setup – Agents & Automation skill for AI agents

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.

Agents & AutomationSKILL.mdClaude Code · Codex · Hermes · DeepSeek

Install the setup skill with one command

Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.

Claude Code

mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/setup-ae576.tgz | tar xz -C ~/.claude/skills

Call it in Claude Code: /setup – then describe the task you want done.

Codex

mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/setup-ae576.tgz | tar xz -C ~/.codex/skills

Call it in Codex: $setup – then describe the task you want done.

Hermes Agent

mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/setup-ae576.tgz | tar xz -C ~/.hermes/skills

Call it in Hermes Agent: /setup – then describe the task you want done.

DeepSeek

mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/setup-ae576.tgz | tar xz -C ~/.deepcode/skills

Call it in DeepSeek: /setup – then describe the task you want done.

SKILL.md content

/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."

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