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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.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use when the user wants to run the optimization loop, improve agent performance, evolve the agent, or iterate on quality. Requires .evolver.json to exist (run harness:setup first).
Use when the user is done evolving and wants to finalize, clean up, tag the result, or push the optimized agent.
Use when the user wants to set up the evolver in their project, optimize an LLM agent, improve agent performance, or mentions evolver for the first time in a project without .evolver.json.
Use when the user wants to check dataset quality, diagnose eval issues, or before running evolve. Checks size, difficulty distribution, dead examples, coverage, and splits. Auto-corrects issues found.
Use when the user asks about evolution progress, current scores, best version, how many iterations ran, or whether the loop is stagnating.
Use when the user wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'.
| 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"] |
Verify score stability by running evaluation multiple times and reporting statistical confidence.
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")}"
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.
CERTIFICATION REPORT
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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.
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.