wehub-resource-sync
1011 lines
44 KiB
JavaScript
1011 lines
44 KiB
JavaScript
#!/usr/bin/env node
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/**
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* analyze-patterns.mjs — Rejection Pattern Detector for career-ops
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*
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* Parses applications.md + all linked reports, extracts dimensions
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* (archetype, seniority, remote, gaps, scores), classifies outcomes,
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* and outputs structured JSON with actionable patterns.
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*
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* Run: node analyze-patterns.mjs (JSON to stdout)
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* node analyze-patterns.mjs --summary (human-readable table)
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* node analyze-patterns.mjs --min-threshold 3
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* node analyze-patterns.mjs --min-vendor-n 8 (per-vendor sample floor)
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* node analyze-patterns.mjs --self-test
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*/
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import { readFileSync, existsSync } from 'fs';
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import { join, dirname, relative, sep } from 'path';
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import { fileURLToPath } from 'url';
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import { load as yamlLoad } from 'js-yaml';
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import { resolveColumns, parseTrackerRow, normalizeVia } from './tracker-parse.mjs';
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const CAREER_OPS = dirname(fileURLToPath(import.meta.url));
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const APPS_FILE = existsSync(join(CAREER_OPS, 'data/applications.md'))
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? join(CAREER_OPS, 'data/applications.md')
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: join(CAREER_OPS, 'applications.md');
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const REPORTS_DIR = join(CAREER_OPS, 'reports');
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const MACHINE_SUMMARY_FIELDS = new Set([
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'company',
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'role',
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'score',
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'legitimacy_tier',
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'archetype',
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'final_decision',
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'hard_stops',
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'soft_gaps',
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'top_strengths',
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'risk_level',
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'confidence',
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'next_action',
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// Optional context fields accepted for future reports.
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'domain',
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'seniority',
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'remote',
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'team_size',
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'advertised_comp',
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'via',
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'company_confidential',
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]);
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// --- CLI args ---
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const args = process.argv.slice(2);
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const summaryMode = args.includes('--summary');
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const minThresholdIdx = args.indexOf('--min-threshold');
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const MIN_THRESHOLD = minThresholdIdx !== -1 && args[minThresholdIdx + 1] !== undefined
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? (Number.isNaN(parseInt(args[minThresholdIdx + 1])) ? 5 : parseInt(args[minThresholdIdx + 1]))
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: 5;
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// Minimum per-vendor sample before a channel-yield recommendation fires. Kept
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// modest (small trackers) but high enough that one unlucky bucket isn't a claim.
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const minVendorNIdx = args.indexOf('--min-vendor-n');
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const MIN_VENDOR_N = (() => {
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if (minVendorNIdx === -1 || args[minVendorNIdx + 1] === undefined) return 8;
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const n = parseInt(args[minVendorNIdx + 1], 10);
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// Reject 0/negative: a floor of 0 makes sufficientSample always true and
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// silently defeats the "don't claim on noise" guard the whole feature rests on.
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return Number.isNaN(n) || n < 1 ? 8 : n;
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})();
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// --- Status normalization (mirrors verify-pipeline.mjs) ---
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const ALIASES = {
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'evaluada': 'evaluated', 'condicional': 'evaluated', 'hold': 'evaluated',
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'evaluar': 'evaluated', 'verificar': 'evaluated',
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'aplicado': 'applied', 'enviada': 'applied', 'aplicada': 'applied',
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'applied': 'applied', 'sent': 'applied',
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'respondido': 'responded',
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'entrevista': 'interview',
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'oferta': 'offer',
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'rechazado': 'rejected', 'rechazada': 'rejected',
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'descartado': 'discarded', 'descartada': 'discarded',
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'cerrada': 'discarded', 'cancelada': 'discarded',
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'no aplicar': 'skip', 'no_aplicar': 'skip', 'monitor': 'skip', 'geo blocker': 'skip',
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};
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function normalizeStatus(raw) {
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const clean = raw.replace(/\*\*/g, '').trim().toLowerCase()
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.replace(/\s+\d{4}-\d{2}-\d{2}.*$/, '').trim();
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return ALIASES[clean] || clean;
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}
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function classifyOutcome(status) {
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const s = normalizeStatus(status);
