wehub-resource-sync
495 lines
20 KiB
JavaScript
495 lines
20 KiB
JavaScript
#!/usr/bin/env node
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/**
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* upskill.mjs — Aggregate skill-gap analyzer for career-ops (#1520, phase 1)
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*
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* Reads the tracker + every linked evaluation report, extracts skill tokens
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* from each report's gaps (Machine Summary hard_stops/soft_gaps + Gap table),
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* removes anything already present in cv.md / config/profile.yml, and emits a
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* weighted, tiered gap map as JSON for the `upskill` mode to narrate.
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*
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* Weighting: each report contributes (5.0 − score) per skill it names — a
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* 2.1/5 report says more about your gaps than a 4.5/5 one. A skill is counted
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* once per report (presence), not once per mention, so one ranty report can't
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* dominate the map.
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*
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* Tiers are fixed, explainable thresholds over the share of low-fit
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* (score < 4.0) reports naming the gap — NOT quantiles, which are noise at
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* the 5–20 report sample sizes this tool sees.
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*
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* Run: node upskill.mjs (JSON to stdout)
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* node upskill.mjs --summary (human-readable table)
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* node upskill.mjs --min-reports 3
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* node upskill.mjs --self-test
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*/
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import { readFileSync, existsSync } from 'fs';
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import { join, dirname } 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 } 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 CV_FILE = join(CAREER_OPS, 'cv.md');
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const PROFILE_FILE = join(CAREER_OPS, 'config/profile.yml');
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// Bump when extraction rules change in a way that would make gap lists from
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// older runs non-comparable. The upskill mode's diff-vs-previous section only
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// compares reports with the same schema_version, so a regex change can't
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// masquerade as "gap closed".
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export const SCHEMA_VERSION = 1;
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// Reports below this global score count as "low fit" — the population whose
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// gaps matter most. Matches the apply threshold in Ethical Use (CLAUDE.md).
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const LOW_FIT_SCORE = 4.0;
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// Skill tokenizer. Superset of the tech regex in analyze-patterns.mjs
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// (deliberately duplicated — see #1520 discussion: extracting a shared module
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// from a tested core script is a follow-up once both call sites are stable).
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const SKILL_TOKENS = [
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// Languages
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'JavaScript', 'TypeScript', 'Python', 'Ruby', 'Java', 'Golang', 'Rust', 'PHP',
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'Kotlin', 'Swift', 'Scala', 'Elixir', 'C\\+\\+', 'C#', '\\.NET', 'SQL',
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// Frontend / frameworks
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'React Native', 'React', 'Angular', 'Vue\\.?js', 'Svelte', 'Next\\.?js',
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'Django', 'Flask', 'FastAPI', 'Rails', 'Laravel', 'Symfony', 'Spring',
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'Node\\.?js', 'NodeJS',
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// Data stores
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'MongoDB', 'MySQL', 'PostgreSQL', 'Postgres', 'Redis', 'Elasticsearch',
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'Snowflake', 'BigQuery', 'Databricks', 'DynamoDB', 'Cassandra',
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// APIs / messaging
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'GraphQL', 'gRPC', 'Kafka', 'RabbitMQ',
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// Cloud / infra
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'AWS', 'GCP', 'Azure', 'Docker', 'Kubernetes', 'k8s', 'Terraform',
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'Ansible', 'Helm', 'Jenkins', 'GitHub Actions', 'GitLab CI', 'CI/CD',
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'Prometheus', 'Grafana', 'Datadog', 'Supabase', 'Inngest',
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// Data / ML / AI
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'PyTorch', 'TensorFlow', 'scikit-learn', 'Pandas', 'NumPy', 'Spark',
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'Airflow', 'dbt', 'MLOps', 'MLflow', 'LangChain', 'LlamaIndex',
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'Hugging Face', 'RAG', 'LLMs?', 'Prompt Engineering', 'Fine-?tuning',
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'Computer Vision', 'NLP',
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// Analytics / enterprise
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'Tableau', 'Power BI', 'Looker', 'Salesforce', 'SAP',
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];
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// \b fails at symbol edges (\bC\+\+\b needs a word char AFTER the +, \b\.NET
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// needs one BEFORE the dot), so C++/C#/.NET would never match standalone.
