Design structured, fair CV/resume screening rubrics and score resumes against them with consistent, explainable verdicts. Use when the user asks to screen, score, rank, shortlist, or evaluate resumes/CVs against a role, or to build a screening scorecard for recruiters or an ATS.
npx skillsify add resume-screening-rubricSi le CV ne le montre pas, le score est zéro — cette skill remplace le tri à l'intuition par une grille pondérée construite uniquement des exigences de l'annonce, où la marque de l'employeur ou le prestige de l'école ne comptent pour rien. Les contrôles éliminatoires passent d'abord, deux évaluateurs indépendants se recalibrent quand les notes divergent, et chaque critère porte une justification d'une ligne car un tri explicable est obligatoire pour les outils assistés par IA et une bonne pratique partout. Les gardes anti-biais sont explicites : ignorer nom, photo, âge et adresse ; ne jamais pénaliser les trous de carrière sauf à tester la récence de la compétence directement ; et ne jamais prendre la qualité rédactionnelle du CV pour un proxy de capacité.
---
name: resume-screening-rubric
description: "Design structured, fair CV/resume screening rubrics and score resumes against them with consistent, explainable verdicts. Use when the user asks to screen, score, rank, shortlist, or evaluate resumes/CVs against a role, or to build a screening scorecard for recruiters or an ATS."
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# Resume Screening Rubric
## Purpose
Replace gut-feel CV review with a structured rubric — the same discipline AI-ATS projects automate: parse → extract → score → rank → explain.
## Rubric design (before scoring anything)
Score each candidate 0–4 on 5–7 criteria derived ONLY from the JD's must-haves:
| Criterion | Weight | 0 | 2 | 4 anchors |
|---|---|---|---|---|
| Core skill match | 30% | none | partial, adjacent | direct, recent, at required depth |
| Relevant impact | 25% | duties only | some metrics | quantified outcomes at similar scale |
| Domain/industry fit | 15% | none | adjacent | direct |
| Seniority calibration | 15% | 2+ levels off | 1 level off | exact |
| Trajectory & stability | 10% | red flags | mixed | consistent growth |
| Education/certs (only if required) | 5% | missing | partial | met |
## Scoring rules
- Evidence-based only: if the CV doesn't show it, it's a 0 — never infer from company brand or school prestige.
- Knock-out checks first (work authorization, mandatory license/cert) — binary, then rubric.
- Two independent screeners on a sample; recalibrate if ratings diverge >1 point.
- Decision bands: ≥80% shortlist, 60–79% hold/talent pool, <60% decline (with respectful template).
- One-line justification per criterion — explainable screening is mandatory for AI-assisted tools (EU AI Act) and good practice everywhere.
## Bias guards
- Hide/ignore: name, photo, age, address, marital status when scoring (anonymized screening where possible).
- Penalizing employment gaps is disallowed unless the criterion is skill recency — then test skill directly.
- CV language quality is NOT a proxy for ability unless the role requires it.
## Outputs
- Rubric table (markdown/CSV)
- Per-candidate scorecard: criterion scores, weighted total, band, justifications, interview-probe suggestions
- Batch mode: ranking table of all candidates + shortlist recommendation
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