Resume fit scorer
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npx -y skills add ebarti/skills --skill resume-fit-scorerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Scores how well a resume fits a specific job description on a 0–10 scale across 6 weighted dimensions (JD match, achievement strength, targeting, ATS-parseability, red flags, language), returns a brutally honest critique, and emits structured prioritized fixes. Distilled from "Resumes For Dummies" (AI-era ed.). Built for automated pipelines (e.g. JobHunter) as the verifier in a write→score→revise loop. Use this skill when: - Gating whether a tailored resume is good enough to submit - Producing a 0–10 fit score with per-dimension sub-scores - Generating an actionable critique + prioritized fixes for revision - Ranking multiple resume variants against one JD - Auditing a resume for ATS or red-flag risk before applying - Checking which JD must-haves a resume fails to cover
SKILL.md
3.4 KB, as published. Nobody here has run it
resume-fit-scorer
Judges a resume against one job — harshly, on a 0–10 scale — and explains exactly why. It scores 6 weighted dimensions, detects red flags, computes must-have coverage, and returns a critique plus impact-ordered fixes that the writer can act on. Built from "Resumes For Dummies" (AI-era ed.), chapters 1, 2, 3, 5, 6, 10, 15, 16, 18.
Role in the JobHunter pipeline
job-description-analyzer → resume-content-writer → [resume-fit-scorer] → (revise loop)
TargetProfile ResumeContent FitScore ──┐
▲ │ prioritized_fixes
└──────────────────────────────┘
It accepts a resume (plain text or resume-content-writer output) + a TargetProfile, and returns a FitScore. See workflows/score-resume-against-jd.md for the full I/O contract and the rubric.
Default rubric (overridable via weights)
| Dimension | Weight |
|---|---|
| JD match | 30% |
| Achievement strength | 25% |
| Targeting & focus | 15% |
| ATS parseability | 10% |
| Red flags | 10% |
| Language | 10% |
Verdicts use threshold comparisons on the real-valued overall, not integer-rounded bands.
| Overall | Verdict |
|---|---|
< 3.0 | Trash |
>= 3.0 and < 5.0 | Weak |
>= 5.0 and < 7.0 | OK |
>= 7.0 and < 9.0 | Strong |
>= 9.0 | Excellent |
Calibrated harshly: a generic, untargeted resume should land 3–5, not 7.
Quick Start
- Read
guidelines.md— it routes from your task/symptom to the right files. - For an end-to-end score, follow
workflows/score-resume-against-jd.md. - Load only the reference files the guidelines point to.
- Compute the weighted overall, apply red-flag/missing-must-have ceilings, and return actionable fixes.
Contents
References
| Category | Files | Purpose |
|---|---|---|
references/scoring-rubric/ | knowledge, rules, examples | The 6 dimensions, weights, 0–10 bands, weighted formula, calibration |
references/jd-match/ | knowledge, rules, examples | The 30% dimension: ATS + glance + decision-maker screening, coverage |
references/red-flags/ | smells, checklist | Red-flag catalog + fast scan / auto-reject criteria |
Workflows
| Task | Workflow |
|---|---|
| Score a resume against a JD (0–10 + critique + fixes) | workflows/score-resume-against-jd.md |
Guidelines
See guidelines.md for task/symptom-based file selection, a decision tree, the full file index, and common combinations.