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if (['interview', 'offer', 'responded', 'applied'].includes(s)) return 'positive';
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if (['rejected', 'discarded'].includes(s)) return 'negative';
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if (['skip'].includes(s)) return 'self_filtered';
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return 'pending'; // evaluated
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}
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function normalizeList(value) {
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if (Array.isArray(value)) return value.map(v => String(v).trim()).filter(Boolean);
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if (value === null || value === undefined || value === '') return [];
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if (typeof value === 'object') return [];
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return [String(value).trim()].filter(Boolean);
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}
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function normalizeScalar(value) {
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if (typeof value === 'string') return value.trim() || null;
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if (typeof value === 'number' && Number.isFinite(value)) return String(value);
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return null;
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}
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function parseMachineSummary(content) {
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const fenceMatch = content.match(/##\s*Machine Summary\s*\n+```(?:yaml|yml|json)?\s*\n([\s\S]*?)\n```/i);
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if (!fenceMatch) return null;
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const raw = fenceMatch[1].trim();
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if (!raw) return null;
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try {
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const parsed = yamlLoad(raw);
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if (!parsed || typeof parsed !== 'object' || Array.isArray(parsed)) return null;
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return Object.fromEntries(
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Object.entries(parsed).filter(([key]) => MACHINE_SUMMARY_FIELDS.has(key))
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);
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} catch {
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return null;
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}
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}
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// --- Via channel analysis (#1596 follow-up) ---
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// Pure: group submitted applications by their Via channel (agency/recruiter
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// firm) and compute per-agency advance rates, plus the agency-vs-direct
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// aggregate. Channel identity uses the SAME normalizeVia key as the
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// merge-tracker dedup guard (tracker-parse.mjs): NFKC + Unicode letters/digits,
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// so "Hays" / "HAYS " / full-width "ＨＡＹＳ" land in one bucket while distinct
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// non-Latin agencies (リクルートAgent vs パーソルAgent) stay separate. The
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// first raw spelling seen is kept for display. Rows in `submitted` whose Via
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// cell is empty (legacy tracker without the column, or a blank cell — as
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// opposed to the explicit `—` direct marker) belong to neither bucket; they
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// are counted as `unknownVia` so agencySubmitted + directSubmitted can't
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// silently undershoot the submitted total.
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function buildViaChannelAnalysis(submitted, isAdvanced, minSample = MIN_VENDOR_N) {
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const viaOf = (e) => String(e.via ?? '').trim();
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const isDirect = (v) => v === '—' || v === '-';
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const agencySubmitted = submitted.filter(e => { const v = viaOf(e); return v !== '' && !isDirect(v); });
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const directSubmitted = submitted.filter(e => isDirect(viaOf(e)));
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const rate = (arr) => (arr.length > 0 ? Math.round((arr.filter(isAdvanced).length / arr.length) * 100) : 0);
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const byAgency = new Map();
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for (const e of agencySubmitted) {
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const raw = viaOf(e);
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// All-symbol names (e.g. "***") normalize to '' — fall back to the
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// NFKC-lowercased raw string so DISTINCT all-symbol names stay distinct
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// buckets instead of merging into one shared empty key.
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const key = normalizeVia(raw) || raw.normalize('NFKC').toLowerCase();
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if (!byAgency.has(key)) byAgency.set(key, { agency: raw, total: 0, advanced: 0 });
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const entry = byAgency.get(key);
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entry.total++;
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if (isAdvanced(e)) entry.advanced++;
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}
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const breakdown = [...byAgency.values()]
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.map(d => ({
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agency: d.agency,
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total: d.total,
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advanced: d.advanced,
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advanceRate: d.total > 0 ? Math.round((d.advanced / d.total) * 100) : 0,
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sufficientSample: d.total >= minSample,
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}))
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.sort((a, b) => b.total - a.total);
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return {
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minSampleForClaim: minSample,
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agencySubmitted: agencySubmitted.length,
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directSubmitted: directSubmitted.length,
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// Coverage honesty: submitted rows with an empty Via cell (no `—` marker)
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// that fall into neither bucket. Non-zero means the agency/direct split
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// covers only a subset of submissions.