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// (?<!\w)/(?!\w) are equivalent to \b for word-char edges and correct for
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// symbol edges.
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const SKILL_PATTERN = new RegExp(
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'(?<!\\w)(?:' + SKILL_TOKENS.join('|') + ')(?!\\w)',
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'gi'
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);
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// "Go" is an everyday English word, so it can't join the case-insensitive
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// token list ("go the extra mile" would register a skill). Match it in a
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// separate CASE-SENSITIVE pass: only the exact standalone token "Go" counts
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// as the language; prose "go"/"GO" never do. "Golang" still resolves to "Go"
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// via the main pattern + CANONICAL. A trailing hyphen also disqualifies:
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// capitalized business phrases like "Go-to-market" and "Go-live" are not the
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// language (punctuation like "Go," "Go/Rust" "(Go)" still counts).
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const GO_SKILL_PATTERN = /(?<!\w)Go(?![\w-])/;
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// lowercase → canonical display casing, derived from SKILL_TOKENS by stripping
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// regex syntax ('Vue\\.?js' → 'Vue.js'). Keeps case-insensitive matches like
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// "graphql" resolving to the same key ("GraphQL") as the CV-known-skills set.
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const DISPLAY = Object.fromEntries(
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SKILL_TOKENS.map(t => {
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const display = t.replace(/\\/g, '').replace(/\?/g, '');
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return [display.toLowerCase(), display];
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})
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);
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// Exact-alias canonicalization ONLY (lowercased match → display name).
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// Deliberately no umbrella aliases: "cloud" must never count as knowing
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// AWS/GCP/Azure — a generous map silently suppresses real gaps, and the
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// "cv skill never appears as gap" acceptance test rewards exactly that
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// failure mode. Every entry here maps spellings of the SAME skill.
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const CANONICAL = {
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'k8s': 'Kubernetes',
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'golang': 'Go',
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'postgres': 'PostgreSQL',
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'nodejs': 'Node.js', 'node.js': 'Node.js', 'nodejs.': 'Node.js',
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'vuejs': 'Vue.js', 'vue.js': 'Vue.js',
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'nextjs': 'Next.js', 'next.js': 'Next.js',
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'llm': 'LLMs', 'llms': 'LLMs',
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'finetuning': 'Fine-tuning', 'fine-tuning': 'Fine-tuning',
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'power bi': 'Power BI',
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'github actions': 'GitHub Actions',
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'gitlab ci': 'GitLab CI',
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'ci/cd': 'CI/CD',
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'hugging face': 'Hugging Face',
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'react native': 'React Native',
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'prompt engineering': 'Prompt Engineering',
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'computer vision': 'Computer Vision',
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'scikit-learn': 'scikit-learn',
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'c++': 'C++', 'c#': 'C#', '.net': '.NET',
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'nlp': 'NLP', 'rag': 'RAG', 'sql': 'SQL', 'aws': 'AWS', 'gcp': 'GCP',
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'grpc': 'gRPC', 'dbt': 'dbt', 'mlops': 'MLOps', 'mlflow': 'MLflow',
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};
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function canonicalize(token) {
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const key = token.toLowerCase();
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// Alias map first (k8s → Kubernetes), then display casing from the token
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// list (graphql → GraphQL, pytorch → PyTorch) — never title-case, which
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// manufactures keys like "Graphql" that miss the known-skills set.
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return CANONICAL[key] || DISPLAY[key] || token;
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}
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/** Extract the set of canonical skill names present in a free-text blob. */
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export function extractSkills(text) {
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if (!text) return new Set();
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const found = new Set();
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for (const m of text.matchAll(SKILL_PATTERN)) {
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found.add(canonicalize(m[0]));
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}
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if (GO_SKILL_PATTERN.test(text)) found.add('Go');
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return found;
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}
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// --- Machine Summary + Gap table parsing ---
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// Mirrors analyze-patterns.mjs (duplicated by design, see header comment).
