fit-score

Fit Score

Purpose

Apply references/fit-scoring-rubric.md deterministically: component scores, total fit score, effective available capacity across the full role duration, utilisation after staffing and provisional rank for every candidate-role pair.

When to use

After bench-query and before constraint-check in every staffing run.

When NOT to Use

• Drafting the partner-ready recommendation or "build me a slate" — use slate-draft instead.
• Deciding eligibility, independence or conflicts — use constraint-check instead.
• Scoring vendor proposals, RFP responses or anything other than candidate-role fit.

Inputs

roles.json + bench.json using contract ps.engagement-staffing-bench-match.v1.

Steps

1. Validate that every role and candidate carries source, confidence and citation; flag missing rates. A duplicate candidate ID is an input error under independence-eligibility-rules.md #9.7: fit_score.py refuses it with one error: line and writes nothing, so stop and report that one-line error; never hand-build the output.
2. Run the scoring engine.
3. Run the ranking engine.
4. Quote scores, components, utilisation and provisional rank verbatim. The model may explain the meaning but never recomputes a number.
5. Hand off ranked.json to constraint-check.

python3 scripts/fit_score.py --roles roles.json --bench bench.json --out scored.json
python3 scripts/slate_rank.py --scored scored.json --out ranked.json

Tools

The bash tool runs the two bundled python3 engines; the file-read tool reads their JSON output. No connector writes.

Output format

scored.json (adds scored_roles[]) and ranked.json (adds ranked_roles[] with each role's candidate_slate[]). Summarise to the user as one markdown table per role, quoting engine values verbatim:

### <role_id> <role_title> — Provisional — pre-governance
| Provisional rank | Candidate | Fit score | Effective available % | Utilisation after staffing % | Citation |
|---|---|---|---|---|---|