openclaw / applicant-screening
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/applicant-screening && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/4315" && unzip -o skill.zip -d .claude/skills/applicant-screening && rm skill.zip
Project Skills
This skill will be saved in .claude/skills/applicant-screening/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
Screen job applications against requirements and score candidates
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Skill Content
--- name: Applicant Screening description: Screen job applications against requirements and score candidates author: claude-office-skills version: "1.0" tags: [hr, recruitment, hiring, screening, resume] models: [claude-sonnet-4, claude-opus-4] tools: [computer, file_operations] --- # 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 | 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