How do I install the Setup skill?

Run npx skillmds@latest add alirezarezvani/setup-4 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Setup safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Setup?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Setup free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Alireza Rezvani (@alirezarezvani) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Setup by alirezarezvani — Agent Skill

Name: Setup
Rating: 4.666666666666667 (3 reviews)
Author: Alireza Rezvani

Setup

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.

Alireza Rezvani Updated Sep 9, 2026 20.4k repo stars

File contents

/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."

Source: alirezarezvani/claude-skills → engineering/autoresearch-agent/skills/setup/SKILL.md

---name: setup-4description: 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.--- # /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." --- **Source:** [`alirezarezvani/claude-skills`](https://github.com/alirezarezvani/claude-skills) → `engineering/autoresearch-agent/skills/setup/SKILL.md`

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