setup
alirezarezvani/claude-skills
Interactively configure a new autoresearch optimization experiment with domain, target file, eval command, and metric.
What is setup?
Sets up a new autoresearch experiment by collecting configuration parameters (domain, target file, eval command, metric, direction, evaluator). Use this when starting a new optimization loop to measure and improve code or content.
- Collects experiment parameters interactively or via command arguments
- Validates target file existence and eval command functionality
- Displays baseline metric from initial evaluation
- Lists existing experiments and available built-in evaluators
- Stores experiment config in project or user scope
- Supports 8 built-in evaluators (speed, size, test pass rate, memory, LLM judges)
How to install setup
npx skills add https://github.com/alirezarezvani/claude-skills --skill setup- autoresearch framework installed
- Python with pytest or custom eval script available
- Target file must exist in the project
How to use setup
- 1.Run `/ar:setup` to enter interactive mode
- 2.Answer prompts for domain, experiment name, target file, eval command, metric, and direction
- 3.Optionally select a built-in evaluator or use your own
- 4.Choose storage scope (project or user)
- 5.Verify baseline metric is reported
- 6.Run `/ar:run` or `/ar:loop` to start optimization
Use cases
- Set up a performance optimization experiment for an API endpoint
- Create a test pass-rate improvement experiment for a test suite
- Configure a bundle size reduction experiment for a web app
- Initialize a content quality evaluation using LLM judges for headlines
- Start a memory usage optimization experiment for a function
- Engineers optimizing code performance or quality metrics
- Content creators improving headlines or copy with LLM evaluation
- DevOps/build engineers reducing build or deployment time
- QA teams improving test pass rates
- Anyone running autoresearch optimization loops
setup FAQ
Yes. Use `/ar:setup domain name target "eval_cmd" metric direction [--evaluator name]` to skip prompts.
Specify which metric to track with the `--metric` parameter. The evaluator will extract that specific value.
Yes. Select 'custom' when prompted for evaluator, and provide your own evaluation script.
In `.autoresearch/` (project scope) or `~/.autoresearch/` (user scope), depending on your choice during setup.
Run `/ar:setup --list` to show existing experiments or `/ar:setup --list-evaluators` to see available evaluators.
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:
- 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."
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