Resumer — ATS Resume Builder

A web app that turns raw candidate inputs into a modern, recruiter-approved, metric-driven resume optimized for Applicant Tracking Systems (ATS).

It works in two modes:

• AI-enhanced — when an ANTHROPIC_API_KEY is configured, a Vercel serverless function calls the Claude API to rewrite bullets into natural XYZ-standard achievements and craft the professional summary. The key stays server-side.
• Rule-based — with no key configured, a deterministic in-browser engine handles scoring, keyword matching, and heuristic bullet rewriting. The app falls back to this automatically, so it always works, even opened as a local file.

The active mode is shown as a badge on the diagnostic panel.

What it does

Given a candidate's target role, raw work history, and skills, Resumer produces the three output modules of the resume-building engine:

1. ATS & Match Score Diagnostic

   • Estimated ATS compatibility score (0–100) from weighted signals.
   • Real-time keyword matching against a pasted job description (matched vs. missing).
   • Readability & impact grade.

2. Complete Modern Resume (copy-paste ready)

   • Clean, single-column, parser-safe markdown.
   • Standardized headers: Professional Summary, Core Competencies, Professional Experience, Key Projects, Education & Certifications.
   • No tables, text boxes, icons, or characters that break ATS parsers.

3. Resume Optimization Audit

   • "Before & After" bullet transformations using the Google XYZ standard — "Accomplished [X], as measured by [Y], by doing [Z]".
   • Recommendations for high-value additions (certifications, portfolio links, quantification).

Engine logic

• ATS scoring weights keyword coverage (30), quantified achievements (25), action-verb openers (20), core-field completeness (15), and parser-safe structure (10).
• Keyword matching extracts frequency-ranked words and phrases from the job description and checks them against the candidate corpus.
• Bullet analysis grades each line for metrics + action verbs, then rewrites weak, responsibility-driven statements into quantified achievements.
• Skill structuring separates technical skills from strategic/functional capabilities.

See js/engine.js — the engine is pure and framework-free.

Run it locally

Rule-based only (no key), zero setup:

python3 -m http.server 8000    # then open http://localhost:8000

With the AI backend (runs the serverless function locally):

npm i -g vercel
cp .env.example .env.local     # add your ANTHROPIC_API_KEY
vercel dev                     # serves the site + /api/enhance

Click Load sample to see a full end-to-end example.

Deploy to Vercel

1. Push this repo to GitHub (already done if you're reading this on main).
2. In Vercel: Add New → Project → Import this repo. It's detected as a static site with serverless functions in api/ — no build command needed.
3. Add an environment variable ANTHROPIC_API_KEY (Project → Settings → Environment Variables) to enable AI mode. Without it, the app deploys fine and runs in rule-based mode.
4. Deploy. Or from the CLI: vercel (preview) / vercel --prod.

Test the engine

npm test        # node test/engine.test.js

Project structure

index.html          # UI
css/styles.css      # styles (light/dark aware, print-friendly)
js/engine.js        # pure ATS engine (scoring, keywords, generation, audit)
js/app.js           # UI controller (form ↔ engine + AI backend, copy/download)
api/enhance.js      # Vercel serverless function — calls the Claude API
vercel.json         # Vercel config
test/engine.test.js # lightweight assertions for the engine

Export

• Copy markdown — paste anywhere.
• Download .md — the raw markdown file.
• Download PDF — opens a print-optimized, single-column view and triggers the browser's print-to-PDF. Text stays selectable (ATS-parseable), not rasterized. Allow pop-ups for this button.

Roadmap

• DOCX export.
• Multiple experience entries and templates.
• Semantic (embedding-based) keyword matching.