Agent skill · alirezarezvani
ar-run
Use for /ar-run, or to run one manual autoresearch iteration: edit, evaluate, keep or discard.
What it needs
About 1k tokens when loaded.
What this skill does
/ar-run — Single Experiment Iteration Run exactly ONE experiment iteration: review history, decide a change, edit, commit, evaluate. Usage What It Does Step 1: Resolve experiment If no experiment specified, run python {skillpath}/scripts/setupexperiment.py --list and ask the user to pick. Step 2: Load context 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 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.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/ar-run