Screen job applications against requirements and score candidates
npx skills add https://github.com/claude-office-skills/skills --skill Applicant Screening
Screen job applications against role requirements to identify top candidates efficiently.
This skill helps you:
"Screen this resume against our [Job Title] requirements"
"Evaluate this application for the [Position] role"
"Screen these 10 applications for the Senior Developer position"
"Rank these candidates based on our requirements"
"Screen for: 5+ years Python, AWS experience required, ML nice-to-have"
## 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
# Candidate Screening: [Name]
## Quick Summary
| Attribute | Value |
|-----------|-------|
| **Position** | [Job Title] |
| **Score** | [X]/100 |
| **Recommendation** | π’ Interview / π‘ Maybe / π΄ 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] | β
| 7 years at 2 companies | 20/20 |
| [AWS experience] | β
| AWS Certified, 3 years | 15/15 |
| [Bachelor's CS] | β
| BS Computer Science, MIT | 10/10 |
| [Team lead exp] | β οΈ | Led 2-person team | 5/10 |
**Must-Have Score**: [X]/[Total]
### Nice-to-Have
| Requirement | Met? | Evidence | Bonus |
|-------------|------|----------|-------|
| [ML experience] | β
| Built recommendation system | +5 |
| [Startup exp] | β
| 2 early-stage startups | +5 |
| [Open source] | β | Not mentioned | 0 |
**Nice-to-Have Bonus**: +[X] points
## Strengths πͺ
1. [Strength 1 with evidence]
2. [Strength 2 with evidence]
3. [Strength 3 with evidence]
## Concerns β οΈ
1. [Concern 1 - question to ask in interview]
2. [Concern 2 - what to verify]
## Red Flags π©
- [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]
# Applicant Ranking: [Position]
**Date**: [Date]
**Total Applications**: [X]
**Reviewed**: [X]
## Summary
| Category | Count | % |
|----------|-------|---|
| π’ Strong Interview | [X] | [%] |
| π‘ Phone Screen | [X] | [%] |
| π΅ Maybe/Hold | [X] | [%] |
| π΄ Not a Fit | [X] | [%] |
## Top Candidates
### π₯ 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] |
### π₯ 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] |
### π₯ Tier 3: Maybe/Hold (Score 50-64)
| Name | Score | Reason for Hold |
|------|-------|-----------------|
| [Name] | 58 | [Reason] |
### β 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]
| 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 |
| 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 |
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Take claude-office-skills/applicant screening from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.