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cv-scorer

Score candidate CVs on a 100-point scale against a Job Description. Use this skill when the user wants to evaluate, score, rank, or screen candidate CVs/resumes against a JD. Also trigger when the user mentions 'review CV', 'screen resume', 'rate candidates', 'shortlist', or any context involving matching resumes to job requirements.

The English summary is being prepared. Showing the original description.

See how to install

Files included (6)

  • SKILL.md2.7 KB
  • README.md3.9 KB
  • README.vi.md5.0 KB
  • README.zh.md3.6 KB
  • references/output-format.md2.0 KB
  • references/scoring-rubric.md2.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

CV Scorer — Candidate CV Evaluation

This skill evaluates how well a candidate's CV matches a specific Job Description (JD), producing a structured score out of 100.

Workflow

Step 1: Identify Inputs

Two inputs are required:

  • Job Description (JD): The job posting with requirements, qualifications, and responsibilities
  • CV(s): One or more candidate CVs (markdown, text, or PDF)

If the user hasn't provided a JD, ask for it. If the JD is already available in context (e.g., a file on Drive or in the project directory), read it directly.

Reading PDF files: Use the /pdf skill to extract text from PDF CVs — it handles multi-page documents and formatted layouts reliably.

Step 2: Analyze the JD

Before scoring, extract from the JD:

  • Must-have skills vs nice-to-have skills
  • Experience requirements (years, seniority level, domain)
  • Education requirements
  • Special requirements (languages, certifications, travel, etc.)

Step 3: Score Against Rubric

Score each CV across 5 criteria using references/scoring-rubric.md:

CriterionWeightMax Points
JD Matching×330
Work Experience×2.525
Project & Impact×1.515
Education×1.515
CV Quality×1.515
Total100

Step 4: Output

Output JSON for each CV using the format in references/output-format.md.

Recommendation thresholds:

  • Recommend (≥ 70): Invite for interview
  • Maybe (50–69): Consider if candidate pool is thin
  • Pass (< 50): Not a fit

Step 5: Batch Processing

When scoring multiple CVs:

  1. Score each CV independently — no cross-comparison during scoring (safe to parallelize)
  2. After all CVs are scored, produce a summary ranking (highest to lowest)
  3. Use the batch summary format in references/output-format.md

Scoring Principles

  • Objective: Score based on facts in the CV, avoid over-inference
  • Fair: Apply the same standard consistently across all candidates
  • Red flag detection: Repetitive content, inflated metrics, unexplained career gaps, contradictory information
  • Output language: Match the user's language (respond in the same language the user is using)
  • No bias: Do not evaluate based on age, gender, ethnicity, or personal factors unrelated to the job

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