mcpField
R
run

run

github.com/alirezarezvaniAgent workflows28.9k1.5k installsUpdated 2d ago
Repository

About run

A skill that performs exactly one iteration of an autoresearch experiment: it reviews prior results, changes the configured target, commits the change, and evaluates it. It reports whether the change is kept, discarded, or failed.

Key features

  1. 01Reviews experiment configuration and history
  2. 02Makes one change to the target file
  3. 03Commits and evaluates the iteration
  4. 04Reports keep, discard, or crash outcomes
  5. 05Updates strategy notes every ten runs

Use cases

  1. 01Test one improvement to a target file
  2. 02Use prior outcomes to guide the next iteration

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill run

After installing, type/runin Claude Code to confirm it loaded.

Details

From the project README

/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:

  • 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.

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