Cv Scorer

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:

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