init

analyze-patterns.mjs

@@ -0,0 +1,863 @@
+#!/usr/bin/env node
+/**
+ * analyze-patterns.mjs — Rejection Pattern Detector for career-ops
+ *
+ * Parses applications.md + all linked reports, extracts dimensions
+ * (archetype, seniority, remote, gaps, scores), classifies outcomes,
+ * and outputs structured JSON with actionable patterns.
+ *
+ * Run: node analyze-patterns.mjs          (JSON to stdout)
+ *      node analyze-patterns.mjs --summary (human-readable table)
+ *      node analyze-patterns.mjs --min-threshold 3
+ *      node analyze-patterns.mjs --min-vendor-n 8   (per-vendor sample floor)
+ *      node analyze-patterns.mjs --self-test
+ */
+
+import { readFileSync, existsSync } from 'fs';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+import { load as yamlLoad } from 'js-yaml';
+import { resolveColumns, parseTrackerRow } from './tracker-parse.mjs';
+
+const CAREER_OPS = dirname(fileURLToPath(import.meta.url));
+const APPS_FILE = existsSync(join(CAREER_OPS, 'data/applications.md'))
+  ? join(CAREER_OPS, 'data/applications.md')
+  : join(CAREER_OPS, 'applications.md');
+const REPORTS_DIR = join(CAREER_OPS, 'reports');
+
+const MACHINE_SUMMARY_FIELDS = new Set([
+  'company',
+  'role',
+  'score',
+  'legitimacy_tier',
+  'archetype',
+  'final_decision',
+  'hard_stops',
+  'soft_gaps',
+  'top_strengths',
+  'risk_level',
+  'confidence',
+  'next_action',
+  // Optional context fields accepted for future reports.
+  'domain',
+  'seniority',
+  'remote',
+  'team_size',
+]);
+
+// --- CLI args ---
+const args = process.argv.slice(2);
+const summaryMode = args.includes('--summary');
+const minThresholdIdx = args.indexOf('--min-threshold');
+const MIN_THRESHOLD = minThresholdIdx !== -1 && args[minThresholdIdx + 1] !== undefined
+  ? (Number.isNaN(parseInt(args[minThresholdIdx + 1])) ? 5 : parseInt(args[minThresholdIdx + 1]))
+  : 5;
+
+// Minimum per-vendor sample before a channel-yield recommendation fires. Kept
+// modest (small trackers) but high enough that one unlucky bucket isn't a claim.
+const minVendorNIdx = args.indexOf('--min-vendor-n');
+const MIN_VENDOR_N = (() => {
+  if (minVendorNIdx === -1 || args[minVendorNIdx + 1] === undefined) return 8;
+  const n = parseInt(args[minVendorNIdx + 1], 10);
+  // Reject 0/negative: a floor of 0 makes sufficientSample always true and
+  // silently defeats the "don't claim on noise" guard the whole feature rests on.
+  return Number.isNaN(n) || n < 1 ? 8 : n;
+})();
+
+// --- Status normalization (mirrors verify-pipeline.mjs) ---
+const ALIASES = {
+  'evaluada': 'evaluated', 'condicional': 'evaluated', 'hold': 'evaluated',
+  'evaluar': 'evaluated', 'verificar': 'evaluated',
+  'aplicado': 'applied', 'enviada': 'applied', 'aplicada': 'applied',
+  'applied': 'applied', 'sent': 'applied',
+  'respondido': 'responded',
+  'entrevista': 'interview',
+  'oferta': 'offer',
+  'rechazado': 'rejected', 'rechazada': 'rejected',
+  'descartado': 'discarded', 'descartada': 'discarded',
+  'cerrada': 'discarded', 'cancelada': 'discarded',
+  'no aplicar': 'skip', 'no_aplicar': 'skip', 'monitor': 'skip', 'geo blocker': 'skip',
+};
+
+function normalizeStatus(raw) {
+  const clean = raw.replace(/\*\*/g, '').trim().toLowerCase()
+    .replace(/\s+\d{4}-\d{2}-\d{2}.*$/, '').trim();
+  return ALIASES[clean] || clean;
+}
+
+function classifyOutcome(status) {
+  const s = normalizeStatus(status);
+  if (['interview', 'offer', 'responded', 'applied'].includes(s)) return 'positive';
+  if (['rejected', 'discarded'].includes(s)) return 'negative';
+  if (['skip'].includes(s)) return 'self_filtered';
+  return 'pending'; // evaluated
+}
+
+function normalizeList(value) {
+  if (Array.isArray(value)) return value.map(v => String(v).trim()).filter(Boolean);
+  if (value === null || value === undefined || value === '') return [];
+  if (typeof value === 'object') return [];
+  return [String(value).trim()].filter(Boolean);
+}
+
+function normalizeScalar(value) {
+  if (typeof value === 'string') return value.trim() || null;
+  if (typeof value === 'number' && Number.isFinite(value)) return String(value);
+  return null;
+}
+
+function parseMachineSummary(content) {
+  const fenceMatch = content.match(/##\s*Machine Summary\s*\n+```(?:yaml|yml|json)?\s*\n([\s\S]*?)\n```/i);
+  if (!fenceMatch) return null;
+
+  const raw = fenceMatch[1].trim();
+  if (!raw) return null;
+
+  try {
+    const parsed = yamlLoad(raw);
+    if (!parsed || typeof parsed !== 'object' || Array.isArray(parsed)) return null;
+    return Object.fromEntries(
+      Object.entries(parsed).filter(([key]) => MACHINE_SUMMARY_FIELDS.has(key))
+    );
+  } catch {
+    return null;
+  }
+}
+
+function runSelfTest() {
+  const summary = parseMachineSummary(`
+## Machine Summary
+
+\`\`\`yaml
+company: "Acme"
+role: "Staff AI Engineer"
+score: 4.4
+legitimacy_tier: "High Confidence"
