run
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
Best use case
run is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
Teams using run should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/run/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How run Compares
| Feature / Agent | run | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# /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.Related Skills
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