skill-from-github_skill

Overview

This skill creates reliable AI skills by learning from high-quality GitHub projects and encoding their proven methodologies. It finds relevant repositories, evaluates their quality, extracts core techniques, and converts that knowledge into a standalone skill specification. The result is a skill that follows expert practices without requiring the original project to be installed.

How this skill works

First it clarifies user intent, inputs, outputs, and constraints. Then it searches GitHub with focused queries and quality filters (stars, recency, documentation, real code). After presenting top candidates and getting confirmation, it deep-dives into the chosen project to extract algorithms, data formats, error handling, and best practices. Finally it summarizes findings and produces a skill design that embodies the learned approach and notes license and attribution.

When to use it

• You want a skill that implements a well-established solution pattern from open-source projects.
• You need a robust starting point for tasks like parsing, conversion, or automation where GitHub hosts mature tools.
• You want a skill informed by battle-tested code and real examples rather than ad-hoc heuristics.
• You need to learn proven input/output formats, error patterns, or dependency constraints before implementing a skill.

Best practices

• Start by clarifying exact input, desired output, and constraints before searching.
• Use broad-to-narrow GitHub queries and include keywords like cli, library, or tool when relevant.
• Apply quality filters: >100 stars, updated within 12 months, clear README, and real code.
• Always present 3–5 top candidates and get user confirmation before deep-diving.
• Encode knowledge (algorithms, formats, pitfalls) into the skill, do not merely wrap the tool.
• Check and record the repository license and include attribution in the skill output.

Example use cases

• Create a skill that converts Markdown to PDF by learning from popular converters and templates.
• Design a sentiment-analysis skill by studying top NLP libraries and example preprocessing pipelines.
• Build a code-documentation skill by extracting techniques from projects that generate API docs.
• Produce a commit-message assistant by encoding the Conventional Commits specification and examples.
• Develop a CLI-style file transformer skill informed by a well-maintained command-line tool.