resume-audit
Skill0 starsChanged 3 months agoMITView on GitHub
Scores a resume's structure, quantified impact, and ATS keyword coverage against a job description. Reads markdown or plain text (convert PDF/docx first with doc-reader) and checks contact info, standard sections, quantified bullet ratio, action-verb openers, weak phrases ("responsible for"), and — given a JD — which required keywords are missing. Offline, deterministic, no LLM needed.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- shaokeyibb/anti-asu-skillstheir own version, first seen Aug 25, 2026
- JhCircle/Defense-ASu-skillstheir own version, first seen Aug 17, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- shaokeyibb/anti-asu-skills★ 28same name
- JhCircle/Defense-ASu-skills★ 4same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository69 other files it gives its agents.
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- skills/AGENTS.mdAGENTS.md
- skills/ultimate-browsing/engine/AGENTS.mdAGENTS.md
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The file
Files
Resume Audit
Recruiters and ATS software filter on mechanical signals before a human reads a word. This scores those signals before you hit submit.
Commands
python3 scripts/resume_audit.py resume.md
python3 scripts/resume_audit.py resume.md --jd job-posting.txt --json
python3 scripts/resume_audit.py resume.md --min-bullet-ratio 0.5Checks
| Check | Pass condition |
contact_info | email, phone, or profile URL present |
sections | experience + skills sections detected |
quantified_bullets | ratio >= --min-bullet-ratio (default 0.4) |
action_verbs | >=50% of bullets open with led/built/shipped/grew/... |
weak_phrases | zero "responsible for" / "worked on" / "helped with" |
With --jd, reports ATS coverage: which JD keywords appear in the resume,
which tech keywords are missing, and a strong/partial/weak verdict.
Workflow
- Convert the resume:
doc-reader resume.pdf > resume.md - Audit:
resume_audit.py resume.md --jd target-role.txt - Fix the failing checks — add numbers to bullets, replace weak phrases, weave missing tech keywords into real accomplishments (never keyword-stuff).
Pairs with
doc-reader (format conversion), nbj-write-clearly (making the prose land
after the structure passes).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.