levalencia /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:
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:
- Domain — Ask: "What domain? (engineering, marketing, content, prompts, custom)"
- Name — Ask: "Experiment name? (e.g., api-speed, blog-titles)"
- Target file — Ask: "Which file to optimize?" Verify it exists.
- Eval command — Ask: "How to measure it? (e.g., pytest bench.py, python evaluate.py)"
- Metric — Ask: "What metric does the eval output? (e.g., p50_ms, ctr_score)"
- Direction — Ask: "Is lower or higher better?"
- Evaluator (optional) — Show built-in evaluators. Ask: "Use a built-in evaluator, or your own?"
- 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."