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unknownVia: submitted.length - agencySubmitted.length - directSubmitted.length,
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agencyAdvanceRate: rate(agencySubmitted),
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directAdvanceRate: rate(directSubmitted),
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breakdown,
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};
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}
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function runSelfTest() {
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const summary = parseMachineSummary(`
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## Machine Summary
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\`\`\`yaml
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company: "Acme"
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role: "Staff AI Engineer"
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score: 4.4
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legitimacy_tier: "High Confidence"
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archetype: "AI Platform / LLMOps Engineer"
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final_decision: "Apply"
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hard_stops: []
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soft_gaps:
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- "No direct healthcare domain experience"
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top_strengths:
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- "Production evaluation pipelines"
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risk_level: "Medium"
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confidence: "High"
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next_action: "Follow up on ticket #42 with tailored CV"
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via: "Hays"
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company_confidential: true
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\`\`\`
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`);
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const failures = [];
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if (!summary) failures.push('summary was not parsed');
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if (summary?.score !== 4.4) failures.push('numeric score was not parsed');
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if (!Array.isArray(summary?.hard_stops) || summary.hard_stops.length !== 0) failures.push('empty list was not parsed');
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if (summary?.soft_gaps?.[0] !== 'No direct healthcare domain experience') failures.push('list item was not parsed');
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if (summary?.next_action !== 'Follow up on ticket #42 with tailored CV') failures.push('hash-containing scalar field was not parsed');
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if (summary?.via !== 'Hays') failures.push('via was not preserved from Machine Summary');
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if (summary?.company_confidential !== true) failures.push('company_confidential boolean was not preserved from Machine Summary');
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// Vendor detection (community ATS only; white-labeled → null)
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const vendorCases = [
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['https://boards.greenhouse.io/acme/jobs/12345', 'greenhouse'],
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['https://job-boards.eu.greenhouse.io/acme/jobs/9', 'greenhouse'],
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['https://jobs.lever.co/acme/abc-def', 'lever'],
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['https://jobs.ashbyhq.com/acme/uuid', 'ashby'],
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['https://acme.wd1.myworkdayjobs.com/en-US/careers/job/R-1', 'workday'],
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['https://careers.icims.com/jobs/9/x', null],
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['https://jobs.dayforcehcm.com/en-US/co/CANDIDATEPORTAL/jobs/1', null],
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['not a url', null],
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['', null],
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[null, null],
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];
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for (const [url, expected] of vendorCases) {
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const got = detectVendor(url);
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if (got !== expected) failures.push(`detectVendor(${JSON.stringify(url)}) → ${JSON.stringify(got)}, expected ${JSON.stringify(expected)}`);
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}
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// Via channel analysis (#1596): agency vs direct yield, normalized buckets.
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const advanced = new Set(['responded', 'interview', 'offer']);
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const viaRows = [
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{ via: 'Hays', normalizedStatus: 'interview' },
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{ via: 'HAYS ', normalizedStatus: 'rejected' }, // same bucket as Hays
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{ via: 'ＨＡＹＳ', normalizedStatus: 'rejected' }, // full-width → same bucket as Hays (NFKC)
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{ via: 'Randstad', normalizedStatus: 'rejected' },
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{ via: 'リクルートAgent', normalizedStatus: 'interview' }, // non-Latin: distinct agency...
