiveFilters = { title: brief };\n } else {\n effectiveFilters = hasFilters ? filters : { title: brief };\n }\n\n const size = Math.max(1, Math.min(25, Number(body.size) || 10));\n let { candidates: raw, dossiers } = await searchPDL(effectiveFilters, size);\n let relaxed = false;\n\n // Strict filters can over-constrain — retry once with location/industry/size\n // demoted to ranking boosts. A zero-result search costs no PDL credits, so the\n // failed strict attempt is free.\n const hadHardFilters = !!(\n effectiveFilters.location ||\n effectiveFilters.industries?.length ||\n effectiveFilters.company_sizes?.length\n );\n if (raw.length === 0 && hadHardFilters) {\n ({ candidates: raw, dossiers } = await searchPDL(\n effectiveFilters,\n size,\n false,\n ));\n relaxed = true;\n }\n\n if (brief && raw.length > 0 && anthropicKey) {\n // ── Rank results with Claude, using the full dossiers ───────────────\n const ranked = await rankCandidates(raw, dossiers, brief, anthropicKey);\n return c.json({\n candidates: ranked,\n filters_used: effectiveFilters,\n relaxed,\n });\n }\n\n return c.json({ candidates: raw, filters_used: effectiveFilters, relaxed });\n } catch (e) {\n return c.json({ error: e instanceof Error ? e.message : String(e) }, 500);\n }\n});\n\n// POST /api/outreach — Claude-drafted first-touch message\napp.post(\"/api/outreach\", async (c) => {\n const body = await c.req.json().catch(() => ({})) as {\n candidate?: Candidate;\n brief?: string;\n };\n if (!body.candidate?.name) {\n return c.json({ error: \"candidate required\" }, 400);\n }\n const apiKey = Deno.env.get(\"ANTHROPIC_KEY\");\n if (!apiKey) return c.json({ error: \"ANTHROPIC_KEY not configured\" }, 500);\n\n try {\n const result = await callClaude(\n apiKey,\n \"claude-opus-4-8\",\n 1024,\n {\n name: \"draft_outreach\",\n description: \"Draft a first-touch recruiting email to a candidate.\",\n input_schema: {\n type: \"object\",\n properties: {\n subject: {\n type: \"string\",\n description: \"Short email subject line\",\n },\n message: {\n type: \"string\",\n description: \"The email body, 4-6 sentences, plain text\",\n },\n },\n required: [\"subject\", \"message\"],\n additionalProperties: false,\n },\n },\n `You are an executive recruiter writing a short, warm, honest first-touch message.\\n\\nROLE BRIEF:\\n${\n body.brief || \"(not provided)\"\n }\\n\\nCANDIDATE (real data — do not invent facts beyond it):\\n${\n JSON.stringify(body.candidate)\n }\\n\\nRules: reference ONE specific signal from the candidate's data; 4-6 sentences; no hype, no fake familiarity; be clear you're a recruiter; close with a low-pressure invitation to chat.`,\n );\n return c.json(result);\n } catch (e) {\n return c.json({ error: e instanceof Error ? e.message : String(e) }, 500);\n }\n});\n\n// GET /api/debug-person — TEMPORARY diagnostic: returns one raw, unprocessed PDL record\n// so we can see exactly which fields this plan includes. Costs 1 credit per call.\n// Token-gated like the rest of /api/*. Remove once the data shape is confirmed.\napp.get(\"/api/debug-person\", async (c) => {\n const apiKey = Deno.env.get(\"PDL_API_KEY\");\n if (!apiKey) return c.json({ error: \"PDL_API_KEY not configured\" }, 500);\n\n const res = await fetch(\"https://api.peopledatalabs.com/v5/person/search\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", \"X-Api-Key\": apiKey },\n body: JSON.stringify({\n query: { match: { job_title: \"chief financial officer\" } },\n size: 1,\n dataset: \"all\",\n }),\n });\n\n const data = await res.json().catch(() => ({}));\n return c.json({ pdl_status: res.status, raw: data });\n});\n\n// GET / — serve the frontend (same origin → no CORS, no keys in the page)\napp.get(\"/\", (c) => c.html(INDEX_HTML));\n\n// >>> INDEX_HTML — generated from index.html by embed.py; do not edit by hand\nconst INDEX_HTML = `\n\n\n\n\nFind Candidates — people search\n\n\n\n

