Background
Recruitment teams spend time on:
- Parsing PDF/Word/image resumes
- Aligning fields to job descriptions
- First-pass ranking
- Surfacing candidates worth human review
This use case builds parse → structure → score → rank as an assist layer, not a replacement for HR.
Key steps
- Parse resumes: PDF, Word, or image input.
- Extract fields: skills, experience, education, tenure.
- Compare to JD: match by dimensions, not free-form judgment.
- Produce ranked list with match reasons.
- Human review before any invitation or rejection.
Workflow sketch
Resumes in → Parse → Extract fields → Compare to JD → Rank + reasons → Human review → HR action
Skill recommendations
openclaw skills install @clawhub/summarize
Run openclaw security audit --deep before processing personal data. Ensure storage and access comply with GDPR or local privacy rules.
Expected results
- Structured resume data per candidate
- Ranked list with match reasons
- Review checklist for HR
- Audit trail for each score
Bias and privacy risks
- Bias: Do not score on protected attributes (age, gender, ethnicity). Use transparent, job-relevant dimensions only.
- Privacy: Limit access to resumes, retention, and sharing. Get consent where required.
- Explainability: Every score must trace back to resume or JD. No opaque "AI decided" conclusions.
- Overreach: Do not auto-contact candidates, auto-reject, or make hiring decisions without human approval.
Common pitfalls
- Treating AI rank as final decision
- Scoring on proxies for protected attributes
- Storing resumes in unapproved or unencrypted systems
- Missing audit logs for compliance review
Related pages
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Official docs → docs.openclaw.ai
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Issues → GitHub Issues