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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 parsed;
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} catch {
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return null;
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}
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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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/**
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* Parse one report file into { score, gapText, hasMachineSummary }.
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* gapText concatenates every gap description (hard stops, soft gaps, Gap
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* table rows) — the haystack the skill tokenizer runs over.
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*/
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export function parseReportGaps(content) {
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const gapDescriptions = [];
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let score = null;
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let hasMachineSummary = false;
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const summary = parseMachineSummary(content);
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if (summary) {
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hasMachineSummary = true;
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if (typeof summary.score === 'number' && Number.isFinite(summary.score)) score = summary.score;
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gapDescriptions.push(...normalizeList(summary.hard_stops));
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gapDescriptions.push(...normalizeList(summary.soft_gaps));
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}
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const plain = content.replace(/\*\*/g, '');
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if (score === null) {
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const glMatch = plain.match(/\|\s*(?:Global)\s*\|\s*([\d.]+)\/5\s*\|/i);
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if (glMatch) score = parseFloat(glMatch[1]);
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}
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const gapTableMatch = content.match(/\|\s*Gap\s*\|\s*Severity\s*\|.*?\n\|[-|\s]+\n([\s\S]*?)(?:\n\n|\n##|\n\*\*|$)/i);
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if (gapTableMatch) {
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for (const row of gapTableMatch[1].split('\n').filter(r => r.startsWith('|'))) {
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const cols = row.split('|').map(s => s.trim()).filter(Boolean);
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if (cols.length >= 2) gapDescriptions.push(cols[0]);
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}
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}
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return { score, gapText: gapDescriptions.join('\n'), hasMachineSummary };
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}
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/**
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* Pure aggregation over parsed reports. Exported for self-testing.
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*
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* @param {Array<{num:number|string, score:number|null, gapText:string}>} reports
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* @param {Set<string>} knownSkills — canonical names already in cv/profile
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*/
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export function aggregateGaps(reports, knownSkills) {
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const scored = reports.filter(r => Number.isFinite(r.score));
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const lowFit = scored.filter(r => r.score < LOW_FIT_SCORE);
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const totalLowFit = lowFit.length;
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const bySkill = new Map();
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const excludedCounts = new Map();
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for (const report of reports) {
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const skills = extractSkills(report.gapText);
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for (const skill of skills) {
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if (knownSkills.has(skill)) {
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excludedCounts.set(skill, (excludedCounts.get(skill) || 0) + 1);
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continue;
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}
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if (!bySkill.has(skill)) {
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bySkill.set(skill, { skill, reports: 0, lowFitReports: 0, weightedScore: 0, sources: [] });
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}
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const entry = bySkill.get(skill);
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entry.reports += 1;
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entry.sources.push(report.num);
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const weight = Number.isFinite(report.score) ? Math.max(0, 5.0 - report.score) : 1.0;
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entry.weightedScore += weight;
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if (Number.isFinite(report.score) && report.score < LOW_FIT_SCORE) entry.lowFitReports += 1;
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}
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}
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const gaps = [...bySkill.values()].map(g => {
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const share = totalLowFit > 0 ? g.lowFitReports / totalLowFit : 0;
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// Fixed thresholds — each tier is explainable in one sentence
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// ("named in 4/9 low-fit reports"), which quantiles at N=5–20 are not.