Find Candidates · people search

Natural-language brief → ranked candidates from structured people data. All keys stay on the server. No LinkedIn, no scraping.

\n\n

⚙︎ Settings (stored only in this browser)

Access token (required)\n

Backend URL (leave blank when using this page from the backend itself)\n

Results per search (each result uses 1 PDL credit)\n

All API keys (PDL, Anthropic) live on the backend. This page stores only the backend address and your access token in localStorage.

\n\n

\n Describe who you're looking for\n \n\n

⛃ Structured filters (optional — used instead of parsing the brief; the brief still drives ranking)

Job title (write it out in full)

Company

Location

Seniority\n

\n Skills / keywords (comma-separated)\n \n

\n\n

\n \n \n

\n CFO with PE-backed company experience in New York\n VP of Sales for a B2B SaaS company in Austin\n Head of Operations at a logistics company in Chicago\n

\n\n \n

\n\n

\n ★ 0 saved\n \n \n

\n\n\n\n\n`;\n// <<< INDEX_HTML\n\nexport default app.fetch;","createdAt",["D",1781106063246],"branchVersion","branchId","f0d4749f-5f6f-4826-a8c1-a25df6fbd7f1","id","5994aa15-9916-409e-a2cf-b02c797027e1","findCandidatesBackend",["D",1781022809094],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","","imageUrl","52d79f98-f575-4133-b870-58d2d9bb24bf","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","019ead36-eedf-7478-a0c1-de445d0092b1","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_95":94,"_102":98},"org",{"_95":94,"_96":94,"_97":98,"_18":55,"_99":100,"_101":25},"branch",{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"isOwned",29,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","mhussar3","handle","displayName","avatar","https://avatars.githubusercontent.com/u/188508645?v=4","tier","free","canRestrictVals","profileImageUrl","branchName","adoptOrgId",{"_43":44,"_32":45,"_38":150,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":151,"_79":152,"_81":153,"_83":25},"tree",[145],"files",[145],"counts",{"_133":61,"_134":135,"_136":61,"_137":138,"_139":140},"contents",[125],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles",true,"grants","orgs","customDomains",{},{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":129,"_13":130,"_33":34,"_35":37,"_24":25,"_131":-5,"_27":132},"ee895a9c-5d27-445a-b4d2-8bc4bae64f84","parentId","latest",["D",1781106063246],13,"label",[28],"pulls","branches",1,"forks","envVars",{"_141":143,"_144":61},"connections",{"_141":61,"_142":61},"count","unfilledCount",4,"emptyCount",{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":146,"_13":130,"_33":34,"_35":37,"_24":25,"_131":-5,"_147":148,"_27":149},["D",1781106063246],"children",[],[28],["D",1781022809094],{"_95":94,"_102":98},{"_95":94,"_96":94,"_97":98,"_18":55,"_99":100,"_101":25},{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"ctx",{"_77":-5,"_122":187,"_188":189,"_190":191,"_192":25,"_193":25,"_194":25,"_195":120,"_196":-5,"_197":-7,"_198":-7,"_199":61,"_200":25,"_201":25,"_202":-5,"_203":25,"_204":25,"_205":25,"_206":-5},"ENV",{"_158":159,"_160":161,"_162":85,"_163":164,"_165":166,"_167":168,"_169":170,"_171":172,"_173":174,"_175":176,"_177":178,"_179":180,"_181":182,"_183":184,"_185":186},"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"]

Diff · v14