Evaluate and refine public skill assets using triple criteria.
npx skills add https://github.com/flashfoxer/openclaw-my-skills --skill luban
Please help me install this Agent Skill. Skill: luban Source: https://github.com/flashfoxer/openclaw-my-skills/tree/main/luban Command: npx skills add https://github.com/flashfoxer/openclaw-my-skills --skill luban
The luban skill serves as a workshop to polish existing skills. Its core aim is to refine skills from functional assets to high-quality public ones. The方法论 revolves around a five-step craft: material checking (validate feasibility and purpose), survey (benchmarking, finding gaps, understanding place), measurement (measuring by criteria: structure, practical results, and reality checks), meticulous cutting (stability and optimization through continuous measurement and refinement), and re-smelting (continuously improve through observation lists based on user feedback).
Features:
Use case: This skill is intended for users looking to enhance their own skills, aiming for them to be understandable, installable, widely spread, verifiable, and capable of continuous evolution.
Quick Start: To initiate the process, use the trigger phrases like "Let Lüban take a look at this skill", "Polish my skill", "Optimize my skill", "Audit my skill", etc. The process begins by submitting a skill directory, GitHub repository link, or a SKILL.md document and specifying the desired improvements.
High-intent search queries and answers about installing and using this skill.
To evaluate an existing public skill asset, you can use a skill improvement workshop that applies a five-step craft: material checking, ecosystem survey, comprehensive measurement, meticulous cutting, and re-smelting based on user feedback. This process refines functional assets into high-quality public skills.
Benchmarking a skill involves finding and comparing similar assets on platforms like GitHub and ClawHub to determine its positioning in the ecosystem. This survey step helps identify gaps and validates the skill's purpose against existing alternatives.
Auditing a skill's structure and practical results requires applying a triple measurement criteria: evaluating the internal structure, measuring practical results, and performing living reality checks. This comprehensive review ensures stability and highlights optimization areas.
Yes, you can continuously iterate a skill through a re-smelting process that uses structured observation lists based on user feedback. This methodology supports ongoing refinement, ensuring the asset remains understandable, installable, and capable of evolution.
To start a skill fine-tuning and assessment process, you need to submit a skill directory, a GitHub repository link, or a SKILL.md document. Specifying your desired improvements triggers the validation and benchmarking workflow.
A comprehensive skill review outputs a structured skill improvement report, directly applicable revised sections, and a Certificate of Achievement. These deliverables provide actionable steps for polishing the asset into a high-quality public skill.
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