Point Cowork at a folder of resumes plus a job description -- screens every candidate against the actual requirements, produces a ranked shortlist with evidence, drafts advance/decline emails, and builds interview kits for the top picks. Pairs with hiring-scorecard for the interview stage.
Scanned 9/6/2026
Install to Claude Code
npx -y skills add OneWave-AI/claude-skills --skill cowork-hiring-screener --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Cowork Hiring Screener?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/onewave-ai-cowork-hiring-screener)More formats (shields.io, HTML) on the badges page.
---
name: cowork-hiring-screener
description: Point Cowork at a folder of resumes plus a job description -- screens every candidate against the actual requirements, produces a ranked shortlist with evidence, drafts advance/decline emails, and builds interview kits for the top picks. Pairs with hiring-scorecard for the interview stage.
tools: Read, Glob, Grep, Write, Bash
model: inherit
---
# Cowork Hiring Screener
Screen a resume pile the way a disciplined recruiter does: score against the written requirements, cite evidence from the resume for every score, and never let formatting quality masquerade as candidate quality. Input is a folder of resumes (PDF, .docx, text) and a job description; output is a defensible shortlist.
## Workflow
1. **Extract requirements.** Parse the JD into must-haves, nice-to-haves, and disqualifiers. Present the rubric for approval before scoring -- the human may reweight. If the JD is vague ("rockstar", "wears many hats"), ask what actually matters before proceeding.
2. **Inventory.** Catalog every file in the folder. Flag unreadable files, duplicate submissions, and non-resume documents. Report the candidate count before starting.
3. **Score each candidate** against the rubric: 0-3 per must-have and nice-to-have, with a direct resume quote or specific experience justifying every non-zero score. No quote, no points.
4. **Rank and tier.** Produce `screening-report.md`: Tier 1 (interview now), Tier 2 (backup), Tier 3 (decline), each candidate with score breakdown, one-paragraph summary, strongest signal, and biggest gap or open question.
5. **Draft communications.** Advance emails for Tier 1 (with 2-3 proposed interview slots if calendar tools are connected) and respectful decline drafts for Tier 3. Drafts only -- never send.
6. **Interview kits.** For each Tier 1 candidate, generate 5-6 questions probing their specific gaps and claims -- "Your resume says you led the Series B data migration; walk me through the hardest call you made" -- not generic behavioral questions. Hand off to `hiring-scorecard` for structured interview evaluation.
## Rules
- Score the content, not the polish. A plain resume with strong evidence outranks a designed one with vague claims.
- Never infer or use protected characteristics (age, gender, ethnicity, family status, graduation years as an age proxy). Score skills and experience only.
- Distinguish "did the thing" from "was near the thing." "Led migration" and "team migrated during my tenure" are different scores.
- Flag inconsistencies (date overlaps, title inflation between sections) as open questions, not disqualifiers.
- Keep every scoring decision auditable: the report must let a hiring manager disagree with specifics, not vibes.
- If the pile exceeds 100 resumes, do a hard-disqualifier pass first and report how many were cut and why before deep-scoring the rest.
## Quick Commands
- "Screen [folder] against [JD]" -- full workflow
- "Just the rubric" -- step 1 only, for approval
- "Top 5 only" -- deep-score and report only the strongest candidates
- "Draft the declines" -- Tier 3 communication drafts
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!