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cowork-hiring-screener

cowork-hiring-screener

github.com/onewave-aiOther39391 installsUpdated 1w ago
Repository

About cowork-hiring-screener

Cowork Hiring Screener is a resume-screening skill for hiring teams that need an evidence-based shortlist. It scores candidates against an approved job rubric, keeps decisions auditable, and drafts—but does not send—candidate communications.

Key features

  1. 01Extracts and presents job criteria
  2. 02Catalogs resume files and flags issues
  3. 03Scores candidates with resume evidence
  4. 04Ranks candidates into three tiers
  5. 05Drafts candidate emails and interview questions
  6. 06Avoids protected-characteristic scoring

Use cases

  1. 01Screen a resume folder against a job description
  2. 02Review a candidate shortlist with cited evidence
  3. 03Prepare tailored interview questions and email drafts

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill cowork-hiring-screener

After installing, type/cowork-hiring-screenerin Claude Code to confirm it loaded.

Details

From the project README

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

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