autoresearch-pro

What This Skill Does

autoresearch-pro is a sophisticated optimization engine designed for OpenClaw users who demand peak performance from their agents. Inspired by Andrej Karpathy's research on iterative refinement, this skill transforms the process of improving documentation, prompts, and code. Instead of guessing how to make your content better, autoresearch-pro utilizes a structured, mutation-testing feedback loop.

It operates by evaluating your content against a custom, 10-point checklist relevant to your specific input type. It then systematically suggests edits, performs internal scoring, and keeps only the improvements that demonstrably enhance the quality of your output, effectively pruning regressions before they ever reach your final version.

Installation

To add this skill to your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/0xcjl/autoresearch-pro

Ensure your OpenClaw runtime is updated to the latest version before installation to avoid dependency conflicts.

Use Cases

This skill is built for power users, developers, and writers. Common use cases include:

• Skill Development: Refine your SKILL.md files to ensure they are compliant with OpenClaw best practices and provide maximum clarity for the AI executor.
• Prompt Engineering: Pass raw, imprecise prompts to the tool to receive highly structured, constraint-aware versions that minimize hallucination.
• Content Polish: Perfect articles, technical docs, or reports by running them through the article-mode filter, which checks for readability, logical flow, and structural integrity.

Example Prompts

1. "autoresearch my web-scraper skill; it keeps failing on dynamic elements and the documentation is unclear."
2. "optimize this prompt: Write a summary of the provided text."
3. "improve this article about neural networks; it feels too repetitive and lacks depth in the concluding section."

Tips & Limitations

For the best results, always define the mode clearly. If you are refining a skill, ensure the directory path exists. Remember that while this tool automates the iterative process of editing, it works best when the user provides high-quality test cases or clear objective goals for the refinement. Note that this skill may consume significant tokens during the mutation-testing phase; large articles should be handled in chunks for better optimization results.