+archetype: "AI Platform / LLMOps Engineer"
+final_decision: "Apply"
+hard_stops: []
+soft_gaps:
+  - "No direct healthcare domain experience"
+top_strengths:
+  - "Production evaluation pipelines"
+risk_level: "Medium"
+confidence: "High"
+next_action: "Follow up on ticket #42 with tailored CV"
+\`\`\`
+`);
+
+  const failures = [];
+  if (!summary) failures.push('summary was not parsed');
+  if (summary?.score !== 4.4) failures.push('numeric score was not parsed');
+  if (!Array.isArray(summary?.hard_stops) || summary.hard_stops.length !== 0) failures.push('empty list was not parsed');
+  if (summary?.soft_gaps?.[0] !== 'No direct healthcare domain experience') failures.push('list item was not parsed');
+  if (summary?.next_action !== 'Follow up on ticket #42 with tailored CV') failures.push('hash-containing scalar field was not parsed');
+
+  // Vendor detection (community ATS only; white-labeled → null)
+  const vendorCases = [
+    ['https://boards.greenhouse.io/acme/jobs/12345', 'greenhouse'],
+    ['https://job-boards.eu.greenhouse.io/acme/jobs/9', 'greenhouse'],
+    ['https://jobs.lever.co/acme/abc-def', 'lever'],
+    ['https://jobs.ashbyhq.com/acme/uuid', 'ashby'],
+    ['https://acme.wd1.myworkdayjobs.com/en-US/careers/job/R-1', 'workday'],
+    ['https://careers.icims.com/jobs/9/x', null],
+    ['https://jobs.dayforcehcm.com/en-US/co/CANDIDATEPORTAL/jobs/1', null],
+    ['not a url', null],
+    ['', null],
+    [null, null],
+  ];
+  for (const [url, expected] of vendorCases) {
+    const got = detectVendor(url);
+    if (got !== expected) failures.push(`detectVendor(${JSON.stringify(url)}) → ${JSON.stringify(got)}, expected ${JSON.stringify(expected)}`);
+  }
+
+  if (failures.length > 0) {
+    console.error(`analyze-patterns self-test failed: ${failures.join('; ')}`);
+    process.exit(1);
+  }
+
+  console.log('analyze-patterns self-test OK (Machine Summary parser + vendor detection)');
+  process.exit(0);
+}
+
+// --- Parse applications.md ---
+function parseTracker() {
+  if (!existsSync(APPS_FILE)) return [];
+  const content = readFileSync(APPS_FILE, 'utf-8');
+  const lines = content.split('\n');
+  const colmap = resolveColumns(lines);
+  const entries = [];
+  for (const line of lines) {
+    const row = parseTrackerRow(line, colmap);
+    if (row) entries.push(row);
+  }
+  return entries;
+}
+
+// --- Parse a single report file ---
+function parseReport(reportPath) {
+  if (!existsSync(reportPath)) return null;
+  const content = readFileSync(reportPath, 'utf-8');
+  const report = {
+    company: null,
+    role: null,
+    url: null,
+    archetype: null,
+    legitimacyTier: null,
+    finalDecision: null,
+    seniority: null,
+    remote: null,
+    teamSize: null,
+    comp: null,
+    domain: null,
+    riskLevel: null,
+    confidence: null,
+    nextAction: null,
+    topStrengths: [],
+    scores: {},
+    gaps: [],
+  };
+
+  const machineSummary = parseMachineSummary(content);
+  if (machineSummary) {
+    report.machineSummary = machineSummary;
+    report.company = normalizeScalar(machineSummary.company) || report.company;
+    report.role = normalizeScalar(machineSummary.role) || report.role;
+    report.archetype = normalizeScalar(machineSummary.archetype) || report.archetype;
+    report.legitimacyTier = normalizeScalar(machineSummary.legitimacy_tier) || report.legitimacyTier;
+    report.finalDecision = normalizeScalar(machineSummary.final_decision) || report.finalDecision;
+    report.domain = normalizeScalar(machineSummary.domain) || report.domain;
+    report.seniority = normalizeScalar(machineSummary.seniority) || report.seniority;
+    report.remote = normalizeScalar(machineSummary.remote) || report.remote;
+    report.teamSize = normalizeScalar(machineSummary.team_size) || report.teamSize;
+    report.riskLevel = normalizeScalar(machineSummary.risk_level) || report.riskLevel;
+    report.confidence = normalizeScalar(machineSummary.confidence) || report.confidence;
+    report.nextAction = normalizeScalar(machineSummary.next_action) || report.nextAction;
+    report.topStrengths = normalizeList(machineSummary.top_strengths);
+
+    if (typeof machineSummary.score === 'number') {
+      report.scores.global = machineSummary.score;
+    }
+
+    for (const hardStop of normalizeList(machineSummary.hard_stops)) {
+      report.gaps.push({ description: hardStop, severity: 'hard stop', mitigation: '' });
+    }
+    for (const softGap of normalizeList(machineSummary.soft_gaps)) {
+      report.gaps.push({ description: softGap, severity: 'soft gap', mitigation: '' });
+    }
+  }
+
+  // Strip bold markers for easier matching
+  const plain = content.replace(/\*\*/g, '');
+
+  // Extract Block A table (Role Summary) — works with both EN and ES headers
+  // Archetype cell may be labeled "Archetype", "Arquetipo", or "Detected archetype" (drift from EN translation).
