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:
| Criterion | Weight | Max Points |
|---|---|---|
| JD Matching | ×3 | 30 |
| Work Experience | ×2.5 | 25 |
| Project & Impact | ×1.5 | 15 |
| Education | ×1.5 | 15 |
| CV Quality | ×1.5 | 15 |
| Total | 100 |
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:
- Score each CV independently — no cross-comparison during scoring (safe to parallelize)
- After all CVs are scored, produce a summary ranking (highest to lowest)
- 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