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: ```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." --- ## Why This Skill Exists Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. <!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. --> ## When to Use Use this skill when the task requires setup capabilities. <!-- SR_40: auto-generated from frontmatter `when`/`description` (OPP-Phase3). --> ## What If Fails If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill. <!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->