ar-run – skill Agent & Tự động hóa cho agent AI

/ar:ar-run — Single Experiment Iteration

Run exactly ONE experiment iteration: review history, decide a change, edit, commit, evaluate.

Usage

/ar:ar-run engineering/api-speed              # Run one iteration
/ar: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:

• 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

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.