---
title: "/ar:run — Single Experiment Iteration — Agent Skill for Codex & OpenClaw"
description: "Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
---

# /ar:run — Single Experiment Iteration

<div class="page-meta" markdown>
<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
<span class="meta-badge">:material-identifier: `run`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/autoresearch-agent/skills/run/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install engineering-advanced-skills</code>
</div>


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.
