# Agent Skills: A Standardized Way to Give AI Agents New Capabilities

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Agent Skills provides a lightweight, open format for extending AI agent capabilities with specialized knowledge and workflows. It allows packaging procedural knowledge and context into portable, version-controlled folders that agents load on demand. This enables agents to gain domain expertise, follow repeatable workflows, and reuse skills across various compatible AI tools.

GitHub: https://github.com/agentskills/agentskills
OSRepos URL: https://osrepos.com/repo/agentskills-agentskills

## Summary

Agent Skills provides a lightweight, open format for extending AI agent capabilities with specialized knowledge and workflows. It allows packaging procedural knowledge and context into portable, version-controlled folders that agents load on demand. This enables agents to gain domain expertise, follow repeatable workflows, and reuse skills across various compatible AI tools.

## Topics

- agent-skills
- Python
- AI
- Agent Development
- Open Standard
- Artificial Intelligence

## Repository Information

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## Content

## Introduction
Agent Skills is an open standard that defines a standardized way to give AI agents new capabilities and expertise. It offers a lightweight, open format for extending AI agent capabilities with specialized knowledge and workflows. At its core, a skill is a folder containing a `SKILL.md` file, which includes metadata and instructions for an agent to perform a specific task, optionally bundling scripts, reference materials, and templates.

## How Agent Skills Work
Agent Skills are not 'installed' in the traditional sense, but rather integrated into AI agent clients. Agents load skills through progressive disclosure, in three stages:
*   **Discovery**: Agents load only the name and description of available skills.
*   **Activation**: When a task matches a skill's description, the agent reads the full `SKILL.md` instructions.
*   **Execution**: The agent follows instructions, potentially executing bundled code or loading referenced files.

This approach ensures agents can manage many skills with a small context footprint. To get started, refer to the official documentation and client showcase.

## Examples
A skill is structured as a folder, typically containing a `SKILL.md` file along with optional `scripts/`, `references/`, and `assets/` directories. For practical examples and inspiration, you can explore the [Example Skills repository](https://github.com/anthropics/skills){:target="_blank"} to see how various capabilities are defined.


my-skill/
??? SKILL.md          # Required: metadata + instructions
??? scripts/          # Optional: executable code
??? references/       # Optional: documentation
??? assets/           # Optional: templates, resources
??? ...               # Any additional files or directories


## Why Use Agent Skills?
Agent Skills address the challenge of providing AI agents with the necessary context to perform real work reliably. By packaging procedural knowledge and specific context into portable, version-controlled folders, agents gain:
*   **Domain expertise**: Capture specialized knowledge, from legal review to data analysis, as reusable instructions.
*   **Repeatable workflows**: Transform multi-step tasks into consistent, auditable procedures.
*   **Cross-product reuse**: Build a skill once and utilize it across any skills-compatible agent.

## Links
For more detailed information and to get involved, please visit the official resources:
*   [Documentation](https://agentskills.io){:target="_blank"}
*   [Specification](https://agentskills.io/specification){:target="_blank"}
*   [Example Skills](https://github.com/anthropics/skills){:target="_blank"}
*   [Discord](https://discord.gg/MKPE9g8aUy){:target="_blank"}
*   [Client Showcase](https://agentskills.io/clients){:target="_blank"}
*   [GitHub Repository](https://github.com/agentskills/agentskills){:target="_blank"}