setup – skill Agent & Tự động hóa cho agent AI

/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:

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

# 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:ar-run {domain}/{name} to start iterating, or /ar:loop {domain}/{name} for autonomous mode."