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{ via: 'パーソルAgent', normalizedStatus: 'rejected' }, // ...must NOT merge with the one above
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{ via: '—', normalizedStatus: 'responded' }, // direct
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{ via: '—', normalizedStatus: 'rejected' }, // direct
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{ via: '', normalizedStatus: 'applied' }, // no Via column → unknownVia, neither bucket
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];
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const viaResult = buildViaChannelAnalysis(viaRows, (e) => advanced.has(e.normalizedStatus), 2);
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if (viaResult.agencySubmitted !== 6) failures.push(`via: agencySubmitted → ${viaResult.agencySubmitted}, expected 6`);
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if (viaResult.directSubmitted !== 2) failures.push(`via: directSubmitted → ${viaResult.directSubmitted}, expected 2`);
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if (viaResult.unknownVia !== 1) failures.push(`via: unknownVia → ${viaResult.unknownVia}, expected 1 (submitted row with empty Via must be counted, not silently dropped)`);
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if (viaResult.directAdvanceRate !== 50) failures.push(`via: directAdvanceRate → ${viaResult.directAdvanceRate}, expected 50`);
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const hays = viaResult.breakdown.find(a => a.agency === 'Hays');
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if (!hays || hays.total !== 3 || hays.advanceRate !== 33) {
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failures.push(`via: Hays bucket wrong (case/space/full-width variants must merge) → ${JSON.stringify(hays)}`);
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}
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if (!hays?.sufficientSample) failures.push('via: Hays should meet the n=2 sample bar');
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const recruit = viaResult.breakdown.find(a => a.agency === 'リクルートAgent');
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const persol = viaResult.breakdown.find(a => a.agency === 'パーソルAgent');
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if (!recruit || !persol || recruit.total !== 1 || persol.total !== 1) {
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failures.push(`via: distinct non-Latin agencies must stay separate buckets → リクルートAgent=${JSON.stringify(recruit)}, パーソルAgent=${JSON.stringify(persol)}`);
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}
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const randstad = viaResult.breakdown.find(a => a.agency === 'Randstad');
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if (randstad?.sufficientSample) failures.push('via: Randstad (n=1) must be flagged as too small for a claim');
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if (buildViaChannelAnalysis([], () => false).breakdown.length !== 0) {
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failures.push('via: empty input must produce an empty breakdown');
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}
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if (failures.length > 0) {
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console.error(`analyze-patterns self-test failed: ${failures.join('; ')}`);
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process.exit(1);
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}
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console.log('analyze-patterns self-test OK (Machine Summary parser + vendor detection + via channel analysis)');
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process.exit(0);
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}
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// --- Parse applications.md ---
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function parseTracker() {
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if (!existsSync(APPS_FILE)) return [];
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const content = readFileSync(APPS_FILE, 'utf-8');
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const lines = content.split('\n');
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const colmap = resolveColumns(lines);
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const entries = [];
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for (const line of lines) {
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const row = parseTrackerRow(line, colmap);
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if (row) entries.push(row);
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}
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return entries;
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}
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// --- Parse a single report file ---
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function parseReport(reportPath) {
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if (!existsSync(reportPath)) return null;
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const content = readFileSync(reportPath, 'utf-8');
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const report = {
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company: null,
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role: null,
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url: null,
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archetype: null,
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legitimacyTier: null,
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finalDecision: null,
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seniority: null,
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remote: null,
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teamSize: null,
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comp: null,
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domain: null,
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riskLevel: null,
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confidence: null,
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nextAction: null,
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topStrengths: [],
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scores: {},
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gaps: [],
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};
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const machineSummary = parseMachineSummary(content);
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if (machineSummary) {
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report.machineSummary = machineSummary;
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report.company = normalizeScalar(machineSummary.company) || report.company;
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report.role = normalizeScalar(machineSummary.role) || report.role;
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report.archetype = normalizeScalar(machineSummary.archetype) || report.archetype;
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report.legitimacyTier = normalizeScalar(machineSummary.legitimacy_tier) || report.legitimacyTier;
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report.finalDecision = normalizeScalar(machineSummary.final_decision) || report.finalDecision;
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report.domain = normalizeScalar(machineSummary.domain) || report.domain;
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report.seniority = normalizeScalar(machineSummary.seniority) || report.seniority;
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report.remote = normalizeScalar(machineSummary.remote) || report.remote;
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report.teamSize = normalizeScalar(machineSummary.team_size) || report.teamSize;
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report.riskLevel = normalizeScalar(machineSummary.risk_level) || report.riskLevel;
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report.confidence = normalizeScalar(machineSummary.confidence) || report.confidence;
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report.nextAction = normalizeScalar(machineSummary.next_action) || report.nextAction;
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report.topStrengths = normalizeList(machineSummary.top_strengths);
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if (typeof machineSummary.score === 'number') {
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report.scores.global = machineSummary.score;
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}
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for (const hardStop of normalizeList(machineSummary.hard_stops)) {
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report.gaps.push({ description: hardStop, severity: 'hard stop', mitigation: '' });
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}
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for (const softGap of normalizeList(machineSummary.soft_gaps)) {
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report.gaps.push({ description: softGap, severity: 'soft gap', mitigation: '' });
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}
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}
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// Strip bold markers for easier matching
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const plain = content.replace(/\*\*/g, '');
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// Extract Block A table (Role Summary) — works with both EN and ES headers
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// Archetype cell may be labeled "Archetype", "Arquetipo", or "Detected archetype" (drift from EN translation).
