Applicant Screening

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 79d91524ef4c · 27 Oct 2026Report an issue or request a re-scan

• Publisherclaude-office-skills GitHub organizationOwner of the repository this skill is read from
• Source folderclaude-office-skills/skills/applicant-screeningThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict79d91524ef4cA new audit runs when the source changes
• Repository statusNot archivedLast push 268 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Screen job applications against requirements and score candidates

---
# ═══════════════════════════════════════════════════════════════════════════════
# 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
```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 | 1

Excerpt, first 6,012 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/claude-office-skills/skills
git -C skills checkout 79d91524ef4c

Works in

1. 2026-10-27
   ✓SAFE B 89 / 100

   first audit · source 79d91524ef4c

Questions about Applicant Screening

• github.com/claude-office-skills/skills/tree/main/applicant-scree — the audited source at commit 79d91524ef4c and every finding aboveObserved · 27 Oct 2026

Spotted something wrong? Report a correction · Methodology