| name | eval-skills |
| description | Run structured evaluations on skills to measure quality and track improvements. |
| argument-hint | [skill-name ...] (e.g. local-code-review review-architecture) |
Eval Skills
Steps
1. Determine skills to evaluate
If names provided via $ARGUMENTS, evaluate those. Otherwise list skills
with evals/evals.json files and ask user to pick (accept "all").
2. Create iteration directory
mkdir -p .claude/evals-workspace/iteration-<N>
Use next sequential number.
3. Run eval cases
For each test case in evals.json, run twice:
- With skill: subagent with skill loaded, save to
iteration-<N>/<skill>-<id>/with_skill/outputs/
- Without skill: subagent without skill, save to
iteration-<N>/<skill>-<id>/without_skill/outputs/
Each run starts with clean context.
4. Grade
Evaluate assertions against output. Save grading.json:
{
"assertion_results": [{"text": "...", "passed": true, "evidence": "..."}],
"summary": {"passed": 3, "failed": 1, "total": 4, "pass_rate": 0.75}
}
Require concrete evidence for every PASS.
5. Aggregate
Save iteration-<N>/benchmark.json with mean pass rates (with/without skill) and delta.
6. Present results
Show per-eval pass rates, overall delta, always-pass candidates (remove?),
always-fail candidates (revise?). Save feedback to feedback.json.
Iteration loop
Update SKILL.md based on findings, run new iteration, compare benchmarks,
stop when pass rates plateau.