+  const blockARegex = /\|\s*(?:Detected\s+)?(?:Archetype|Arquetipo)\s*\|\s*(.*?)\s*\|/i;
+  const seniorityRegex = /\|\s*(?:Seniority|Nivel|Level)\s*\|\s*(.*?)\s*\|/i;
+  const remoteRegex = /\|\s*(?:Remote|Remoto|Location)\s*\|\s*(.*?)\s*\|/i;
+  const teamRegex = /\|\s*(?:Team|Team size|Equipo)\s*\|\s*(.*?)\s*\|/i;
+  const compRegex = /\|\s*(?:Comp|Salary|Salario|Listed salary)\s*\|\s*(.*?)\s*\|/i;
+  const domainRegex = /\|\s*(?:Domain|Dominio|Industry)\s*\|\s*(.*?)\s*\|/i;
+
+  // Fallback: report header field `Archetype: ...` or `Arquetipo: ...` (newer reports use this).
+  const headerArchRegex = /^(?:Archetype|Arquetipo):\s*(.+?)$/im;
+
+  // Report header carries `**URL:**` between Score and PDF (see CLAUDE.md /
+  // Pipeline Integrity). Capture the first http(s) URL on that line for vendor
+  // detection; reports predating the field simply leave url null (→ unknown bucket).
+  const urlMatch = plain.match(/^URL:\s*(https?:\/\/\S+)/im);
+  if (urlMatch && !report.url) report.url = urlMatch[1].trim().replace(/[)>\].,]+$/, '');
+
+  const archMatch = plain.match(blockARegex) || plain.match(headerArchRegex);
+  if (archMatch && !report.archetype) report.archetype = archMatch[1].trim();
+
+  const senMatch = plain.match(seniorityRegex);
+  if (senMatch && !report.seniority) report.seniority = senMatch[1].trim();
+
+  const remMatch = plain.match(remoteRegex);
+  if (remMatch && !report.remote) report.remote = remMatch[1].trim();
+
+  const teamMatch = plain.match(teamRegex);
+  if (teamMatch && !report.teamSize) report.teamSize = teamMatch[1].trim();
+
+  const compMatch = plain.match(compRegex);
+  if (compMatch && !report.comp) report.comp = compMatch[1].trim();
+
+  const domainMatch = plain.match(domainRegex);
+  if (domainMatch && !report.domain) report.domain = domainMatch[1].trim();
+
+  // Extract scoring table — look for table with "Global" row (using plain, bold already stripped)
+  const scoreRegex = /\|\s*(?:CV Match|Match con CV)\s*\|\s*([\d.]+)\/5\s*\|/i;
+  const northStarRegex = /\|\s*(?:North Star)\s*\|\s*([\d.]+)\/5\s*\|/i;
+  const compScoreRegex = /\|\s*(?:Comp)\s*\|\s*([\d.]+)\/5\s*\|/i;
+  const culturalRegex = /\|\s*(?:Cultural signals|Cultural)\s*\|\s*([\d.]+)\/5\s*\|/i;
+  const redFlagsRegex = /\|\s*(?:Red flags)\s*\|\s*([-+]?[\d.]+)\s*\|/i;
+  const globalRegex = /\|\s*(?:Global)\s*\|\s*([\d.]+)\/5\s*\|/i;
+
+  const cvScoreMatch = plain.match(scoreRegex);
+  if (cvScoreMatch && report.scores.cvMatch === undefined) report.scores.cvMatch = parseFloat(cvScoreMatch[1]);
+
+  const nsMatch = plain.match(northStarRegex);
+  if (nsMatch && report.scores.northStar === undefined) report.scores.northStar = parseFloat(nsMatch[1]);
+
+  const csMatch = plain.match(compScoreRegex);
+  if (csMatch && report.scores.comp === undefined) report.scores.comp = parseFloat(csMatch[1]);
+
+  const culMatch = plain.match(culturalRegex);
+  if (culMatch && report.scores.cultural === undefined) report.scores.cultural = parseFloat(culMatch[1]);
+
+  const rfMatch = plain.match(redFlagsRegex);
+  if (rfMatch && report.scores.redFlags === undefined) report.scores.redFlags = parseFloat(rfMatch[1]);
+
+  const glMatch = plain.match(globalRegex);
+  if (glMatch && report.scores.global === undefined) report.scores.global = parseFloat(glMatch[1]);
+
+  // Extract gaps table
+  const gapTableRegex = /\|\s*Gap\s*\|\s*Severity\s*\|.*?\n\|[-|\s]+\n([\s\S]*?)(?:\n\n|\n##|\n\*\*|$)/i;
+  const gapTableMatch = content.match(gapTableRegex);
+  if (gapTableMatch) {
+    const gapRows = gapTableMatch[1].split('\n').filter(r => r.startsWith('|'));
+    for (const row of gapRows) {
+      const cols = row.split('|').map(s => s.trim()).filter(Boolean);
+      if (cols.length >= 2) {
+        const duplicate = report.gaps.some(g => g.description.toLowerCase() === cols[0].toLowerCase());
+        if (!duplicate) {
+          report.gaps.push({
+            description: cols[0],
+            severity: cols[1].toLowerCase(),
+            mitigation: cols[2] || '',
+          });
+        }
+      }
+    }
+  }
+
+  return report;
+}
+
+// --- Classify remote policy into buckets ---
+function classifyRemote(raw) {
+  if (!raw) return 'unknown';
+  const lower = raw.toLowerCase();
+  // Order matters: check geo-restricted before general remote
+  if (/\b(us[- ]?only|canada[- ]?only|residents only|usa only|us residents|canada residents)\b/.test(lower)) return 'geo-restricted';
+  if (/\bargentina\s+remote\s+only\b/.test(lower)) return 'geo-restricted';
+  if (/\b(hybrid|on-?site|office|columbus|cape town|relocat)\b/.test(lower)) return 'hybrid/onsite';
+  if (/\b(global|anywhere|worldwide|no restrict|70\+|work from anywhere)\b/.test(lower)) return 'global remote';
+  if (/\b(remote|latam|americas|brazil|fully remote)\b/.test(lower)) return 'regional remote';
+  return 'unknown';
+}
+
+// --- Detect ATS vendor from a posting URL ---
+// Host-only match, deliberately looser than liveness-api.mjs's resolveAtsApi()
+// (which needs the full posting path to build an API URL) — a tracker report's
+// URL may point at a board/careers page, not a canonical posting.