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let tier = 'Low';
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if (share >= 0.5 && g.lowFitReports >= 3) tier = 'Critical';
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else if (share >= 0.3 && g.lowFitReports >= 2) tier = 'High';
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else if (g.lowFitReports >= 2) tier = 'Medium';
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return {
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...g,
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lowFitShare: Math.round(share * 100) / 100,
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weightedScore: Math.round(g.weightedScore * 100) / 100,
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tier,
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};
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}).sort((a, b) => b.weightedScore - a.weightedScore || b.reports - a.reports);
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const excludedAsKnown = [...excludedCounts.entries()]
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.map(([skill, reports]) => ({ skill, reports }))
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.sort((a, b) => b.reports - a.reports);
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return { gaps, excludedAsKnown, totalLowFit };
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}
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// --- Main ---
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function analyze(minReports) {
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if (!existsSync(APPS_FILE)) {
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return { error: 'No applications tracker found. Run some evaluations first.' };
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}
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const lines = readFileSync(APPS_FILE, 'utf-8').split('\n');
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const colmap = resolveColumns(lines);
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const rows = lines.map(l => parseTrackerRow(l, colmap)).filter(Boolean);
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let reportsLinked = 0;
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let reportsRead = 0;
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let reportsWithMachineSummary = 0;
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const parsedReports = [];
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for (const row of rows) {
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const linkMatch = (row.report || '').match(/\]\(([^)]+)\)/);
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if (!linkMatch) continue;
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reportsLinked += 1;
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// Tracker links are normalized relative to the tracker file's directory
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// (see merge-tracker.mjs); resolve against it, with a root-relative fallback.
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const candidates = [join(dirname(APPS_FILE), linkMatch[1]), join(CAREER_OPS, linkMatch[1])];
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const reportPath = candidates.find(p => existsSync(p));
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if (!reportPath) continue;
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reportsRead += 1;
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const content = readFileSync(reportPath, 'utf-8');
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const { score, gapText, hasMachineSummary } = parseReportGaps(content);
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if (hasMachineSummary) reportsWithMachineSummary += 1;
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const trackerScore = parseFloat(row.score);
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parsedReports.push({
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num: row.num,
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score: Number.isFinite(trackerScore) ? trackerScore : score,
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gapText,
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});
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}
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const scoredCount = parsedReports.filter(r => Number.isFinite(r.score)).length;
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if (scoredCount < minReports) {
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return {
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error: `Not enough data: ${scoredCount}/${minReports} scored reports. Evaluate more offers and come back.`,
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current: scoredCount,
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threshold: minReports,
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};
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}
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const knownText = [
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existsSync(CV_FILE) ? readFileSync(CV_FILE, 'utf-8') : '',
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existsSync(PROFILE_FILE) ? readFileSync(PROFILE_FILE, 'utf-8') : '',
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].join('\n');
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const knownSkills = extractSkills(knownText);
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const { gaps, excludedAsKnown, totalLowFit } = aggregateGaps(parsedReports, knownSkills);
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return {
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schema_version: SCHEMA_VERSION,
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metadata: {
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reportsLinked,
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reportsRead,
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reportsWithMachineSummary,
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reportsScored: scoredCount,
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lowFitReports: totalLowFit,
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lowFitScoreThreshold: LOW_FIT_SCORE,
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knownSkillCount: knownSkills.size,
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},
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gaps,
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excludedAsKnown,
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knownSkills: [...knownSkills].sort(),
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};
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}
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function printSummary(result) {
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if (result.error) {
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console.log(`upskill: ${result.error}`);
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return;
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}
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const m = result.metadata;
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console.log(`UPSKILL GAP MAP (schema v${result.schema_version})`);
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console.log(`Reports: ${m.reportsRead}/${m.reportsLinked} read, ${m.reportsScored} scored, ${m.lowFitReports} low-fit (<${m.lowFitScoreThreshold}), ${m.reportsWithMachineSummary} with Machine Summary`);
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console.log('');
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if (result.gaps.length === 0) {
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console.log('No skill gaps detected across your evaluated reports.');
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} else {
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const pad = (s, n) => String(s).padEnd(n);
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console.log(`${pad('TIER', 10)}${pad('SKILL', 22)}${pad('REPORTS', 9)}${pad('LOW-FIT', 9)}WEIGHTED`);
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for (const g of result.gaps) {
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console.log(`${pad(g.tier, 10)}${pad(g.skill, 22)}${pad(g.reports, 9)}${pad(`${g.lowFitReports}/${result.metadata.lowFitReports}`, 9)}${g.weightedScore}`);
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}
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}
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if (result.excludedAsKnown.length > 0) {
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console.log('');
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console.log(`Excluded (already in cv.md/profile): ${result.excludedAsKnown.map(e => e.skill).join(', ')}`);
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}
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}
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// --- Self-test (pure functions, no filesystem) ---
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function runSelfTest() {
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const failures = [];
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// extractSkills: canonicalization
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const s1 = extractSkills('Needs k8s, golang and Postgres experience; NodeJS a plus');
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for (const expected of ['Kubernetes', 'Go', 'PostgreSQL', 'Node.js']) {
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if (!s1.has(expected)) failures.push(`extractSkills missing canonical ${expected} (got ${[...s1].join(',')})`);
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}
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// Symbol-terminated skills: \b-style boundaries would drop all three
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const s1b = extractSkills('Requires C++ and C# on .NET, plus SQL.');
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for (const expected of ['C++', 'C#', '.NET', 'SQL']) {
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if (!s1b.has(expected)) failures.push(`extractSkills missing symbol skill ${expected} (got ${[...s1b].join(',')})`);
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}
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// Standalone "Go" is matched case-SENSITIVELY: a capitalized token in a
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// skills list counts, but prose "go"/"GO" must never register as a skill
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// (the global pattern is case-insensitive, so Go lives outside it).
