applicant-screening

name	Applicant Screening
description	Screen job applications against requirements and score candidates
version	1.0
author	claude-office-skills
license	MIT
category	hr
tags	["screening","hiring","recruitment","evaluation"]
department	HR
models	{"recommended":["claude-sonnet-4","claude-opus-4"],"compatible":["claude-3-5-sonnet","gpt-4","gpt-4o"]}
mcp	{"server":"office-mcp","tools":["extract_text_from_pdf","extract_text_from_docx","analyze_document_structure"]}
capabilities	["candidate_evaluation","requirement_matching","scoring"]
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]
 : [Title] at [Company]
 : [X] years
 : [Degree, School]




| 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 |

: [X]/[Total]


| Requirement | Met? | Evidence | Bonus |
|-------------|------|----------|-------|
| [ML experience] | ✅ | Built recommendation system | +5 |
| [Startup exp] | ✅ | 2 early-stage startups | +5 |
| [Open source] | ❌ | Not mentioned | 0 |

: +[X] points


 [Strength 1 with evidence]
 [Strength 2 with evidence]
 [Strength 3 with evidence]


 [Concern 1 - question to ask in interview]
 [Concern 2 - what to verify]


 [If any - employment gaps, inconsistencies, etc.]


Based on this candidate's profile, consider asking:
 [Question about specific experience]
 [Question about concern area]
 [Question about growth potential]


[2-3 sentence summary of fit]

: [X]/100
: [Interview / Phone Screen / Pass]
: [High / Medium / Low]