run
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 autoresearch iteration.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill run- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads run automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
/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.
FAQ
What does the run skill do?
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 autoresearch iteration.
How do I install run?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill run in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/run/ (personal) or .claude/skills/run/ (project).
Which AI agents can use run?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is run safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains run?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 1,464 recorded installs.
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