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const s1d = extractSkills('Skills: Go, Rust, TypeScript');
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if (!s1d.has('Go')) failures.push(`extractSkills missing standalone Go (got ${[...s1d].join(',')})`);
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const s1e = extractSkills('willing to go the extra mile; ready to GO live');
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if (s1e.has('Go')) failures.push('prose "go"/"GO" wrongly matched as Go skill');
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// Capitalized hyphenated business phrases must not register as the language
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const s1f = extractSkills('Own the Go-to-market strategy and Go-live support');
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if (s1f.has('Go')) failures.push('hyphenated "Go-to-market"/"Go-live" wrongly matched as Go skill');
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// ...but ordinary punctuation after the token still counts
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const s1g = extractSkills('Backend in Go/Rust (Go preferred). We ship Go.');
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if (!s1g.has('Go')) failures.push('punctuation-adjacent standalone Go missed');
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// Lowercase mentions of mixed-case skills must resolve to canonical casing,
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// or knownSkills.has() misses them (Graphql !== GraphQL)
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const s1c = extractSkills('familiar with graphql, pytorch and postgresql');
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for (const expected of ['GraphQL', 'PyTorch', 'PostgreSQL']) {
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if (!s1c.has(expected)) failures.push(`extractSkills lowercase mention not canonical ${expected} (got ${[...s1c].join(',')})`);
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}
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// Over-suppression guard: cv "Java" must NOT swallow a "JavaScript" gap,
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// and cv "AWS" must not swallow GCP/Azure. This is the failure mode the
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// "cv skill never appears as gap" acceptance test cannot see.
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const cvSkills = extractSkills('Expert in Java and AWS.');
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if (cvSkills.has('JavaScript')) failures.push('cv "Java" wrongly matched JavaScript');
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const { gaps: g1 } = aggregateGaps(
|
||
[{ num: 1, score: 2.0, gapText: 'Missing JavaScript and GCP experience' }],
|
||
cvSkills
|
||
);
|
||
const gapNames = g1.map(g => g.skill);
|
||
if (!gapNames.includes('JavaScript')) failures.push('JavaScript gap suppressed by cv "Java"');
|
||
if (!gapNames.includes('GCP')) failures.push('GCP gap suppressed by cv "AWS"');
|
||
|
||
// Known-skill exclusion (the acceptance criterion itself)
|
||
const { gaps: g2, excludedAsKnown: ex2 } = aggregateGaps(
|
||
[{ num: 2, score: 3.0, gapText: 'Needs Java and Kubernetes' }],
|
||
extractSkills('Java developer')
|
||
);