+//
+// SCOPE (intentional): only community ATS with clean, public URL fingerprints —
+// Greenhouse, Lever, Ashby, Workday. White-labeled ATS (iCIMS/UKG/Dayforce) are
+// NOT detectable from the URL alone and are deferred until the community adds a
+// reliable signal (e.g. confirmation-email domain). Undetected → 'unknown'.
+const VENDOR_HOST_PATTERNS = [
+  { id: 'greenhouse', test: (h) => /(^|\.)greenhouse\.io$/.test(h) },
+  { id: 'lever',      test: (h) => h === 'jobs.lever.co' || h.endsWith('.lever.co') },
+  { id: 'ashby',      test: (h) => h === 'jobs.ashbyhq.com' || h.endsWith('.ashbyhq.com') },
+  { id: 'workday',    test: (h) => h.endsWith('.myworkdayjobs.com') || h.endsWith('.myworkdaysite.com') },
+];
+
+function detectVendor(rawUrl) {
+  if (!rawUrl || typeof rawUrl !== 'string') return null;
+  let u;
+  try { u = new URL(rawUrl.trim()); } catch { return null; }
+  if (u.protocol !== 'https:' && u.protocol !== 'http:') return null;
+  const host = u.hostname.toLowerCase();
+  for (const v of VENDOR_HOST_PATTERNS) if (v.test(host)) return v.id;
+  return null;
+}
+
+// --- Classify company size ---
+function classifyCompanySize(teamSize) {
+  if (!teamSize) return 'unknown';
+  const lower = teamSize.toLowerCase();
+  // Extract numbers
+  const nums = lower.match(/[\d,]+/g);
+  if (nums) {
+    const max = Math.max(...nums.map(n => parseInt(n.replace(/,/g, ''))));
+    if (max <= 50) return 'startup';
+    if (max <= 500) return 'scaleup';
+    return 'enterprise';
+  }
+  if (/\b(small|elite|tiny|founding)\b/.test(lower)) return 'startup';
+  if (/\b(large|enterprise|global)\b/.test(lower)) return 'enterprise';
+  return 'unknown';
+}
+
+// --- Extract hard blocker keywords from gaps ---
+function extractBlockerType(gap) {
+  const desc = gap.description.toLowerCase();
+  const sev = gap.severity.toLowerCase();
+  if (sev.includes('nice') || sev.includes('soft')) return null; // skip soft gaps
+  if (/\b(residency|us[- ]only|canada|location|visa|geo|country|region)\b/.test(desc)) return 'geo-restriction';
+  if (/\b(javascript|typescript|python|ruby|java|go|rust|node|react|angular|vue|django|flask|rails)\b/.test(desc)) return 'stack-mismatch';
+  if (/\b(senior|staff|lead|principal|director|manager|head)\b/.test(desc)) return 'seniority-mismatch';
+  if (/\b(hybrid|on-?site|office|relocat)\b/.test(desc)) return 'onsite-requirement';
+  return 'other';
+}
+
+// --- Main analysis ---
+function analyze() {
+  const entries = parseTracker();
+
+  if (entries.length === 0) {
+    return { error: 'No applications found in tracker.' };
+  }
+
+  // Enrich entries with report data and classification
+  const enriched = entries.map(e => {
+    const reportMatch = e.report.match(/\]\(([^)]+)\)/);
+    const reportPath = reportMatch ? join(CAREER_OPS, reportMatch[1]) : null;
+    const reportData = reportPath ? parseReport(reportPath) : null;
+    const outcome = classifyOutcome(e.status);
+    const trackerScore = parseFloat(e.score);
+    const score = Number.isFinite(trackerScore)
+      ? trackerScore
+      : (Number.isFinite(reportData?.scores?.global) ? reportData.scores.global : 0);
+
+    // Fallback: if report didn't have Remote field, try the notes column
+    const remoteSource = reportData?.remote || e.notes || '';
+    const teamSource = reportData?.teamSize || '';
+
+    return {
+      ...e,
+      normalizedStatus: normalizeStatus(e.status),
+      outcome,
+      score,
+      report: reportData,
+      remoteBucket: classifyRemote(remoteSource),
+      companySize: classifyCompanySize(teamSource),
+      vendor: detectVendor(reportData?.url),
+    };
+  });
+
+  // Count entries beyond "Evaluated"
+  const beyondEvaluated = enriched.filter(e => e.normalizedStatus !== 'evaluated');
+  if (beyondEvaluated.length < MIN_THRESHOLD) {
+    return {
+      error: `Not enough data: ${beyondEvaluated.length}/${MIN_THRESHOLD} applications beyond "Evaluated". Keep applying and come back later.`,
+      current: beyondEvaluated.length,
+      threshold: MIN_THRESHOLD,
+    };
+  }
+
+  // --- Funnel ---
+  const funnel = {};
+  for (const e of enriched) {
