Setup

Skill sinhoneyy/master-skills/skills/setup

Unified skill library for Claude, Codex, Cursor, Antigravity & AI agents — 2,658 skills across 15 domains

Install

npx -y skills add sinhoneyy/master-skills --skill setup

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What its author says it does

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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.

SKILL.md

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/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."