resume-ats

name

resume-ats

description

Score, review, and optimize a resume for Applicant Tracking Systems (ATS) using the canonical ResumeJson format. Use this when the user wants an ATS score, an ATS compliance review, ATS screening questions, or a rewritten resume optimized for ATS. Triggers on: "ATS score", "score my resume", "ATS review", "is my resume ATS-friendly", "optimize my resume for ATS", "improve my resume", "ATS questions", "screening questions". All AI steps run on the current Claude session — no OpenAI, no API key. Produce a ResumeJson first with the resume-convert skill if you only have a PDF/Markdown.

resume-ats

Run the AI-powered ATS pipeline over a ResumeJson. You, Claude, perform every step in this session — there is no external model and no API key. Each step writes a JSON file, which you then gate through the toolbelt's validate command and self-correct until it passes.

The toolbelt CLI

Deterministic operations (fetch a contract, validate output) run through the bundled CLI. In this repo (development):

npm run resumejson_claude -- <command> [args]

Command	Purpose
schema <name>	Print the JSON Schema for the contract you must produce
validate -i <file> --schema <name>	Zod-validate; exits non-zero and lists path: message problems

Schema names used here: ats-score, ats-review, ats-question, and resume (for the enriched/optimized resume outputs).

The loop for every step

1. Fetch the contract: schema <name>.
2. Read the input JSON (resume, and where noted questions/review).
3. Produce the output object per the rubric below — never fabricate facts, skills, employers, dates, or credentials.
4. Write it to a file.
5. validate -i <file> --schema <name>; fix reported problems; re-validate.
6. Present a short human summary.

Capabilities

Inputs/outputs are ResumeJson unless a schema is named.

Step	In	Out (schema)
ats_score	resume	ats-score
ats_review	resume	ats-review
ats_question	resume	ats-question (answers empty)
ats_answering	resume + ats-question	ats-question (answers filled)
ats_answered	resume + answered ats-question	resume (enriched)
ats_optimize	resume + ats-review	resume (optimized)

ats_score → ats-score

Numerical ATS readiness audit; use it as a before/after gauge. Score sections and the overall resume 0–100 as parsed by Workday, Greenhouse, Lever, Taleo, iCIMS:

• Structure (30%) — standard headings, contact info in metadata, clean hierarchy, no content hidden in unusual fields.
• Content (40%) — bullets start with action verbs; quantified achievements; no vague language ("responsible for", "helped with"); keywords spelled out AND abbreviated ("Amazon Web Services (AWS)"); a comprehensive Skills section.
• Formatting (15%) — no parser-breaking unicode (smart quotes, em dashes); no excessive nesting; consistent, parseable dates.
• Completeness (15%) — summary, contact info, education, skills, and work entries with company/title/dates/description.

70+ ≈ likely to pass most filters; below 50 ≈ likely filtered out. Flag every concrete issue with a specific fix. Fill topStrengths / topImprovements (max 5 each) and keywordDensity.

ats_review → ats-review

Not a score — an actionable report: what exactly must change to pass ATS filters. For every issue give the exact problem, the exact action, and a before/after example. Focus on parsing compatibility (headings, structured contact info, machine-parseable dates, no parser-breaking unicode), keyword optimization (skills in a Skills section AND in context; acronyms expanded on first use), content structure (action verbs, quantified metrics, no vague language), and completeness. Order actions by impact (deal breakers → high → nice-to-have), populate quickWins (max 5) and dealBreakers, and set complianceLevel. Be specific — never "add more keywords"; say which keywords, where.

ats_question → ats-question (answers empty)

Identify information that is absent and that only the user can supply — data you cannot infer or fabricate. Do NOT flag formatting/verbs/keyword density (the review handles those). Look for: target role (critical if absent); each missing contact field (email, phone, LinkedIn, city — one question each); top-3 unquantified achievements (quote the bullet, ask for a number); employment gaps

3 months; missing work-experience fields (title, company, dates, location); incomplete education; certifications missing issuer/date; skills named in experience but absent from the Skills section. Rules: ask about missing data only; max 7 questions; each cites its section in context; one thing per question; critical first. Leave every answer empty.

ats_answering → ats-question (answers filled)

Given the resume and an unanswered ats-question file, fill each answer as if you were the candidate: one short sentence (< 15 words), concrete values where relevant, consistent with the question's context. Answer every question. Output the same ats-question shape with answers populated. (This simulates the user for a full dry run; a real user would answer instead.)

ats_answered → resume (enriched)

Given the original resume and an answered ats-question file, fold each answer into the correct section (the context field says where) and return an enriched ResumeJson. Insert metrics into the relevant highlights; put missing contact data into basics. Preserve all existing content unless an answer directly supersedes it; skip empty answers — never invent. Validate against resume.

ats_optimize → resume (optimized)

Given the resume and an ats-review, apply the review's actions and return an optimized ResumeJson (same schema, same field names — do not rename or invent fields; keep every top-level key, using null/[] for empty). Fix deal breakers first, then high-priority actions and quick wins. Preserve accuracy: never fabricate experience/skills/dates/credentials; you may rephrase and restructure, not add new entries. Ensure contact info lives in basics; expand acronyms on first use; start highlights with action verbs; add metrics only where the original content implies them; keep dates ISO-8601. Validate against resume.

Full pipeline

When the user asks to "optimize / improve my resume for ATS", run the whole pipeline (a real user answers ats_question themselves instead of ats_answering):

ats_score     resume.json                          -> resume.ats_score.json          (baseline)
ats_question  resume.json                          -> questions.unanswered.json
ats_answering resume.json + questions.unanswered   -> questions.answered.json         (or user answers)
ats_answered  resume.json + questions.answered     -> resume.answered.json
ats_review    resume.answered.json                 -> resume.ats_review.json
ats_optimize  resume.answered.json + ats_review    -> resume.optimized.json
ats_score     resume.optimized.json                -> resume.optimized.ats_score.json (confirm improvement)

Validate every output against its schema before moving to the next step. Finish with a short before/after summary (overall score delta, deal breakers resolved).

Notes

• Format conversion (PDF/Markdown ↔ JSON) lives in the sibling resume-convert skill. Produce a ResumeJson there first if you only have a PDF or Markdown.
• No credentials are ever needed — the reasoning is you; the validation is local TypeScript.