| name | harness:certify |
| description | Use when the user wants to verify that the evolved agent's score is stable and reliable. Runs evaluation multiple times and reports mean ± std. |
| allowed-tools | ["Read","Bash","Glob"] |
/harness:certify
Verify score stability by running evaluation multiple times and reporting statistical confidence.
Resolve Tool Path
TOOLS="${EVOLVER_TOOLS:-$([ -d ".evolver/tools" ] && echo ".evolver/tools" || echo "$HOME/.evolver/tools")}"
EVOLVER_PY="${EVOLVER_PY:-$([ -f "$HOME/.evolver/venv/bin/python" ] && echo "$HOME/.evolver/venv/bin/python" || echo "python3")}"
What To Do
Read .evolver.json to get the best experiment and dataset.
Run evaluation 3 times on the current code (not a worktree — the best code is already merged):
for i in 1 2 3; do
$EVOLVER_PY $TOOLS/run_eval.py \
--config .evolver.json \
--worktree-path "." \
--experiment-prefix "certify-run-$i"
done
After all 3 runs complete, read results and compute statistics:
$EVOLVER_PY $TOOLS/read_results.py --experiments "certify-run-1-{suffix},certify-run-2-{suffix},certify-run-3-{suffix}" --config .evolver.json --format summary
Calculate mean and standard deviation from the 3 combined_scores.
Report
CERTIFICATION REPORT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Runs: 3
Mean: {mean:.3f}
Std: {std:.3f}
Range: {min:.3f} — {max:.3f}
Verdict: {STABLE|UNSTABLE}
STABLE (std < 0.05): Score is reliable. The agent performs consistently.
MARGINAL (0.05 <= std < 0.10): Score varies moderately. Consider adding rubrics to reduce judge variance.
UNSTABLE (std >= 0.10): Score is unreliable. The LLM judge interprets criteria differently across runs. Add few-shot examples or tighter rubrics.
After Certification
If STABLE: suggest /harness:deploy to finalize.
If UNSTABLE: suggest adding rubrics to dataset examples, or running /harness:evolve with heavy mode for more thorough evaluation.
