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ts: number } | null = null;\nconst CACHE_TTL_MS = 24 * 60 * 60 * 1000;\n\nexport async function getTickers(): Promise {\n if (tickerCache && Date.now() - tickerCache.ts < CACHE_TTL_MS) {\n return tickerCache.data;\n }\n const data = JSON.parse(await secFetch(\"https://www.sec.gov/files/company_tickers.json\"));\n tickerCache = { data, ts: Date.now() };\n return data;\n}\n\n// Fetch with retry + backoff for SEC rate limits (10 req/s)\nexport async function secFetch(url: string, retries = 3): Promise {\n for (let i = 0; i < retries; i++) {\n const resp = await fetch(url, { headers: HEADERS });\n if (resp.ok) return resp.text();\n if (resp.status === 429) {\n await new Promise(r => setTimeout(r, 1000 * (i + 1)));\n continue;\n }\n throw new Error(`SEC fetch failed: ${resp.status} ${resp.statusText} for ${url}`);\n }\n throw new Error(`SEC fetch failed after ${retries} retries for ${url}`);\n}\n","label",[130],"26b7874a-3f50-11f1-84b6-42b51c65c3df",["D",1776974890453],[27],"db940c94-3f49-11f1-8b7e-42b51c65c3df","main.ts","f3c77a2a-3f3b-11f1-9116-42b51c65c3df",13,["D",1776972187387],"http","// ============================================================================\n// SEC Filings Signal Scraper — v4 (catch-all mode)\n// Philosophy: capture everything that might be signal. Don't filter.\n// Let the downstream LLM decide what matters.\n// ============================================================================\n\nconst HEADERS = {\n \"User-Agent\": \"AlexKrasiuk alex@myrunbook.com\",\n \"Accept-Encoding\": \"gzip, deflate\",\n};\n\nconst CONTEXT_CHARS = 800; // WIDER context — give LLM more to work with\nconst MAX_HITS_RETURNED = 150; // Cap payload size, not per-keyword\n\n// Broad keyword list — cast a wide net\nconst KEYWORDS = [\n // AI / ML\n \"artificial intelligence\",\n \"AI agent\",\n \"AI agents\",\n \"generative AI\",\n \"gen AI\",\n \"GenAI\",\n \"machine learning\",\n \"large language model\",\n \"LLM\",\n \"AI-enabled\",\n \"AI-powered\",\n \"AI initiative\",\n \"AI technology\",\n \"AI tool\",\n \"neural network\",\n \"deep learning\",\n\n // Headcount / workforce\n \"headcount reduction\",\n \"headcount reductions\",\n \"workforce reduction\",\n \"reduction in force\",\n \"reduction-in-force\",\n \"workforce optimization\",\n \"restructuring\",\n \"severance\",\n \"severance charges\",\n \"layoff\",\n \"layoffs\",\n \"job cut\",\n \"overhead reduction\",\n \"workforce transformation\",\n \"reorganization\",\n \"reorganize\",\n\n // Back office / ops\n \"back office\",\n \"back-office\",\n \"back office function\",\n \"shared services\",\n \"shared service\",\n \"shared service center\",\n \"administrative function\",\n \"administrative functions\",\n \"centralize\",\n \"centralization\",\n \"offshore\",\n \"offshoring\",\n \"outsourc\",\n\n // Automation\n \"process automation\",\n \"robotic process automation\",\n \"RPA\",\n \"automate\",\n \"automated\",\n \"automation\",\n \"AI-enabled automation\",\n \"intelligent automation\",\n \"workflow automation\",\n \"workflow tool\",\n\n // Transformation\n \"digital transformation\",\n \"technology transformation\",\n \"digital initiative\",\n \"digital strategy\",\n \"modernize\",\n \"modernization\",\n \"technology investment\",\n \"technology upgrade\",\n\n // Efficiency\n \"process optimization\",\n \"process improvement\",\n \"operational