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Applicant Screening Agent Skill

Screen job applications against requirements and score candidates

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
353
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/claude-office-skills/skills --skill Applicant Screening

The instruction itself

19 sections, as written by the author

Applicant Screening

Screen job applications against role requirements to identify top candidates efficiently.

Overview

This skill helps you:

  • Evaluate resumes against job requirements
  • Score candidates consistently
  • Identify must-have vs. nice-to-have qualifications
  • Flag potential concerns
  • Rank applicants for interviews

How to Use

Single Candidate

"Screen this resume against our [Job Title] requirements"
"Evaluate this application for the [Position] role"

Batch Screening

"Screen these 10 applications for the Senior Developer position"
"Rank these candidates based on our requirements"

With Criteria

"Screen for: 5+ years Python, AWS experience required, ML nice-to-have"

Screening Framework

Requirements Matrix

## Job Requirements: [Position]

### Must-Have (Required)
| Requirement | Weight | Criteria |
|-------------|--------|----------|
| [Skill 1] | 20% | [X] years experience |
| [Skill 2] | 15% | [Certification/level] |
| [Education] | 10% | [Degree type] |
| [Experience] | 25% | [Industry/role type] |

### Nice-to-Have (Preferred)
| Requirement | Bonus | Criteria |
|-------------|-------|----------|
| [Skill 3] | +5pts | [Description] |
| [Skill 4] | +5pts | [Description] |
| [Trait] | +3pts | [Indicator] |

### Disqualifiers
- [ ] No work authorization
- [ ] Below minimum experience
- [ ] Missing required certification
- [ ] Salary expectation mismatch

Output Formats

Individual Screening Report

# Candidate Screening: [Name]

## Quick Summary
| Attribute | Value |
|-----------|-------|
| **Position** | [Job Title] |
| **Score** | [X]/100 |
| **Recommendation** | 🟒 Interview / 🟑 Maybe / πŸ”΄ Pass |

## Candidate Profile
- **Name**: [Full Name]
- **Location**: [City, State]
- **Current Role**: [Title] at [Company]
- **Total Experience**: [X] years
- **Education**: [Degree, School]

## Requirements Match

### Must-Have Requirements
| Requirement | Met? | Evidence | Score |
|-------------|------|----------|-------|
| [5+ years Python] | βœ… | 7 years at 2 companies | 20/20 |
| [AWS experience] | βœ… | AWS Certified, 3 years | 15/15 |
| [Bachelor's CS] | βœ… | BS Computer Science, MIT | 10/10 |
| [Team lead exp] | ⚠️ | Led 2-person team | 5/10 |

**Must-Have Score**: [X]/[Total]

### Nice-to-Have
| Requirement | Met? | Evidence | Bonus |
|-------------|------|----------|-------|
| [ML experience] | βœ… | Built recommendation system | +5 |
| [Startup exp] | βœ… | 2 early-stage startups | +5 |
| [Open source] | ❌ | Not mentioned | 0 |

**Nice-to-Have Bonus**: +[X] points

## Strengths πŸ’ͺ
1. [Strength 1 with evidence]
2. [Strength 2 with evidence]
3. [Strength 3 with evidence]

## Concerns ⚠️
1. [Concern 1 - question to ask in interview]
2. [Concern 2 - what to verify]

## Red Flags 🚩
- [If any - employment gaps, inconsistencies, etc.]

## Interview Questions
Based on this candidate's profile, consider asking:
1. [Question about specific experience]
2. [Question about concern area]
3. [Question about growth potential]

## Overall Assessment
[2-3 sentence summary of fit]

**Final Score**: [X]/100
**Recommendation**: [Interview / Phone Screen / Pass]
**Priority**: [High / Medium / Low]

Batch Ranking Report

# Applicant Ranking: [Position]

**Date**: [Date]
**Total Applications**: [X]
**Reviewed**: [X]

## Summary
| Category | Count | % |
|----------|-------|---|
| 🟒 Strong Interview | [X] | [%] |
| 🟑 Phone Screen | [X] | [%] |
| πŸ”΅ Maybe/Hold | [X] | [%] |
| πŸ”΄ Not a Fit | [X] | [%] |

## Top Candidates

### πŸ₯‡ Tier 1: Strong Interview (Score 80+)

| Rank | Name | Score | Key Strengths | Concerns |
|------|------|-------|---------------|----------|
| 1 | [Name] | 92 | [Strengths] | [Concerns] |
| 2 | [Name] | 88 | [Strengths] | [Concerns] |
| 3 | [Name] | 85 | [Strengths] | [Concerns] |

### πŸ₯ˆ Tier 2: Phone Screen (Score 65-79)

| Rank | Name | Score | Key Strengths | Gap to Address |
|------|------|-------|---------------|----------------|
| 4 | [Name] | 75 | [Strengths] | [Gap] |
| 5 | [Name] | 72 | [Strengths] | [Gap] |

### πŸ₯‰ Tier 3: Maybe/Hold (Score 50-64)

| Name | Score | Reason for Hold |
|------|-------|-----------------|
| [Name] | 58 | [Reason] |

### ❌ Not Proceeding (Score <50)

| Name | Score | Primary Reason |
|------|-------|----------------|
| [Name] | 45 | Missing required [X] |
| [Name] | 38 | Below minimum experience |

## Insights

### Applicant Pool Quality
[Assessment of overall pool quality]

### Common Strengths
- [Frequently seen strength]
- [Frequently seen strength]

### Common Gaps
- [What most candidates lack]
- [Skill shortage in pool]

### Recommendations
1. [Action for top candidates]
2. [Suggestion for sourcing if pool weak]

Scoring Rubric

Experience Scoring

| Years | Entry | Mid | Senior | Lead |

|-------|-------|-----|--------|------|

| 0-1 | 10/10 | 3/10 | 0/10 | 0/10 |

| 2-3 | 8/10 | 7/10 | 3/10 | 0/10 |

| 4-5 | 5/10 | 10/10 | 7/10 | 3/10 |

| 6-8 | 3/10 | 8/10 | 10/10 | 7/10 |

| 9+ | 0/10 | 5/10 | 10/10 | 10/10 |

Education Scoring

| Level | Technical Role | Non-Technical |

|-------|----------------|---------------|

| PhD | 10/10 | 8/10 |

| Master's | 9/10 | 9/10 |

| Bachelor's | 8/10 | 10/10 |

| Associate's | 5/10 | 7/10 |

| Bootcamp | 6/10 | N/A |

| Self-taught | 4/10 | N/A |

Best Practices

Fair Screening

  • Focus on job-related criteria only
  • Ignore protected characteristics
  • Use consistent scoring
  • Document decisions
  • Consider diverse backgrounds

Bias Awareness

  • Name/gender bias: Focus on qualifications
  • Affinity bias: Diverse interview panels
  • Confirmation bias: Score before gut feeling
  • Halo effect: Evaluate each criterion separately
  • Only use job-relevant criteria
  • Apply standards consistently
  • Keep screening records
  • Have HR review process
  • Consider adverse impact

Limitations

  • Cannot verify employment history
  • May miss context from non-traditional backgrounds
  • Scoring is guidance, not absolute
  • Cannot assess cultural fit or soft skills fully
  • Human judgment essential for final decisions

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How to use it

Copy the folder

Take claude-office-skills/applicant screening from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side β€” one of them will be ignored.