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 ```bash # 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: - What changes were kept? What pattern do they share? - What was discarded? Avoid repeating those approaches. - What crashed? Understand why. - How many runs so far? (Escalate strategy accordingly) **Strategy escalation:** - Runs 1-5: Low-hanging fruit (obvious improvements) - Runs 6-15: Systematic exploration (vary one parameter) - Runs 16-30: Structural changes (algorithm swaps) - 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 ```bash 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: - **KEEP**: "Improvement! {metric}: {value} ({delta} from previous best)" - **DISCARD**: "No improvement. {metric}: {value} vs best {best}. Reverted." - **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 - ONE change per iteration. Don't change 5 things at once. - NEVER modify the evaluator (evaluate.py). It's ground truth. - Simplicity wins. Equal performance with simpler code is an improvement. - No new dependencies. ## When to Use Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Covers: /ar:run — Single Experiment Iteration, What It Does, Resolve experiment, Load context, Read experiment config.