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setup

alirezarezvani/claude-skills

How to install setup

npx skills add https://github.com/alirezarezvani/claude-skills --skill setup
Claude Code
Cursor
Windsurf
Cline
Full instructions (SKILL.md)

Source of truth, from alirezarezvani/claude-skills.


name: "setup" description: "Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop." command: /ar:setup

/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:

  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

NameMetricUse Case
benchmark_speedp50_ms (lower)Function/API execution time
benchmark_sizesize_bytes (lower)File, bundle, Docker image size
test_pass_ratepass_rate (higher)Test suite pass percentage
build_speedbuild_seconds (lower)Build/compile/Docker build time
memory_usagepeak_mb (lower)Peak memory during execution
llm_judge_contentctr_score (higher)Headlines, titles, descriptions
llm_judge_promptquality_score (higher)System prompts, agent instructions
llm_judge_copyengagement_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."

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