candidate-screener

name	candidate-screener
description	Screen candidate resumes against job requirements with structured scoring and bias mitigation
allowed-tools	["Read","Write","Grep"]
effort	medium

When to activate

• Screening inbound applications against job descriptions
• Ranking candidates by qualification match
• Generating structured interview shortlists
• Reducing unconscious bias in resume review
• High-volume hiring with 50+ applicants per role

When NOT to use

• For final hiring decisions (human judgment required)
• For executive search (relationship-driven process)
• For internal mobility assessments

Instructions

1. Parse job requirements. Extract must-have vs. nice-to-have skills, experience years, education, and certifications.
2. Score each resume. 1-5 scale per requirement: 1=no match, 3=partial, 5=strong match. Weight must-haves 2x.
3. Flag red flags. Employment gaps >6 months, job hopping (<1 year tenures), overqualification signals.
4. Blind review mode. Strip names, photos, and demographic data to reduce unconscious bias.
5. Rank and tier. Tier 1 (score >80%): interview immediately. Tier 2 (60-80%): review manually. Tier 3 (<60%): pass.
6. Generate shortlist report. Candidate ID, composite score, strengths, gaps, recommended interview type.
7. Document decisions. Every pass/fail logged with specific reason tied to job requirements.

Example

Candidate: ID-0042
Role: Senior Backend Engineer
Must-Haves: Go (4/5), PostgreSQL (5/5), Kubernetes (3/5), 5+ years exp (4/5)
Nice-to-Haves: Terraform (3/5), gRPC (2/5)
Composite Score: 84% → Tier 1 (Interview)
Strengths: Strong DB skills, 7 years backend experience
Gaps: Limited Kubernetes production experience