Applicant Screening

# ═══════════════════════════════════════════════════════════════════════════════
# CLAUDE OFFICE SKILL - Enhanced Metadata v2.0
# ═══════════════════════════════════════════════════════════════════════════════

# Basic Information
name: Applicant Screening
description: "Screen job applications against requirements and score candidates"
version: "1.0"
author: claude-office-skills
license: MIT

# Categorization
category: hr
tags:
  - screening
  - hiring
  - recruitment
  - evaluation
department: HR

# AI Model Compatibility
models:
  recommended:
    - claude-sonnet-4
    - claude-opus-4
  compatible:
    - claude-3-5-sonnet
    - gpt-4
    - gpt-4o

# MCP Tools Integration
mcp:
  server: office-mcp
  tools:
    - extract_text_from_pdf
    - extract_text_from_docx
    - analyze_document_structure

# Skill Capabilities
capabilities:
  - candidate_evaluation
  - requirement_matching
  - scoring

# Language Support
languages:
  - en
  - zh

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.]

## 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