|
||
if (g2.some(g => g.skill === 'Java')) failures.push('known skill Java appeared as gap');
|
||
if (!ex2.some(e => e.skill === 'Java')) failures.push('excludedAsKnown missing Java');
|
||
if (!g2.some(g => g.skill === 'Kubernetes')) failures.push('Kubernetes gap missing');
|
||
|
||
// Weighting: low score contributes more; presence counted once per report
|
||
const { gaps: g3 } = aggregateGaps(
|
||
[
|
||
{ num: 3, score: 2.0, gapText: 'Kubernetes Kubernetes Kubernetes' },
|
||
{ num: 4, score: 4.5, gapText: 'Kubernetes' },
|
||
],
|
||
new Set()
|
||
);
|
||
const k = g3.find(g => g.skill === 'Kubernetes');
|
||
if (!k) failures.push('Kubernetes not aggregated');
|
||
else {
|
||
if (k.reports !== 2) failures.push(`presence not deduped per report (reports=${k.reports})`);
|
||
if (Math.abs(k.weightedScore - 3.5) > 1e-9) failures.push(`weightedScore expected 3.5, got ${k.weightedScore}`);
|
||
}
|
||
|
||
// Tiering: 3/5 low-fit reports naming a skill → Critical; 1/5 → Low
|
||
const lowFitReports = [
|
||
{ num: 10, score: 2.0, gapText: 'Terraform' },
|
||
{ num: 11, score: 2.5, gapText: 'Terraform' },
|
||
{ num: 12, score: 3.0, gapText: 'Terraform and Spark' },
|
||
{ num: 13, score: 3.5, gapText: 'nothing here' },
|
||
{ num: 14, score: 3.9, gapText: 'nothing here' },
|
||
];
|
||
const { gaps: g4 } = aggregateGaps(lowFitReports, new Set());
|
||
const terraform = g4.find(g => g.skill === 'Terraform');
|
||
const spark = g4.find(g => g.skill === 'Spark');
|
||
if (terraform?.tier !== 'Critical') failures.push(`Terraform tier expected Critical, got ${terraform?.tier}`);
|
||
if (spark?.tier !== 'Low') failures.push(`Spark tier expected Low, got ${spark?.tier}`);
|
||
|
||
// parseReportGaps: Machine Summary + Gap table + score fallback
|
||
const parsed = parseReportGaps(`
|
||
# 042 - Acme
|
||
|
||
| Gap | Severity | Mitigation |
|
||
|-----|----------|------------|
|
||
| No Kafka experience | soft gap | Learn it |
|
||
|
||
## Machine Summary
|
||
|
||
\`\`\`yaml
|
||
score: 3.2
|
||
hard_stops: []
|
||
soft_gaps:
|
||
- "Limited Airflow exposure"
|
||
\`\`\`
|
||
`);
|
||
if (parsed.score !== 3.2) failures.push(`report score expected 3.2, got ${parsed.score}`);
|
||
if (!parsed.hasMachineSummary) failures.push('hasMachineSummary false');
|
||
if (!/Kafka/.test(parsed.gapText)) failures.push('Gap table row not captured');
|
||
if (!/Airflow/.test(parsed.gapText)) failures.push('soft_gaps not captured');
|
||
|
||
if (failures.length > 0) {
|
||
console.error(`upskill self-test failed: ${failures.join('; ')}`);
|
||
process.exit(1);
|
||
}
|
||
console.log('upskill self-test OK (extraction, suppression guards, weighting, tiering, report parsing)');
|
||
process.exit(0);
|
||
}
|
||
|
||
// --- CLI ---
|
||
const args = process.argv.slice(2);
|
||
if (args.includes('--self-test')) runSelfTest();
|
||
|
||
const minReportsIdx = args.indexOf('--min-reports');
|
||
const MIN_REPORTS = (() => {
|
||
if (minReportsIdx === -1 || args[minReportsIdx + 1] === undefined) return 5;
|
||
const n = parseInt(args[minReportsIdx + 1], 10);
|
||
return Number.isNaN(n) || n < 1 ? 5 : n;
|
||
})();
|
||
|
||
const result = analyze(MIN_REPORTS);
|
||
if (args.includes('--summary')) {
|
||
printSummary(result);
|
||
} else {
|
||
console.log(JSON.stringify(result, null, 2));
|
||
}
|