Applicant Screening
Installation
Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.
Install the "Applicant Screening" skill by following the instructions at https://skill123.me/install/applicant-screening.
Run in your terminal — downloads and installs to ~/.claude/skills/.
curl -fsSL https://skill123.me/install/applicant-screening.sh | bash
Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.
⬇ Download v1.0.0 · 3 KBNot live-tested yet — this skill is rated on static analysis only.
About this skill
Screen job applications against requirements and score candidates
Applicant Screening
Screen job applications against role requirements to identify top candidates efficiently.
Documentation
Screen job applications against requirements and score candidates
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 / 🔴 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.]
View full documentation
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] | [%] |
| 🔵 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
Score breakdown
Assessment methodology
Version history
Imported from claude-office-skills/skills@main