efficiency\",\n \"operational excellence\",\n \"productivity improvement\",\n \"productivity gain\",\n \"cost savings\",\n \"cost reduction\",\n \"cost optimization\",\n \"margin expansion\",\n \"margin improvement\",\n \"do more with less\",\n \"scale without\",\n \"efficiency initiative\",\n \"efficiency program\",\n\n // Manual work\n \"manual processes\",\n \"manual work\",\n \"manual task\",\n \"streamline\",\n \"streamlined\",\n \"streamlining\",\n\n // Field ops / dispatch-adjacent (Runbook-specific)\n \"dispatch\",\n \"scheduling\",\n \"route optimization\",\n \"field service\",\n \"field operations\",\n \"field technician\",\n \"service technician\",\n \"work order\",\n \"customer service center\",\n \"call center\",\n \"contact center\",\n\n // Financial pressure tells\n \"margin pressure\",\n \"inflation\",\n \"wage inflation\",\n \"labor cost\",\n \"labor shortage\",\n];\n\n// ============================================================================\n// Ticker cache (24h)\n// ============================================================================\n\nlet tickerCache: { data: any; ts: number } | null = null;\nconst CACHE_TTL_MS = 24 * 60 * 60 * 1000;\n\nasync function getTickers(): Promise {\n if (tickerCache && Date.now() - tickerCache.ts < CACHE_TTL_MS) {\n return tickerCache.data;\n }\n const data = JSON.parse(\n await secFetch(\"https://www.sec.gov/files/company_tickers.json\"),\n );\n tickerCache = { data, ts: Date.now() };\n return data;\n}\n\n// ============================================================================\n// SEC fetch with 429 retry\n// ============================================================================\n\nasync function secFetch(url: string, retries = 3): Promise {\n for (let i = 0; i < retries; i++) {\n const resp = await fetch(url, { headers: HEADERS });\n if (resp.ok) return resp.text();\n if (resp.status === 429) {\n await new Promise((r) => setTimeout(r, 1000 * (i + 1)));\n continue;\n }\n throw new Error(`SEC fetch failed: ${resp.status} for ${url}`);\n }\n throw new Error(`SEC fetch failed after ${retries} retries for ${url}`);\n}\n\n// ============================================================================\n// HTML stripping\n// ============================================================================\n\nfunction stripHtml(html: string): string {\n let text = html.replace(/]*>.*?<\\/script>/gis, \" \");\n text = text.replace(/]*>.*?<\\/style>/gis, \" \");\n text = text.replace(/<\\/?ix:[^>]+>/gi, \" \");\n text = text.replace(/<[^>]+>/g, \" \");\n text = text\n .replace(/&/g, \"&\").replace(/</g, \"<\").replace(/>/g, \">\")\n .replace(/"/g, '\"').replace(/'/g, \"'\").replace(/ /g, \" \")\n .replace(/&#\\d+;/g, \" \").replace(/&[a-zA-Z]+;/g, \" \");\n text = text.replace(/\\s+/g, \" \").trim();\n return text;\n}\n\n// ============================================================================\n// Analysis helpers\n// ============================================================================\n\nfunction normalize(s: string): string {\n return s.toLowerCase().replace(/[^a-z0-9\\s]/g, \"\").replace(/\\s+/g, \" \")\n .trim();\n}\n\nfunction findSection(text: string, matchIdx: number): string {\n const before = text.slice(Math.max(0, matchIdx - 10000), matchIdx);\n const sectionMatches = [\n ...before.matchAll(\n /(?:^|\\.\\s+|\\n\\s*)Item\\s+(\\d+[A-Z]?)\\.\\s+([A-Z][A-Za-z\\s&',\\-]{3,80}?)