- name
- autoresearch-pro
- description
- Automatically improve OpenClaw skills, prompts, or articles through iterative mutation-testing loops. Inspired by Karpathy's autoresearch. Use when user says 'optimize [skill]', 'autoresearch [skill]', 'improve my skill', 'optimize this prompt', 'improve my prompt', 'polish this article', 'improve this article', or explicitly requests quality improvement for any text-based content. Supports three modes: skill (SKILL.md files), prompt (any prompt text), and article (any document).
autoresearch-pro
Overview
Automatically improve any OpenClaw skill, prompt, or article through iterative mutation-testing: small edits → run test cases → score with checklist → keep improvements, discard regressions.
Inspired by Karpathy/autoresearch.
Supports three optimization modes:
| Mode | Input | Output |
|---|---|---|
| Skill | Path to a skill directory | Improved SKILL.md |
| Prompt | A prompt text string | Improved prompt |
| Article | An article/document text | Improved article |
Workflow
Step 1 — Identify Mode and Input
Ask the user to confirm:
- Mode 1 — Skill: User says "optimize [skill-name]" or provides a skill path
- Mode 2 — Prompt: User says "optimize this prompt" or pastes a prompt
- Mode 3 — Article: User says "improve this article" or pastes article text
For Skill mode, resolve the skill path to ~/.openclaw/skills/<skill-name>/SKILL.md. For Prompt/Article mode, keep the text in context (do not write to disk unless needed).
Step 2 — Generate Checklist (10 Questions)
Read the target content first. Then generate 10 diverse, specific yes/no checklist questions relevant to the content type:
For Skill mode (same as before):
| # | Dimension | What to Check |
|---|---|---|
| 1 | Description clarity | Is the frontmatter description precise and actionable? |
| 2 | Trigger coverage | Does it cover the main real-world use cases? |
| 3 | Workflow structure | Are steps clearly sequenced and unambiguous? |
| 4 | Error guidance | Does it handle error states and edge cases? |
| 5 | Tool usage accuracy | Are tool names and parameters correct for OpenClaw? |
| 6 | Example quality | Do examples reflect real usage patterns? |
| 7 | Conciseness | Is content free of redundant repetition? |
| 8 | Freedom calibration | Is instruction specificity appropriate? |
| 9 | Reference quality | Are references and links accurate? |
| 10 | Completeness | Are all sections filled with real content? |
For Prompt mode (10 tailored questions):
| # | Dimension | What to Check |
|---|---|---|
| 1 | Goal clarity | Does the prompt state a clear, specific goal? |
| 2 | Role/tone | Is the desired role or tone specified? |
| 3 | Input format | Is the input format clearly described? |
| 4 | Output format | Is the expected output format specified? |
| 5 | Constraints | Are key constraints and boundaries stated? |
| 6 | Context sufficiency | Is enough context provided to avoid hallucination? |
| 7 | Edge cases | Does it handle ambiguous or edge case inputs? |
| 8 | Conciseness | Is it free of redundant or contradictory instructions? |
| 9 | Actionability | Are instructions concrete and actionable vs. vague? |
| 10 | Completeness | Are all necessary elements for the task present? |
For Article mode (10 tailored questions):
| # | Dimension | What to Check |
|---|---|---|
| 1 | Title quality | Does the title clearly convey the main value? |
| 2 | Opening hook | Does the opening grab attention and set expectations? |
| 3 | Logical structure | Are ideas logically organized (not random)? |
| 4 | Argument clarity | Are claims supported with evidence or reasoning? |
| 5 | Conciseness | Is unnecessary padding or repetition removed? |
| 6 | Transition flow | Do paragraphs/sections flow smoothly? |
| 7 | Closing strength | Does the conclusion summarize and inspire action? |
| 8 | Tone consistency | Is the tone consistent throughout? |
| 9 | Readability | Is sentence/paragraph length varied appropriately? |
| 10 | Audience match | Does language match the target audience level? |
Present the 10 questions, numbered 1-10. Ask the user to select which ones to activate (e.g., "use questions 1, 3, 5, 7"). Default: use all 10 if user doesn't specify.
Step 3 — Prepare Test Cases
- Skill mode: Generate 3-5 realistic prompts a user would send when using the skill
- Prompt mode: Generate 3-5 test inputs that the prompt would process
- Article mode: Generate 3-5 ways the article might be read or consumed
Store test cases in context — do not write to disk.
Step 4 — Run Autoresearch Loop
Loop configuration:
- Rounds per batch: 30
- Max total rounds: 100
- Pause: After every 30 rounds, show summary and ask user to continue or stop
- Stop conditions: User says stop, OR 100 rounds completed
Per-round procedure:
- Mutate: Make ONE small edit to the target content:
- Skill mode: edit SKILL.md - Prompt mode: edit the prompt string - Article mode: edit the article text
- Test: For each test case, simulate what output the content would produce.
- Score: Apply each active checklist question (0 or 1 per question). Score = (passed / total) × 100.
- Decide: If new score ≥ best score → keep the mutation. If lower → revert.
- Log: Round number, mutation type, score, keep/revert decision.
Mutation types (pick one per round):
| Type | Description |
|---|---|
| A | Add a constraint rule |
| B | Strengthen trigger/coverage |
| C | Add a concrete example |
| D | Tighten vague language |
| E | Improve error/edge case handling |
| F | Remove redundant content |
| G | Improve transitions |
| H | Expand a thin section |
| I | Add cross-reference |
| J | Adjust degree-of-freedom |
Step 5 — Report Results
After each batch (30 rounds):
Batch N (rounds X-Y):
Best score: XX%
Mutations kept: N | Reverted: N
Most effective types: [list top 2-3]
Accumulated improvements: [summary]
Continue? (yes/stop)After full completion:
- Original score vs. final score
- Top 3 most impactful mutations
- Final improved content (inline or diff)
- File path (skill mode only)
Mutation Strategy Reference
High-impact, low-risk changes:
- Adding explicit constraints where the content is vague
- Expanding coverage to cover edge cases
- Adding concrete examples to abstract instructions
- Tightening soft language ("try to" → "must")
Avoid in one round:
- Large rewrites of entire sections
- Multiple unrelated changes at once
- Changing fundamental scope or purpose
See references/mutation_strategies.md for the full strategy guide.
Mode Selection Quick Reference
| User says | Mode |
|---|---|
| "optimize [skill]" / "autoresearch [skill]" | Skill |
| "optimize this prompt" / "improve my prompt" | Prompt |
| "polish this article" / "improve this article" | Article |
| "optimize this document" | Article |
Default to Prompt mode if the input is a text string without a skill path.