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applicant-screening skill

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/skills/lijie420461340/applicant-screening

This skill screens job applications against requirements, scoring candidates to identify top fits and inform interview decisions.

npx playbooks add skill openclaw/skills --skill applicant-screening

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: Applicant Screening
description: Screen job applications against requirements and score candidates
author: claude-office-skills
version: "1.0"
tags: [hr, recruitment, hiring, screening, resume]
models: [claude-sonnet-4, claude-opus-4]
tools: [computer, file_operations]
---

# 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
```markdown
## 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
```markdown
# Candidate Screening: [Name]

## Quick Summary
| Attribute | Value |
|-----------|-------|
| **Position** | [Job Title] |
| **Score** | [X]/100 |
| **Recommendation** | 🟢 Interview / 🟡 Maybe / 🔴 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] | ✅ | 7 years at 2 companies | 20/20 |
| [AWS experience] | ✅ | AWS Certified, 3 years | 15/15 |
| [Bachelor's CS] | ✅ | 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] | ✅ | Built recommendation system | +5 |
| [Startup exp] | ✅ | 2 early-stage startups | +5 |
| [Open source] | ❌ | Not mentioned | 0 |

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

## Strengths 💪
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
```markdown
# Applicant Ranking: [Position]

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

## Summary
| Category | Count | % |
|----------|-------|---|
| 🟢 Strong Interview | [X] | [%] |
| 🟡 Phone Screen | [X] | [%] |
| 🔵 Maybe/Hold | [X] | [%] |
| 🔴 Not a Fit | [X] | [%] |

## Top Candidates

### 🥇 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] |

### 🥈 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] |

### 🥉 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

### Legal Considerations
- 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

Overview

This skill screens job applications against role requirements and produces consistent candidate scores and rankings. It helps identify must-have qualifications, flag concerns, and generate interview prompts. Use it to accelerate shortlisting and maintain documented, fair decisions.

How this skill works

The skill parses resumes and application data, maps candidate attributes to a configurable requirements matrix, and applies weighted scoring for must-have and nice-to-have items. It outputs individual screening reports or batch rankings with strengths, concerns, red flags, and recommended next steps. Reports include evidence lines, numeric scores, and suggested interview questions.

When to use it

Initial resume triage for high-volume roles
Shortlisting candidates for phone screens or interviews
Comparing applicants against explicit skill and experience criteria
Re-evaluating a candidate pool after updating role requirements
Documenting screening decisions for audit or compliance

Best practices

Define clear must-have vs. nice-to-have requirements before screening
Use consistent weights and document the scoring rubric
Blind or redact identifying info to reduce bias when possible
Record evidence for each match or gap to support decisions
Have HR review disqualifiers and legal considerations

Example use cases

Screen 50 software engineer applications for 5+ years Python and AWS experience
Generate individual reports with strengths, concerns, and interview questions
Rank candidates into tiers (Interview / Phone Screen / Hold / Pass) for hiring managers
Identify common skill gaps across an applicant pool and inform sourcing strategy
Apply the same rubric across roles to ensure consistent hiring standards

FAQ

Can this verify employment or certifications?

No. The skill flags evidence mentioned in resumes or applications but cannot independently verify employment or certification status; follow up with references and checks.

How are scores calculated?

Scores combine weighted must-have requirements and bonus points for nice-to-have items based on a configurable rubric. Final recommendations map to score ranges (e.g., 80+ = strong interview).