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const blockARegex = /\|\s*(?:Detected\s+)?(?:Archetype|Arquetipo)\s*\|\s*(.*?)\s*\|/i;
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const seniorityRegex = /\|\s*(?:Seniority|Nivel|Level)\s*\|\s*(.*?)\s*\|/i;
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const remoteRegex = /\|\s*(?:Remote|Remoto|Location)\s*\|\s*(.*?)\s*\|/i;
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const teamRegex = /\|\s*(?:Team|Team size|Equipo)\s*\|\s*(.*?)\s*\|/i;
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const compRegex = /\|\s*(?:Comp|Salary|Salario|Listed salary)\s*\|\s*(.*?)\s*\|/i;
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const domainRegex = /\|\s*(?:Domain|Dominio|Industry)\s*\|\s*(.*?)\s*\|/i;
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// Fallback: report header field `Archetype: ...` or `Arquetipo: ...` (newer reports use this).
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const headerArchRegex = /^(?:Archetype|Arquetipo):\s*(.+?)$/im;
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// Report header carries `**URL:**` between Score and PDF (see CLAUDE.md /
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// Pipeline Integrity). Capture the first http(s) URL on that line for vendor
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// detection; reports predating the field simply leave url null (→ unknown bucket).
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const urlMatch = plain.match(/^URL:\s*(https?:\/\/\S+)/im);
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if (urlMatch && !report.url) report.url = urlMatch[1].trim().replace(/[)>\].,]+$/, '');
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const archMatch = plain.match(blockARegex) || plain.match(headerArchRegex);
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if (archMatch && !report.archetype) report.archetype = archMatch[1].trim();
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const senMatch = plain.match(seniorityRegex);
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if (senMatch && !report.seniority) report.seniority = senMatch[1].trim();
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const remMatch = plain.match(remoteRegex);
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if (remMatch && !report.remote) report.remote = remMatch[1].trim();
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const teamMatch = plain.match(teamRegex);
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if (teamMatch && !report.teamSize) report.teamSize = teamMatch[1].trim();
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const compMatch = plain.match(compRegex);
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if (compMatch && !report.comp) report.comp = compMatch[1].trim();
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const domainMatch = plain.match(domainRegex);
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if (domainMatch && !report.domain) report.domain = domainMatch[1].trim();
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// Extract scoring table — look for table with "Global" row (using plain, bold already stripped)
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const scoreRegex = /\|\s*(?:CV Match|Match con CV)\s*\|\s*([\d.]+)\/5\s*\|/i;
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const northStarRegex = /\|\s*(?:North Star)\s*\|\s*([\d.]+)\/5\s*\|/i;
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const compScoreRegex = /\|\s*(?:Comp)\s*\|\s*([\d.]+)\/5\s*\|/i;
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const culturalRegex = /\|\s*(?:Cultural signals|Cultural)\s*\|\s*([\d.]+)\/5\s*\|/i;
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const redFlagsRegex = /\|\s*(?:Red flags)\s*\|\s*([-+]?[\d.]+)\s*\|/i;
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const globalRegex = /\|\s*(?:Global)\s*\|\s*([\d.]+)\/5\s*\|/i;
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const cvScoreMatch = plain.match(scoreRegex);
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if (cvScoreMatch && report.scores.cvMatch === undefined) report.scores.cvMatch = parseFloat(cvScoreMatch[1]);
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const nsMatch = plain.match(northStarRegex);
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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(/\]\(([^)]+)\)/);
|
|
// Tracker links are relative to the tracker file's own directory (see
|
|
// merge-tracker.mjs link normalization); fall back to repo root for
|
|
// legacy root-relative links.
|
|
let reportPath = null;
|
|
if (reportMatch) {
|
|
const fromTracker = join(dirname(APPS_FILE), reportMatch[1]);
|
|
const candidate = existsSync(fromTracker) ? fromTracker : join(CAREER_OPS, reportMatch[1]);
|
|
|
|
const repoRelative = relative(CAREER_OPS, candidate).split(sep).join('/');
|
|
if (repoRelative.startsWith('reports/') && !repoRelative.includes('..')) {
|
|
reportPath = existsSync(candidate) ? candidate : 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)',
|
|
};
|
|
|
|
// --- Via channel analysis (#1596 follow-up): per-agency advance rate ---
|
|
// Same honesty rules as the vendor analysis above: this reports CHANNEL
|
|
// YIELD. In an agency-mediated search the highest-leverage decision is which
|
|
// recruiter relationships to invest in — this shows which ones convert.