+    const s = e.normalizedStatus;
+    funnel[s] = (funnel[s] || 0) + 1;
+  }
+
+  // --- Score comparison by outcome ---
+  const scoresByOutcome = { positive: [], negative: [], self_filtered: [], pending: [] };
+  for (const e of enriched) {
+    if (e.score > 0) scoresByOutcome[e.outcome].push(e.score);
+  }
+
+  const scoreStats = (arr) => {
+    if (arr.length === 0) return { avg: 0, min: 0, max: 0, count: 0 };
+    const avg = arr.reduce((a, b) => a + b, 0) / arr.length;
+    return {
+      avg: Math.round(avg * 100) / 100,
+      min: Math.min(...arr),
+      max: Math.max(...arr),
+      count: arr.length,
+    };
+  };
+
+  const scoreComparison = {
+    positive: scoreStats(scoresByOutcome.positive),
+    negative: scoreStats(scoresByOutcome.negative),
+    self_filtered: scoreStats(scoresByOutcome.self_filtered),
+    pending: scoreStats(scoresByOutcome.pending),
+  };
+
+  // --- Archetype breakdown ---
+  const archetypeMap = new Map();
+  for (const e of enriched) {
+    const arch = e.report?.archetype || 'Unknown';
+    if (!archetypeMap.has(arch)) archetypeMap.set(arch, { total: 0, positive: 0, negative: 0, self_filtered: 0, pending: 0 });
+    const entry = archetypeMap.get(arch);
+    entry.total++;
+    entry[e.outcome]++;
+  }
+  const archetypeBreakdown = [...archetypeMap.entries()].map(([archetype, data]) => ({
+    archetype,
+    ...data,
+    conversionRate: data.total > 0 ? Math.round((data.positive / data.total) * 100) : 0,
+  })).sort((a, b) => b.total - a.total);
+
+  // --- Blocker analysis ---
+  const blockerCounts = new Map();
+  const totalWithGaps = enriched.filter(e => e.report?.gaps?.length > 0);
+  for (const e of enriched) {
+    if (!e.report?.gaps) continue;
+    for (const gap of e.report.gaps) {
+      const type = extractBlockerType(gap);
+      if (!type) continue;
+      blockerCounts.set(type, (blockerCounts.get(type) || 0) + 1);
+    }
+  }
+  const blockerAnalysis = [...blockerCounts.entries()]
+    .map(([blocker, frequency]) => ({
+      blocker,
+      frequency,
+      percentage: Math.round((frequency / enriched.length) * 100),
+    }))
+    .sort((a, b) => b.frequency - a.frequency);
+
+  // --- Remote policy breakdown ---
+  const remoteMap = new Map();
+  for (const e of enriched) {
+    const policy = e.remoteBucket;
+    if (!remoteMap.has(policy)) remoteMap.set(policy, { total: 0, positive: 0, negative: 0, self_filtered: 0, pending: 0 });
+    const entry = remoteMap.get(policy);
+    entry.total++;
+    entry[e.outcome]++;
+  }
+  const remotePolicy = [...remoteMap.entries()].map(([policy, data]) => ({
+    policy,
+    ...data,
+    conversionRate: data.total > 0 ? Math.round((data.positive / data.total) * 100) : 0,
+  })).sort((a, b) => b.total - a.total);
+
+  // --- Company size breakdown ---
+  const sizeMap = new Map();
+  for (const e of enriched) {
+    const size = e.companySize;
+    if (!sizeMap.has(size)) sizeMap.set(size, { total: 0, positive: 0, negative: 0, self_filtered: 0, pending: 0 });
+    const entry = sizeMap.get(size);
+    entry.total++;
+    entry[e.outcome]++;
+  }
+  const companySizeBreakdown = [...sizeMap.entries()].map(([size, data]) => ({
+    size,
+    ...data,
+    conversionRate: data.total > 0 ? Math.round((data.positive / data.total) * 100) : 0,
+  })).sort((a, b) => b.total - a.total);
+
+  // --- ATS vendor / channel analysis (algorithmic-monoculture aware) ---
+  // Motivation: Bommasani et al., "Algorithmic Monocultures in Hiring" (FAccT
+  // 2026, arXiv:2605.27371) — rejections routed through a shared screening
+  // vendor are correlated, not independent. If a concentrated channel yields
+  // nothing, feeding it the same profile has diminishing returns; the rational
+  // move is to divert those companies to referral/direct contact.
+  //
+  // HONESTY: this reports CHANNEL YIELD, not discrimination. A single tracker
+  // can't causally separate "the vendor's algorithm filters me" from "that
+  // vendor skews toward a segment I fit poorly" — but "stop feeding a dead
+  // channel, go around it" is rational under either explanation.
