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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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HEADERS = {\n \"User-Agent\": \"AlexKrasiuk alex@myrunbook.com\",\n \"Accept-Encoding\": \"gzip, deflate\",\n};\n\n// Cache ticker->CIK lookup (refreshes every 24h)\nlet tickerCache: { data: any; 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",[136],"5cc38dc4-3f47-11f1-a505-42b51c65c3df","main.ts","f3c77a2a-3f3b-11f1-9116-42b51c65c3df",5,["D",1776971115652],4,"http","import { scoreHit, normalize, findSection, countByKeyword } from \"./analysis.ts\";\nimport { secFetch, getTickers } from \"./sec-client.ts\";\nimport { KEYWORDS } from \"./keywords.ts\";\nimport { stripHtml } from \"./html.ts\";\n\nconst CONTEXT_CHARS = 600; // ~100 words at ~6 chars/word avg\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 // Also support POST with JSON body: { \"ticker\": \"AAPL\" }\n if (!ticker && req.method === \"POST\") {\n try {\n const body = await req.json();\n ticker = (body.ticker || \"\").toUpperCase().trim();\n } catch { /* ignore parse errors */ }\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 body {\"ticker\": \"AAPL\"}',\n },\n },\n { status: 400 },\n );\n }\n\n try {\n // Step 1: Ticker -> CIK (cached)\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({ error: `Ticker ${ticker} not found in SEC EDGAR` }, { status: 404 });\n }\n\n // Step 2: Find latest 10-K and 10-Q\n const subs = JSON.parse(await secFetch(`https://data.sec.gov/submissions/CIK${cik}.json`));\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/${parseInt(cik)}/${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 ${ticker}` }, { status: 404 });\n }\n\n // Step 3: Process each filing\n type Hit = { keyword: string; context: string; section: string; score: number; filing_type: string };\n const allHits: Hit[] = [];\n\n for (const filing of filings) {\n const html = await secFetch(filing.url);\n const text = stripHtml(html);\n\n // Find keywords with context (char-offset based)\n for (const kw of KEYWORDS) {\n const pattern = new RegExp(\"\\\\b\" + kw.replace(/[.*+?^${}()|[\\]\\\\]/g, \"\\\\$&\") + \"\\\\b\", \"gi\");\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(text.length, match.index + kw.length + CONTEXT_CHARS);\n const context = text.slice(charStart, charEnd).trim();\n const section = findSection(text, match.index);\n const score = scoreHit(context);\n\n allHits.push({\n keyword: kw,\n context,\n section,\n score,\n filing_type: filing.type,\n });\n }\n }\n }\n\n // Dedupe by normalized context (300-char window)\n const seen = new Set();\n const unique: Hit[] = [];\n for (const h of allHits) {\n const key = normalize(h.context).slice(0, 400);\n if (!seen.has(key)) {\n seen.add(key);\n unique.push(h);\n }\n }\n\n // Sort by score descending\n unique.sort((a, b) => b.score - a.score);\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: unique.length,\n keyword_counts: countByKeyword(unique),\n top_signals: unique.slice(0, 5),\n all_hits: unique.slice(0, 40),\n });\n } catch (e: any) {\n return Response.json({ error: e.message }, { status: 500 });\n }\n}\n",[146],"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",[155],"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, 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Diff · v9