analyze-patterns.mjs

#!/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
 */

import { readFileSync, existsSync } from 'fs';
import { join, dirname } from 'path';
import { fileURLToPath } from 'url';

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');

// --- 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;

// --- 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
}

// --- Parse applications.md ---
function parseTracker() {
  if (!existsSync(APPS_FILE)) return [];
  const content = readFileSync(APPS_FILE, 'utf-8');
  const entries = [];
  for (const line of content.split('\n')) {
    if (!line.startsWith('|')) continue;
    const parts = line.split('|').map(s => s.trim());
    if (parts.length < 9) continue;
    const num = parseInt(parts[1]);
    if (isNaN(num)) continue;
    entries.push({
      num, date: parts[2], company: parts[3], role: parts[4],
      score: parts[5], status: parts[6], pdf: parts[7], report: parts[8],
      notes: parts[9] || '',
    });
  }
  return entries;
}

// --- Parse a single report file ---
function parseReport(reportPath) {
  if (!existsSync(reportPath)) return null;
  const content = readFileSync(reportPath, 'utf-8');
  const report = {
    archetype: null,
    seniority: null,
    remote: null,
    teamSize: null,
    comp: null,
    domain: null,
    scores: {},
    gaps: [],
  };

  // Strip bold markers for easier matching
  const plain = content.replace(/\*\*/g, '');

  // Extract Block A table (Role Summary) — works with both EN and ES headers
  const blockARegex = /\|\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;

  const archMatch = plain.match(blockARegex);
  if (archMatch) report.archetype = archMatch[1].trim();

  const senMatch = plain.match(seniorityRegex);
  if (senMatch) report.seniority = senMatch[1].trim();

  const remMatch = plain.match(remoteRegex);
  if (remMatch) report.remote = remMatch[1].trim();

  const teamMatch = plain.match(teamRegex);
  if (teamMatch) report.teamSize = teamMatch[1].trim();

  const compMatch = plain.match(compRegex);
  if (compMatch) report.comp = compMatch[1].trim();

  const domainMatch = plain.match(domainRegex);
  if (domainMatch) 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 = parseFloat(cvScoreMatch[1]);

  const nsMatch = plain.match(northStarRegex);
  if (nsMatch) report.scores.northStar = parseFloat(nsMatch[1]);

  const csMatch = plain.match(compScoreRegex);
  if (csMatch) report.scores.comp = parseFloat(csMatch[1]);

  const culMatch = plain.match(culturalRegex);
  if (culMatch) report.scores.cultural = parseFloat(culMatch[1]);

  const rfMatch = plain.match(redFlagsRegex);
  if (rfMatch) report.scores.redFlags = parseFloat(rfMatch[1]);

  const glMatch = plain.match(globalRegex);
  if (glMatch) 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) {
        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';
}

// --- 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 score = parseFloat(e.score) || 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),
    };
  });

  // 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);

  // --- 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) ---
  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|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|React Native)\b/gi);
      if (techs) {
        for (const tech of techs) {
          const normalized = tech.charAt(0).toUpperCase() + tech.slice(1);
          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',
    });
  }

  // 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,
    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`);
    }
  }

  // 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 ---
const result = analyze();

if (summaryMode) {
  printSummary(result);
} else {
  console.log(JSON.stringify(result, null, 2));
}

if (result.error) process.exit(1);