+  //
+  // "Advanced" here is STRICTER than the outcome=='positive' bucket: a bare
+  // 'applied' (submitted, no reply yet) does NOT count as passing screening.
+  const ADVANCED_STATUSES = new Set(['responded', 'interview', 'offer']);
+  const SUBMITTED_STATUSES = new Set(['applied', 'responded', 'interview', 'offer', 'rejected', 'discarded']);
+  const isAdvanced = (e) => ADVANCED_STATUSES.has(e.normalizedStatus);
+
+  // Only applications we actually submitted count toward channel yield (drop
+  // 'evaluated' = never applied, and 'skip' = self-filtered).
+  const submitted = enriched.filter(e => SUBMITTED_STATUSES.has(e.normalizedStatus));
+  const overallAdvanced = submitted.filter(isAdvanced).length;
+  const overallAdvanceRate = submitted.length > 0
+    ? Math.round((overallAdvanced / submitted.length) * 100) : 0;
+
+  const vendorMap = new Map();
+  for (const e of submitted) {
+    const v = e.vendor || 'unknown';
+    if (!vendorMap.has(v)) vendorMap.set(v, { total: 0, advanced: 0 });
+    const entry = vendorMap.get(v);
+    entry.total++;
+    if (isAdvanced(e)) entry.advanced++;
+  }
+
+  // Recommendations only fire on buckets with enough n to not be noise; the
+  // breakdown still SHOWS every bucket (with its n) so nothing is hidden.
+  const vendorBreakdown = [...vendorMap.entries()]
+    .filter(([v]) => v !== 'unknown')
+    .map(([vendor, data]) => ({
+      vendor,
+      total: data.total,
+      advanced: data.advanced,
+      advanceRate: data.total > 0 ? Math.round((data.advanced / data.total) * 100) : 0,
+      sharePct: submitted.length > 0 ? Math.round((data.total / submitted.length) * 100) : 0,
+      sufficientSample: data.total >= MIN_VENDOR_N,
+    }))
+    .sort((a, b) => b.total - a.total);
+
+  const identifiedCount = submitted.length - (vendorMap.get('unknown')?.total || 0);
+  const vendorAnalysis = {
+    scope: ['greenhouse', 'lever', 'ashby', 'workday'],
+    minSampleForClaim: MIN_VENDOR_N,
+    submitted: submitted.length,
+    identified: identifiedCount,
+    coveragePct: submitted.length > 0 ? Math.round((identifiedCount / submitted.length) * 100) : 0,
+    overallAdvanceRate,
+    breakdown: vendorBreakdown,
+    citation: 'Bommasani et al., Algorithmic Monocultures in Hiring, FAccT 2026 (arXiv:2605.27371)',
+  };
+
+  // --- Score threshold analysis ---
+  const positiveScores = scoresByOutcome.positive.filter(s => s > 0);
+  const minPositiveScore = positiveScores.length > 0 ? Math.min(...positiveScores) : 0;
+  const scoreThreshold = {
+    recommended: minPositiveScore > 0 ? Math.floor(minPositiveScore * 10) / 10 : 3.5,
+    reasoning: positiveScores.length > 0
+      ? `Lowest score among positive outcomes is ${minPositiveScore}. No applications below this score led to progress.`
+      : 'Not enough positive outcome data to determine threshold.',
+    positiveRange: positiveScores.length > 0
+      ? `${Math.min(...positiveScores)} - ${Math.max(...positiveScores)}`
+      : 'N/A',
+  };
+
+  // --- Tech stack gaps (from negative + self_filtered outcomes) ---
+  // Canonical spellings keyed by lowercased match — the /i regex below returns
+  // the source casing ("react native", "NODEJS"), and without this map each
+  // case variant of the same tech lands in its own techStackGaps bucket.
+  // Keys cover the optional-dot regex variants (node.js/nodejs, vue.js/vuejs).