|
|
// Rows only carry `via` when the tracker has the optional Via column
|
|
// (#1596); without it every bucket is empty and nothing is claimed.
|
|
const viaChannelAnalysis = buildViaChannelAnalysis(submitted, isAdvanced);
|
|
|
|
// --- 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',
|
|
});
|
|
}
|
|
|
|
// Best-converting agency (#1596 follow-up): with a sufficient sample and a
|
|
// clear lead over the overall pipeline, that recruiter relationship is worth
|
|
// prioritizing. One recommendation at most — the breakdown shows the rest.
|
|
const topAgency = viaChannelAnalysis.breakdown
|
|
.filter(a => a.sufficientSample && a.advanced > 0 && a.advanceRate >= overallAdvanceRate + 10)
|
|
.sort((a, b) => b.advanceRate - a.advanceRate)[0];
|
|
if (topAgency) {
|
|
recommendations.push({
|
|
action: `Prioritize roles via ${topAgency.agency} -- ${topAgency.advanceRate}% advance rate across ${topAgency.total} submissions (overall: ${overallAdvanceRate}%)`,
|
|
reasoning: `${topAgency.advanced}/${topAgency.total} applications through ${topAgency.agency} advanced past screening, well above your overall rate. In an agency-mediated search the highest-leverage decision is which recruiter relationships to invest in -- this one converts. Channel yield, not a causal claim.`,
|
|
impact: 'medium',
|
|
});
|
|
}
|
|
|
|
// Date range
|
|
const dates = enriched.map(e => e.date).filter(Boolean).sort();
|
|
|
|
return {
|
|
metadata: {
|
|
total: enriched.length,
|
|
dateRange: { from: dates[0], to: dates[dates.length - 1] },
|
|
analysisDate: new Date().toISOString().split('T')[0],
|
|
byOutcome: {
|
|
positive: enriched.filter(e => e.outcome === 'positive').length,
|
|
negative: enriched.filter(e => e.outcome === 'negative').length,
|
|
self_filtered: enriched.filter(e => e.outcome === 'self_filtered').length,
|
|
pending: enriched.filter(e => e.outcome === 'pending').length,
|
|
},
|
|
},
|
|
funnel,
|
|
scoreComparison,
|
|
archetypeBreakdown,
|
|
blockerAnalysis,
|
|
remotePolicy,
|
|
companySizeBreakdown,
|
|
vendorAnalysis,
|
|
viaChannelAnalysis,
|
|
scoreThreshold,
|
|
techStackGaps,
|
|
recommendations,
|
|
};
|
|
}
|
|
|
|
// --- Summary mode (human-readable) ---
|
|
function printSummary(result) {
|
|
if (result.error) {
|
|
console.log(`\n${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.');
|
|
}
|
|
|
|
// Via channel analysis (#1596): which recruiter relationships convert
|
|
const via = result.viaChannelAnalysis;
|
|
if (via && (via.breakdown.length > 0 || via.directSubmitted > 0)) {
|
|
console.log('\nVIA CHANNEL ANALYSIS (agency vs direct, #1596)');
|
|
console.log('-'.repeat(40));
|
|
console.log(` direct ${String(via.directSubmitted).padStart(3)} apps ${String(via.directAdvanceRate).padStart(3)}% advance`);
|
|
console.log(` agency ${String(via.agencySubmitted).padStart(3)} apps ${String(via.agencyAdvanceRate).padStart(3)}% advance`);
|
|
if (via.unknownVia > 0) {
|
|
console.log(` unknown ${String(via.unknownVia).padStart(3)} apps (no Via recorded — not counted in either channel)`);
|
|
}
|
|
for (const a of via.breakdown) {
|
|
const flag = a.sufficientSample ? '' : ' (n too small for a claim)';
|
|
console.log(` ${a.agency.padEnd(16)} ${String(a.total).padStart(3)} apps ${String(a.advanceRate).padStart(3)}% advance${flag}`);
|
|
}
|
|
}
|
|
|
|
// 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);
|