(?=\\s*(?:\\.|\\n|Table of Contents))/g,\n ),\n ];\n if (sectionMatches.length === 0) return \"Unknown\";\n const last = sectionMatches[sectionMatches.length - 1];\n const title = last[2].trim().replace(/\\s+/g, \" \").replace(/\\s+of the$/i, \"\");\n return `Item ${last[1]} - ${title}`;\n}\n\n// Score is INFORMATIONAL ONLY — never used to filter\n// Gives the downstream LLM a hint about which passages are likely richest\nfunction scoreHit(context: string, section: string, keyword: string): number {\n const lower = context.toLowerCase();\n let score = 0;\n\n // Strategic neighbor words\n const neighbors = [\n \"initiative\",\n \"invest\",\n \"deploy\",\n \"implement\",\n \"launch\",\n \"reduce\",\n \"reduction\",\n \"eliminate\",\n \"transform\",\n \"strategy\",\n \"pilot\",\n \"acquired\",\n \"acquisition\",\n \"partnership\",\n \"committed\",\n \"priority\",\n \"focus\",\n \"accelerate\",\n \"expand\",\n ];\n for (const w of neighbors) if (lower.includes(w)) score += 1;\n\n // Section hints\n const s = section.toLowerCase();\n if (s.startsWith(\"item 7 \")) score += 3;\n if (s.startsWith(\"item 1a\")) score += 2;\n if (s.startsWith(\"item 2 \") && s.includes(\"management\")) score += 2;\n\n // Mega-signal patterns\n const hasStreamline = /\\bstreamlin/i.test(lower);\n const hasHeadcount =\n /\\b(headcount|severance|workforce reduction|reduction in force|layoff)/i\n .test(lower);\n const hasDollar = /\\$\\s*\\d+(?:\\.\\d+)?\\s*(?:million|billion|M|B)\\b/i.test(\n context,\n );\n const hasBackOffice =\n /\\b(back[\\s-]?office|administrative function|shared service)/i.test(lower);\n\n if (hasStreamline && hasHeadcount) score += 8;\n if (hasStreamline && hasDollar) score += 4;\n if (hasHeadcount && hasDollar) score += 4;\n if (hasBackOffice) score += 3;\n\n return score;\n}\n\n// EXACT dedupe only — remove only bit-for-bit duplicates\n// Preserves near-duplicates because they may contain slightly different useful context\nfunction buildDedupeKey(context: string): string {\n return normalize(context);\n}\n\nfunction countByKeyword(hits: { keyword: string }[]): Record {\n const counts: Record = {};\n for (const h of hits) counts[h.keyword] = (counts[h.keyword] || 0) + 1;\n return counts;\n}\n\n// ============================================================================\n// Main handler\n// ============================================================================\n\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n let ticker = (url.searchParams.get(\"ticker\") || \"\").toUpperCase().trim();\n\n if (!ticker && req.method === \"POST\") {\n try {\n const body = await req.json();\n ticker = (body.ticker || \"\").toUpperCase().trim();\n } catch { /* ignore */ }\n }\n\n if (!ticker) {\n return Response.json(\n {\n error: 'Pass ?ticker=AAPL or POST with {\"ticker\": \"AAPL\"}',\n usage: {\n get: \"GET /?ticker=AAPL\",\n post: 'POST / with {\"ticker\": \"AAPL\"}',\n },\n },\n { status: 400 },\n );\n }\n\n try {\n // Step 1: Ticker -> CIK\n const tickersJson = await getTickers();\n let cik: string | null = null;\n let company: string | null = null;\n for (const entry of Object.values(tickersJson) as any[]) {\n if (entry.ticker.toUpperCase() === ticker) {\n cik = String(entry.cik_str).padStart(10, \"0\");\n company = entry.title;\n break;\n }\n }\n if (!cik) {\n return Response.json(\n { error: `Ticker ${ticker} not found in SEC EDGAR` },\n { status: 404 },\n );\n }\n\n // Step 2: Latest 10-K and 10-Q\n const subs = JSON.parse(\n await secFetch(`https://data.sec.gov/submissions/CIK${cik}.json`),\n );\n const recent = subs.filings.recent;\n\n type FilingInfo = { url: string; date: string; type: string };\n const filings: FilingInfo[] = [];\n\n let found10K = false;\n let found10Q = false;\n for (let i = 0; i < recent.form.length; i++) {\n const form = recent.form[i];\n if ((form === \"10-K\" && !found10K) || (form === \"10-Q\" && !found10Q)) {\n const acc = recent.accessionNumber[i].replace(/-/g, \"\");\n const doc = recent.primaryDocument[i];\n filings.push({\n url: `https://www.sec.gov/Archives/edgar/data/${\n parseInt(cik)\n }/${acc}/${doc}`,\n date: recent.filingDate[i],\n type: form,\n });\n if (form === \"10-K\") found10K = true;\n if (form === \"10-Q\") found10Q = true;\n if (found10K && found10Q) break;\n }\n }\n\n if (filings.length === 0) {\n return Response.json({ error: `No 10-K or 10-Q found for ${ticker}` }, {\n status: 404,\n });\n }\n\n // Step 3: Scan each filing\n type Hit = {\n keyword: string;\n context: string;\n section: string;\n score: number;\n filing_type: string;\n filing_date: string;\n };\n const allHits: Hit[] = [];\n\n for (const filing of filings) {\n const html = await secFetch(filing.url);\n const text = stripHtml(html);\n\n for (const kw of KEYWORDS) {\n const pattern = new RegExp(\n \"\\\\b\" + kw.replace(/[.*+?^${}()|[\\]\\\\]/g, \"\\\\$&\") + \"\\\\b\",\n \"gi\",\n );\n let match;\n while ((match = pattern.exec(text)) !== null) {\n const charStart = Math.max(0, match.index - CONTEXT_CHARS);\n const charEnd = Math.min(\n text.length,\n match.index + kw.length + CONTEXT_CHARS,\n );\n const context = text.slice(charStart, charEnd).trim();\n const section = findSection(text, match.index);\n const score = scoreHit(context, section, kw);\n\n allHits.push({\n keyword: kw,\n context,\n section,\n score,\n filing_type: filing.type,\n filing_date: filing.date,\n });\n }\n }\n }\n\n // EXACT dedupe only (bit-for-bit identical passages)\n const seen = new Set();\n const unique: Hit[] = [];\n for (const h of allHits) {\n const key = buildDedupeKey(h.context);\n if (!seen.has(key)) {\n seen.add(key);\n unique.push(h);\n }\n }\n\n // Sort by score so LLM sees best stuff first — but DON'T filter\n unique.sort((a, b) => b.score - a.score);\n\n // Only cap is total payload size, not per-keyword\n const returned = unique.slice(0, MAX_HITS_RETURNED);\n\n return Response.json({\n ticker,\n company,\n filings: filings.map((f) => ({ type: f.type, date: f.date, url: f.url })),\n total_hits_found: unique.length,\n total_hits_returned: returned.length,\n keyword_counts: countByKeyword(unique),\n hits: returned, // flat list, sorted by score, no structure games\n });\n } catch (e: any) {\n return Response.json({ error: e.message }, { status: 500 });\n }\n}",[143],"61195cdc-3f47-11f1-a505-42b51c65c3df","keywords.ts","61195db8-3f47-11f1-a505-42b51c65c3df",6,["D",1776971122935],"export const KEYWORDS = [\n // AI / ML\n \"artificial intelligence\", \"AI agent\", \"AI agents\",\n \"artificial intelligence initiative\",\n \"generative AI\", \"gen AI\", \"GenAI\",\n \"machine learning\", \"large language model\", \"LLM\",\n \"AI-enabled\", \"AI-powered\",\n\n // Workforce\n \"headcount reduction\", \"workforce reduction\", \"reduction in force\",\n \"workforce optimization\", \"do more with less\",\n\n // Operations / Automation\n \"back office\", \"back-office\",\n \"process automation\", \"robotic process automation\", \"RPA\",\n \"digital transformation\", \"technology transformation\",\n \"process optimization\", \"operational efficiency\",\n \"productivity improvement\",\n \"manual processes\", \"manual work\",\n \"automate\", \"automation\", \"streamline\",\n \"modernize\", \"modernization\",\n \"shared services\",\n\n // Cost\n \"cost savings\", \"cost reduction\",\n \"restructuring\",\n \"scale without\",\n];\n",[151],"64beae46-3f47-11f1-a505-42b51c65c3df","html.ts","64beaf0e-3f47-11f1-a505-42b51c65c3df",7,["D",1776971129053],"export function stripHtml(html: string): string {\n // Remove scripts and styles\n let text = html.replace(/]*>.*?<\\/script>/gis, \" \");\n text = text.replace(/]*>.*?<\\/style>/gis, \" \");\n // Strip XBRL inline tags (SEC filings have these)\n text = text.replace(/<\\/?ix:[^>]+>/gi, \" \");\n // Remove all tags\n text = text.replace(/<[^>]+>/g, \" \");\n // Unescape common HTML entities\n text = text.replace(/&/g, \"&\")\n .replace(/</g, \"<\")\n .replace(/>/g, \">\")\n .replace(/"/g, '\"')\n .replace(/'/g, \"'\")\n .replace(/ /g, \" \")\n .replace(/&#\\d+;/g, \" \")\n .replace(/&[a-zA-Z]+;/g, \" \");\n // Collapse whitespace\n text = text.replace(/\\s+/g, \" \").trim();\n return text;\n}\n",[158],"708b6160-3f47-11f1-829c-42b51c65c3df","analysis.ts","708b6200-3f47-11f1-829c-42b51c65c3df",9,["D",1776971148838],"// High-value neighbor words that indicate strategic intent (not boilerplate)\nconst HIGH_VALUE_NEIGHBORS = [\n \"initiative\", \"invest\", \"deploy\", \"implement\", \"launch\",\n \"reduce\", \"reduction\", \"eliminate\", \"transform\", \"strategy\",\n \"pilot\", \"roll out\", \"acquired\", \"acquisition\", \"partnership\",\n \"committed\", \"priority\", \"focus\", \"budget\", \"spend\",\n \"savings\", \"efficiency\", \"headcount\", \"workforce\",\n \"restructuring\", \"consolidat\", \"migrat\",\n];\n\n// Sections that indicate strategic commentary (higher signal)\nconst HIGH_VALUE_SECTIONS = [\n \"Item 7\", // MD&A\n \"Item 1A\", // Risk Factors\n \"Item 2\", // Properties (sometimes has ops strategy)\n];\n\nexport function scoreHit(context: string, section?: string): number {\n const lower = context.toLowerCase();\n let score = HIGH_VALUE_NEIGHBORS.filter(w => lower.includes(w)).length;\n\n // Bonus for high-signal sections\n if (section) {\n for (const s of HIGH_VALUE_SECTIONS) {\n if (section.startsWith(s)) {\n score += 2;\n break;\n }\n }\n }\n\n return score;\n}\n\nexport function normalize(s: string): string {\n return s.toLowerCase().replace(/[^a-z0-9\\s]/g, \"\").replace(/\\s+/g, \" \").trim();\n}\n\n// Find the nearest preceding \"Item X\" section header\nexport function findSection(text: string, matchIdx: number): string {\n const before = text.slice(Math.max(0, matchIdx - 10000), matchIdx);\n const sectionMatches = [...before.matchAll(/Item\\s+(\\d+[A-Z]?)\\.?\\s+([A-Z][A-Za-z\\s&']{3,60})/g)];\n if (sectionMatches.length === 0) return \"Unknown\";\n const last = sectionMatches[sectionMatches.length - 1];\n return `Item ${last[1]} - ${last[2].trim()}`;\n}\n\nexport function countByKeyword(hits: { keyword: string }[]): Record {\n const counts: Record = {};\n for (const h of hits) {\n counts[h.keyword] = (counts[h.keyword] || 0) + 1;\n }\n return 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Diff · v14