resume-parser

name: resume-parser
description: Extract structured data from resumes in any format (PDF, DOCX, LinkedIn). Use when ingesting candidate applications or building talent databases.
license: MIT
compatibility: claude-code, cursor, cline, windsurf
metadata:
industry: hr-software
segment: applicant-tracking
function: talent-acquisition
value_driver: efficiency
complexity: low
source_systems: greenhouse, ashby, workday, lever
allowed-tools: Read, Write, Edit

Summary

This skill extracts structured, machine-readable data from unstructured resumes. It handles multiple formats (PDF, DOCX, plain text, LinkedIn exports) and normalizes the output for downstream processing like candidate screening or database storage.

Inputs

Outputs

Workflow

1. Format Detection

• Identify input format (PDF, DOCX, TXT, LinkedIn JSON)
• Extract raw text using appropriate parser
• Preserve section structure where possible

2. Section Identification

Identify standard resume sections:
- Contact Information
- Summary/Objective
- Work Experience
- Education
- Skills
- Certifications
- Projects
- Publications

3. Entity Extraction

Contact Info:
- Name (handle international name formats)
- Email (validate format)
- Phone (normalize to E.164)
- Location (city, state, country)
- LinkedIn URL

Experience:

{
  "company": "Acme Corp",
  "title": "Senior Engineer",
  "start_date": "2020-03",
  "end_date": "2023-06",
  "current": false,
  "description": "Led backend team...",
  "achievements": ["Reduced latency 40%", "Mentored 3 juniors"]
}

Education:

{
  "institution": "MIT",
  "degree": "BS",
  "field": "Computer Science",
  "graduation_date": "2018-05",
  "gpa": 3.8
}

4. Skill Normalization

• Map variations to canonical names (JS → JavaScript)
• Categorize: languages, frameworks, tools, soft skills
• Infer skills from experience descriptions

5. Quality Scoring

Calculate parse confidence based on:
- Fields successfully extracted
- Date format consistency
- Section completeness

Supported Formats

Example Output

{
  "candidate_name": "Sarah Chen",
  "email": "[email protected]",
  "phone": "+1-415-555-0123",
  "location": {
    "city": "San Francisco",
    "state": "CA",
    "country": "US"
  },
  "linkedin_url": "linkedin.com/in/sarahchen",
  "summary": "Senior software engineer with 8 years of experience...",
  "experience": [
    {
      "company": "Stripe",
      "title": "Staff Engineer",
      "start_date": "2021-01",
      "end_date": null,
      "current": true,
      "description": "Lead payments infrastructure team",
      "achievements": [
        "Scaled payment processing 10x",
        "Reduced fraud by 30%"
      ]
    }
  ],
  "education": [
    {
      "institution": "Stanford University",
      "degree": "MS",
      "field": "Computer Science",
      "graduation_date": "2015-06"
    }
  ],
  "skills": {
    "languages": ["Python", "Go", "TypeScript"],
    "frameworks": ["Django", "React", "Kubernetes"],
    "tools": ["PostgreSQL", "Redis", "AWS"],
    "soft_skills": ["Leadership", "Mentoring"]
  },
  "certifications": [
    {
      "name": "AWS Solutions Architect",
      "issuer": "Amazon",
      "date": "2022-03"
    }
  ],
  "parse_confidence": 95
}

Error Handling

Integration Points

• Upstream: File uploads, email attachments, ATS imports
• Downstream: candidate-screening, database storage, search indexing