resume-screening

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Intelligent resume parsing and candidate screening with bias-reduction capabilities

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Resume Parsing and Screening Skill

Overview

The Resume Parsing and Screening skill provides intelligent resume analysis and candidate evaluation capabilities. This skill enables structured data extraction, skills matching, fit scoring, and bias-reduction through standardized evaluation methods.

Capabilities

Resume Parsing

Parse resumes in multiple formats (PDF, Word, text)
Extract structured data (skills, experience, education)
Normalize job titles and company names
Handle international formats and languages
Process LinkedIn profiles and portfolios

Skills Matching

Match candidates against job requirements
Map candidate skills to role competencies
Identify transferable skills
Calculate skills gap analysis
Suggest development areas

Fit Scoring

Calculate fit scores based on configurable criteria
Weight experience vs. skills vs. education
Apply minimum threshold filters
Generate comparative rankings
Provide score explanations

Red Flag Detection

Detect potential red flags (gaps, inconsistencies)
Flag employment tenure concerns
Identify career trajectory issues
Note credential verification needs
Surface information inconsistencies

Candidate Summaries

Generate candidate summaries for hiring managers
Create comparison matrices
Highlight strengths and development areas
Summarize relevant experience
Note cultural fit indicators

Bias Reduction

Support bias-reduction through standardized evaluation
Remove identifying information for blind review
Apply consistent scoring criteria
Track demographic patterns in screening
Generate diversity pipeline reports

Usage

Resume Parsing

const parseConfig = {
  format: 'auto-detect',
  extractFields: [
    'contact',
    'experience',
    'education',
    'skills',
    'certifications'
  ],
  normalization: {
    titles: true,
    companies: true,
    skills: 'standard-taxonomy'
  },
  redFlagRules: {
    maxGapMonths: 12,
    minTenureMonths: 12,
    flagJobHopping: true
  }
};

Candidate Scoring

const scoringCriteria = {
  jobRequirements: {
    requiredSkills: ['Python', 'SQL', 'Machine Learning'],
    preferredSkills: ['AWS', 'Spark', 'Docker'],
    minExperienceYears: 5,
    education: {
      required: 'Bachelors',
      preferredFields: ['Computer Science', 'Data Science']
    }
  },
  weights: {
    requiredSkills: 40,
    preferredSkills: 20,
    experience: 25,
    education: 15
  },
  thresholds: {
    autoAdvance: 80,
    review: 60,
    autoReject: 40
  }
};

Process Integration

This skill integrates with the following HR processes:

Process	Integration Points
full-cycle-recruiting.js	Candidate screening, ranking
structured-interview-design.js	Interview focus areas

Best Practices

Consistent Criteria: Apply the same scoring criteria to all candidates
Regular Calibration: Review scoring outcomes for consistency
Bias Monitoring: Track outcomes by demographic groups
Human Review: Use AI scoring as input, not final decision
Transparency: Be prepared to explain scoring rationale
Skills Updates: Regularly update skills taxonomies

Metrics and KPIs

Metric	Description	Target
Screening Accuracy	Correlation with interview performance	>0.7
Time to Screen	Minutes per resume	<5 min
Adverse Impact	Score distribution across groups	No significant difference
False Positive Rate	Low-fit candidates advanced	<15%
False Negative Rate	High-fit candidates rejected	<10%

Related Skills

SK-001: ATS Integration (candidate sourcing)
SK-003: Interview Questions (evaluation continuity)