setup
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.
Best use case
setup is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.
Teams using setup 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/setup/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How setup Compares
| Feature / Agent | setup | 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?
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.
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.
SKILL.md Source
# /ar:setup — Create New Experiment
Set up a new autoresearch experiment with all required configuration.
## Usage
```
/ar:setup # Interactive mode
/ar:setup engineering api-speed src/api.py "pytest bench.py" p50_ms lower
/ar:setup --list # Show existing experiments
/ar:setup --list-evaluators # Show available evaluators
```
## What It Does
### If arguments provided
Pass them directly to the setup script:
```bash
python {skill_path}/scripts/setup_experiment.py \
--domain {domain} --name {name} \
--target {target} --eval "{eval_cmd}" \
--metric {metric} --direction {direction} \
[--evaluator {evaluator}] [--scope {scope}]
```
### If no arguments (interactive mode)
Collect each parameter one at a time:
1. **Domain** — Ask: "What domain? (engineering, marketing, content, prompts, custom)"
2. **Name** — Ask: "Experiment name? (e.g., api-speed, blog-titles)"
3. **Target file** — Ask: "Which file to optimize?" Verify it exists.
4. **Eval command** — Ask: "How to measure it? (e.g., pytest bench.py, python evaluate.py)"
5. **Metric** — Ask: "What metric does the eval output? (e.g., p50_ms, ctr_score)"
6. **Direction** — Ask: "Is lower or higher better?"
7. **Evaluator** (optional) — Show built-in evaluators. Ask: "Use a built-in evaluator, or your own?"
8. **Scope** — Ask: "Store in project (.autoresearch/) or user (~/.autoresearch/)?"
Then run `setup_experiment.py` with the collected parameters.
### Listing
```bash
# Show existing experiments
python {skill_path}/scripts/setup_experiment.py --list
# Show available evaluators
python {skill_path}/scripts/setup_experiment.py --list-evaluators
```
## Built-in Evaluators
| Name | Metric | Use Case |
|------|--------|----------|
| `benchmark_speed` | `p50_ms` (lower) | Function/API execution time |
| `benchmark_size` | `size_bytes` (lower) | File, bundle, Docker image size |
| `test_pass_rate` | `pass_rate` (higher) | Test suite pass percentage |
| `build_speed` | `build_seconds` (lower) | Build/compile/Docker build time |
| `memory_usage` | `peak_mb` (lower) | Peak memory during execution |
| `llm_judge_content` | `ctr_score` (higher) | Headlines, titles, descriptions |
| `llm_judge_prompt` | `quality_score` (higher) | System prompts, agent instructions |
| `llm_judge_copy` | `engagement_score` (higher) | Social posts, ad copy, emails |
## After Setup
Report to the user:
- Experiment path and branch name
- Whether the eval command worked and the baseline metric
- Suggest: "Run `/ar:run {domain}/{name}` to start iterating, or `/ar:loop {domain}/{name}` for autonomous mode."Related Skills
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