+  const TECH_CANONICAL = new Map([
+    'JavaScript', 'TypeScript', 'Python', 'Ruby', 'Java', 'Go', 'Rust',
+    'React Native', 'React', 'Angular', 'Django', 'Flask', 'Rails', 'PHP',
+    'Laravel', 'Symfony', 'Kotlin', 'Swift', 'C++', 'C#', '.NET', 'MongoDB',
+    'MySQL', 'PostgreSQL', 'Redis', 'GraphQL', 'REST', 'AWS', 'GCP', 'Azure',
+    'Docker', 'Kubernetes', 'Terraform', 'Supabase', 'Inngest',
+  ].map(t => [t.toLowerCase(), t]));
+  TECH_CANONICAL.set('node.js', 'Node.js').set('nodejs', 'Node.js');
+  TECH_CANONICAL.set('vue.js', 'Vue.js').set('vuejs', 'Vue.js');
+  const stackGapCounts = new Map();
+  for (const e of enriched) {
+    if (e.outcome !== 'negative' && e.outcome !== 'self_filtered') continue;
+    if (!e.report?.gaps) continue;
+    for (const gap of e.report.gaps) {
+      // Extract tech keywords from gap descriptions
+      const techs = gap.description.match(/\b(JavaScript|TypeScript|Python|Ruby|Java|Go|Rust|Node\.?js|React Native|React|Angular|Vue\.?js|Django|Flask|Rails|PHP|Laravel|Symfony|Kotlin|Swift|C\+\+|C#|\.NET|MongoDB|MySQL|PostgreSQL|Redis|GraphQL|REST|AWS|GCP|Azure|Docker|Kubernetes|Terraform|Supabase|Inngest)\b/gi);
+      if (techs) {
+        for (const tech of techs) {
+          const normalized = TECH_CANONICAL.get(tech.toLowerCase()) || tech;
+          stackGapCounts.set(normalized, (stackGapCounts.get(normalized) || 0) + 1);
+        }
+      }
+    }
+  }
+  const techStackGaps = [...stackGapCounts.entries()]
+    .map(([skill, frequency]) => ({ skill, frequency }))
+    .sort((a, b) => b.frequency - a.frequency)
+    .slice(0, 15);
+
+  // --- Generate recommendations ---
+  const recommendations = [];
+
+  // Geo-restriction recommendation
+  const geoBlocker = blockerAnalysis.find(b => b.blocker === 'geo-restriction');
+  if (geoBlocker && geoBlocker.percentage >= 20) {
+    recommendations.push({
+      action: `Tighten location filters in portals.yml -- ${geoBlocker.percentage}% of applications hit a geo-restriction blocker`,
+      reasoning: `${geoBlocker.frequency} of ${enriched.length} offers are location-restricted (US/Canada-only). These are wasted evaluation effort.`,
+      impact: 'high',
+    });
+  }
+
+  // Stack mismatch recommendation
+  const stackBlocker = blockerAnalysis.find(b => b.blocker === 'stack-mismatch');
+  if (stackBlocker && stackBlocker.percentage >= 15) {
+    const topGaps = techStackGaps.slice(0, 3).map(g => g.skill).join(', ');
+    recommendations.push({
+      action: `Filter out roles requiring ${topGaps} as primary stack -- ${stackBlocker.percentage}% hit stack mismatch`,
+      reasoning: `Core stack gaps (${topGaps}) are the most common technical blockers in negative outcomes.`,
+      impact: 'high',
+    });
+  }
+
+  // Score threshold recommendation
+  if (minPositiveScore > 3.0) {
+    recommendations.push({
+      action: `Set minimum score threshold at ${scoreThreshold.recommended}/5 before generating PDFs`,
+      reasoning: `No positive outcomes below ${minPositiveScore}/5. Scores below this are wasted effort.`,
+      impact: 'medium',
+    });
+  }
+
+  // Best archetype recommendation
+  const bestArchetype = archetypeBreakdown.filter(a => a.total >= 2).sort((a, b) => b.conversionRate - a.conversionRate)[0];
+  if (bestArchetype && bestArchetype.conversionRate > 0) {
+    recommendations.push({
+      action: `Double down on "${bestArchetype.archetype}" roles (${bestArchetype.conversionRate}% conversion rate)`,
+      reasoning: `${bestArchetype.positive} of ${bestArchetype.total} applications in this archetype led to positive outcomes.`,
+      impact: 'medium',
+    });
+  }
+
+  // Remote policy recommendation
+  const bestRemote = remotePolicy.filter(r => r.total >= 2).sort((a, b) => b.conversionRate - a.conversionRate)[0];
+  const worstRemote = remotePolicy.filter(r => r.total >= 2 && r.conversionRate === 0)[0];
+  if (worstRemote) {
+    recommendations.push({
+      action: `Avoid "${worstRemote.policy}" roles (0% conversion across ${worstRemote.total} applications)`,
+      reasoning: `None of the ${worstRemote.total} applications with "${worstRemote.policy}" policy led to progress.`,
+      impact: 'medium',
+    });
+  }
+
+  // Channel-monoculture recommendation: a concentrated vendor (>= 25% of
+  // submissions, sufficient sample) whose advance rate is well below EVERY OTHER
+  // channel is a dead channel worth routing around, not re-feeding. The baseline
+  // is leave-one-out (this vendor vs all other submissions) — comparing to an
+  // overall rate that INCLUDES the vendor understates the gap when it dominates.
+  let deadChannel = null;
+  for (const v of vendorBreakdown) {
+    if (!v.sufficientSample || v.sharePct < 25) continue;
+    const others = submitted.filter(e => (e.vendor || 'unknown') !== v.vendor);
+    if (others.length === 0) continue;
+    const othersRate = Math.round((others.filter(isAdvanced).length / others.length) * 100);
+    // Meaningful gap only: the rest of the pipeline must be doing at least
+    // moderately better, so we're not flagging a uniformly cold market.
