How do I install the Setup skill?

Run npx skillmds add thedixitjain/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?

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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.

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

Setup by thedixitjain — Agent Skill

Name: Setup
Rating: 4.666666666666667 (3 reviews)
Author: thedixitjain

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.

thedixitjain 5da5132 3.1 KB Updated Sep 9, 2026 2 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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