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 el CV no lo muestra, la puntuación es cero —esta skill reemplaza el cribado por intuición con una rúbrica ponderada construida solo de los imprescindibles de la oferta, donde la marca de la empresa o el prestigio de la escuela no valen nada—. Los chequeos eliminatorios van primero, dos cribadores independientes se recalibran cuando sus ratings divergen, y cada criterio lleva una justificación de una línea porque el cribado explicable es obligatorio para herramientas asistidas por IA y buena práctica en todas partes. Las guardas de sesgo son explícitas: ignorar nombre, foto, edad y dirección; nunca penalizar lagunas de empleo salvo probando la recencia de la habilidad directamente; y nunca usar la calidad del lenguaje del CV como proxy de capacidad.
---
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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