run
単一の実験イテレーションを実行。履歴をレビューし、変更を決定し、コードを編集し、コミットし、結果を評価。科学的手法を用いた体系的な実験プロセスをサポート。
npx skills add alirezarezvani/claude-skills --skill runBefore / After 効果比較
1 组実験履歴の手動記録、結果分析、次回の実験設計、コード修正と検証実行。実験プロセスが混乱し、変数の影響を追跡しにくく、結果が再現不可能でした。
実験履歴とコンテキストを自動管理し、実験イテレーションの全プロセスを体系的に実行します。すべての変更が記録および評価され、実験結果の追跡可能性と再現性を確保します。
run
/ar:run — Single Experiment Iteration
Run exactly ONE experiment iteration: review history, decide a change, edit, commit, evaluate.
Usage
/ar:run engineering/api-speed # Run one iteration
/ar:run # List experiments, let user pick
What It Does
Step 1: Resolve experiment
If no experiment specified, run python {skill_path}/scripts/setup_experiment.py --list and ask the user to pick.
Step 2: Load context
# Read experiment config
cat .autoresearch/{domain}/{name}/config.cfg
# Read strategy and constraints
cat .autoresearch/{domain}/{name}/program.md
# Read experiment history
cat .autoresearch/{domain}/{name}/results.tsv
# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}
Step 3: Decide what to try
Review results.tsv:
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What changes were kept? What pattern do they share?
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What was discarded? Avoid repeating those approaches.
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What crashed? Understand why.
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How many runs so far? (Escalate strategy accordingly)
Strategy escalation:
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Runs 1-5: Low-hanging fruit (obvious improvements)
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Runs 6-15: Systematic exploration (vary one parameter)
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Runs 16-30: Structural changes (algorithm swaps)
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Runs 30+: Radical experiments (completely different approaches)
Step 4: Make ONE change
Edit only the target file specified in config.cfg. Change one thing. Keep it simple.
Step 5: Commit and evaluate
git add {target}
git commit -m "experiment: {short description of what changed}"
python {skill_path}/scripts/run_experiment.py \
--experiment {domain}/{name} --single
Step 6: Report result
Read the script output. Tell the user:
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KEEP: "Improvement! {metric}: {value} ({delta} from previous best)"
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DISCARD: "No improvement. {metric}: {value} vs best {best}. Reverted."
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CRASH: "Evaluation failed: {reason}. Reverted."
Step 7: Self-improvement check
After every 10th experiment (check results.tsv line count), update the Strategy section of program.md with patterns learned.
Rules
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ONE change per iteration. Don't change 5 things at once.
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NEVER modify the evaluator (evaluate.py). It's ground truth.
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Simplicity wins. Equal performance with simpler code is an improvement.
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No new dependencies.
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