setup

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:

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

# 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."