resume-screening

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

1. Consistent Criteria: Apply the same scoring criteria to all candidates
2. Regular Calibration: Review scoring outcomes for consistency
3. Bias Monitoring: Track outcomes by demographic groups
4. Human Review: Use AI scoring as input, not final decision
5. Transparency: Be prepared to explain scoring rationale
6. 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)