HRClaw turns messy JD text and PDF resumes into recruiter-ready decisions. It keeps screening consistent, fast, and easy to share in team chat.

把 JD 和 PDF 简历变成结构化、可执行的招聘结论。

Use this skill for two related flows:

• JD -> scorecard
• Resume PDF/text -> score against a scorecard

Best for

• high-volume recruiting
• QA / Python / operations roles
• teams that want one repeatable scoring standard
• Feishu / DingTalk collaboration

If the user gives both a JD and a resume, generate the scorecard first and then score the resume.

JD flow

Default to a single JSON object with:

• role_title
• summary
• filters
• must_have
• nice_to_have
• exclude
• weights
• thresholds
• interview_questions
• red_flags
• assumptions
• next_steps

If the user asks for a readable version, format the same content with templates/scorecard.md. If the user asks for a Feishu/DingTalk-friendly chat view, format the same content with templates/chat-scorecard.md.

Resume score flow

Use this flow when the user uploads a resume PDF or pastes resume text together with a scorecard.

If the user only provides a resume, ask for a scorecard or JD before scoring.

• Extract the resume text from the PDF first.
• If the PDF is image-only and no readable text is available, set extraction_status to needs_ocr and stop.
• Normalize the resume into a candidate profile.
• Score it against the provided scorecard using the same filters, weights, and thresholds.
• Return one pure JSON object first.

Resume output should include:

• mode
• source_type
• extraction_status
• scorecard_name
• candidate_profile
• hard_filter_pass
• hard_filter_fail_reasons
• dimension_scores
• total_score
• decision
• review_reasons
• matched_terms
• missing_terms
• blocked_terms
• evidence
• summary
• next_steps

If the user asks for a Feishu/DingTalk-friendly chat view, format the same content with templates/chat-resume-score.md.

Candidate profile fields:

• name
• location
• years_experience
• education_level
• current_title
• current_company
• skills
• industry_tags

If the user provides a JD and a resume together, generate the scorecard first, then score the resume against it.

Rules

• Use only explicit evidence from the JD.
• For resume scoring, use only explicit evidence from the resume and scorecard.
• Do not invent requirements or hidden intent.
• Keep one primary role per scorecard.
• If the JD is mixed or vague, add short assumptions instead of guessing.
• Prefer practical screening signals over generic hiring advice.
• Generate 5 to 10 interview questions that test real work.
• If a resume PDF is unreadable and OCR text is not available, say so clearly instead of guessing.

Flow

• Extract the role, location, years of experience, education, tools, and exclusions.
• Convert those signals into a scorecard.
• Add interview questions that verify the must-haves.
• Add red flags that help a recruiter reject quickly.
• For resumes, extract the profile, apply the scorecard, and return the scoring JSON first.

References

• references/quickstart.md
• references/faq.md
• references/limitations.md
• prompts/jd-to-scorecard.md
• prompts/resume-score.md
• prompts/interview-questions.md
• templates/scorecard.json
• templates/scorecard.md
• templates/chat-scorecard.md
• templates/resume-score.json
• templates/resume-score.md
• templates/chat-resume-score.md