+    if (v.advanceRate < othersRate && othersRate - v.advanceRate >= 10) {
+      if (!deadChannel || v.advanceRate < deadChannel.advanceRate) deadChannel = { ...v, othersRate };
+    }
+  }
+  if (deadChannel) {
+    recommendations.push({
+      action: `Route ${deadChannel.vendor} companies through referral / direct contact -- ${deadChannel.sharePct}% of your applications flow through it at a ${deadChannel.advanceRate}% advance rate (vs ${deadChannel.othersRate}% through other channels)`,
+      reasoning: `${deadChannel.advanced}/${deadChannel.total} ${deadChannel.vendor} applications advanced past screening, well below your other channels. Under algorithmic monoculture (Bommasani et al., FAccT 2026) a shared screener's rejections are correlated -- re-applying the same profile through the same engine has diminishing returns; a human channel bypasses it. Channel yield, not a discrimination claim.`,
+      impact: 'high',
+    });
+  }
+
+  // Date range
+  const dates = enriched.map(e => e.date).filter(Boolean).sort();
+
+  return {
+    metadata: {
+      total: enriched.length,
+      dateRange: { from: dates[0], to: dates[dates.length - 1] },
+      analysisDate: new Date().toISOString().split('T')[0],
+      byOutcome: {
+        positive: enriched.filter(e => e.outcome === 'positive').length,
+        negative: enriched.filter(e => e.outcome === 'negative').length,
+        self_filtered: enriched.filter(e => e.outcome === 'self_filtered').length,
+        pending: enriched.filter(e => e.outcome === 'pending').length,
+      },
+    },
+    funnel,
+    scoreComparison,
+    archetypeBreakdown,
+    blockerAnalysis,
+    remotePolicy,
+    companySizeBreakdown,
+    vendorAnalysis,
+    scoreThreshold,
+    techStackGaps,
+    recommendations,
+  };
+}
+
+// --- Summary mode (human-readable) ---
+function printSummary(result) {
+  if (result.error) {
+    console.log(`\n${result.error}\n`);
+    return;
+  }
+
+  const { metadata, funnel, scoreComparison, archetypeBreakdown, blockerAnalysis, remotePolicy, scoreThreshold, techStackGaps, recommendations } = result;
+
+  console.log(`\n${'='.repeat(60)}`);
+  console.log(`  Pattern Analysis — ${metadata.analysisDate}`);
+  console.log(`  ${metadata.total} applications (${metadata.dateRange.from} to ${metadata.dateRange.to})`);
+  console.log(`${'='.repeat(60)}\n`);
+
+  // Funnel
+  console.log('CONVERSION FUNNEL');
+  console.log('-'.repeat(40));
+  const funnelOrder = ['evaluated', 'applied', 'responded', 'interview', 'offer', 'rejected', 'discarded', 'skip'];
+  for (const status of funnelOrder) {
+    if (funnel[status]) {
+      const pct = Math.round((funnel[status] / metadata.total) * 100);
+      console.log(`  ${status.padEnd(15)} ${String(funnel[status]).padStart(3)} (${pct}%)`);
+    }
+  }
+
+  // Score comparison
+  console.log('\nSCORE BY OUTCOME');
+  console.log('-'.repeat(40));
+  for (const [group, stats] of Object.entries(scoreComparison)) {
+    if (stats.count > 0) {
+      console.log(`  ${group.padEnd(15)} avg ${stats.avg}/5  (${stats.count} entries, range ${stats.min}-${stats.max})`);
+    }
+  }
+
+  // Blockers
+  if (blockerAnalysis.length > 0) {
+    console.log('\nTOP BLOCKERS');
+    console.log('-'.repeat(40));
+    for (const b of blockerAnalysis) {
+      console.log(`  ${b.blocker.padEnd(20)} ${String(b.frequency).padStart(2)}x (${b.percentage}% of all)`);
+    }
+  }
+
+  // Remote policy
+  console.log('\nREMOTE POLICY');
+  console.log('-'.repeat(40));
+  for (const r of remotePolicy) {
+    console.log(`  ${r.policy.padEnd(20)} ${String(r.total).padStart(2)} total, ${r.positive} positive (${r.conversionRate}%)`);
+  }
+
+  // Tech gaps
+  if (techStackGaps.length > 0) {
+    console.log('\nTOP TECH STACK GAPS (negative outcomes)');
+    console.log('-'.repeat(40));
+    for (const g of techStackGaps.slice(0, 10)) {
+      console.log(`  ${g.skill.padEnd(20)} ${g.frequency}x`);
+    }
+  }
+
+  // ATS vendor / channel analysis
+  const va = result.vendorAnalysis;
+  if (va && va.breakdown.length > 0) {
+    console.log('\nATS CHANNEL ANALYSIS (community ATS only)');
+    console.log('-'.repeat(40));
+    console.log(`  vendor identified for ${va.identified}/${va.submitted} submissions (${va.coveragePct}% coverage); overall advance rate ${va.overallAdvanceRate}%`);
+    for (const v of va.breakdown) {
+      const flag = v.sufficientSample ? '' : '  (n too small for a claim)';
+      console.log(`  ${v.vendor.padEnd(12)} ${String(v.total).padStart(3)} apps  ${String(v.sharePct).padStart(3)}% share  ${String(v.advanceRate).padStart(3)}% advance${flag}`);
+    }
+    console.log('  Channel yield, not discrimination — see Bommasani et al., FAccT 2026.');
+  }
+
+  // Score threshold
+  console.log(`\nSCORE THRESHOLD: ${scoreThreshold.recommended}/5`);
+  console.log(`  ${scoreThreshold.reasoning}`);
+
+  // Recommendations
+  if (recommendations.length > 0) {
+    console.log(`\nRECOMMENDATIONS`);
+    console.log('='.repeat(60));
+    for (let i = 0; i < recommendations.length; i++) {
+      const r = recommendations[i];
+      console.log(`  ${i + 1}. [${r.impact.toUpperCase()}] ${r.action}`);
+      console.log(`     ${r.reasoning}`);
+    }
+  }
+
+  console.log('');
+}
+
+// --- Run ---
+if (args.includes('--self-test')) {
+  runSelfTest();
+}
+
+const result = analyze();
+
+if (summaryMode) {
+  printSummary(result);
+} else {
+  console.log(JSON.stringify(result, null, 2));
+}
+
+if (result.error) process.exit(1);