skiLL.Md is a structured, open-source collection of reusable, self-contained Markdown modules designed to teach AI coding assistants and human developers how to perform specific software engineering tasks.
When working with modern AI coding assistants (like Cursor, GitHub Copilot, or Claude) or autonomous agents, the quality of the output is heavily dependent on the context provided.
Massive, monolithic "system prompts" or .cursorrules files quickly become unmanageable, leading to token bloat and diluted AI focus.
The skiLL.Md solves this by introducing modularity.
Instead of telling the AI everything about your codebase at once, you dynamically inject hyper-specific, technology-agnostic checklists (SKILL.md) exactly when the AI needs them.
- No generic advice ("Write clean code").
- No training datasets (Only rules, constraints, and actionable workflows).
- No model or IDE specific logic (Works across ChatGPT, Claude, Gemini, Github Copilot, Cursor, VSCode, Claude Code, Codex etc.).
A "Skill" is an executable, technology-agnostic mental checklist for a specific workflow, design pattern, or architecture. Every skill in this repository strictly adheres to schema/SKILL_SCHEMA.md and contains:
- Clear Use Cases: Exact conditions for when the skill should be activated.
- Actionable Workflows: Step-by-step instructions.
- Hard Constraints: Non-negotiable architectural rules.
- Anti-patterns: Specific mistakes the AI (or developer) must avoid.
- Output Formats: Expected code footprint.
Skills are meant to be used as injected context. Here is how you can use them across different workflows:
When asking your IDE's chat or inline generator to perform a task, @-mention the relevant skill file to strictly bind the AI's output to the standard.
Prompt: "I need to build a new Settings page for the user dashboard. Please review
@skills/frontend/react-component-design/SKILL.mdand@skills/ui-ux/accessibility-ui-design/SKILL.mdbefore generating the code."
If you are building a custom AI agent, use the registry.json file.
Provide the registry to your agent as a tool. When the user asks for a task, the agent can query the registry, find the path to the required SKILL.md, and read the file into its context window before writing code.
Because every skill is written in clear, checklist-style Markdown, they double perfectly as onboarding documents, PR review checklists, and team engineering standards.
The library is organized by domain, ensuring skills are easy to find and categorize.
The repository includes an auto-updated registry.json. This acts as an API for the filesystem. Tooling, CI/CD pipelines, and AI agents can parse this file to dynamically load skills by category or tags without needing to crawl directories.
Here is an example of what happens when you combine an AI prompt with a Skill from this library.
Without the Skill:
"Write a generic React button component." Result: The AI might write a class component, it might use inline styles, it might forget ARIA labels, or it might overcomplicate the props.
With the Skill:
"Write a React button component. Adhere strictly to the rules and anti-patterns defined in the attached
react-component-design/SKILL.md." Result: The AI outputs a pure functional component, strictly typed via TypeScript interfaces, isolated from business logic, utilizing CSS classes instead of inline styles, and avoiding prop-drilling.
We welcome contributions from the community! However, to maintain the high quality and uniformity of this library, all contributions must pass strict quality checks.
Please read the CONTRIBUTING.md before submitting a Pull Request.
If you aren't sure if your skill belongs here, open a New Skill Proposal Issue using our issue templates!