by github · Agent Tool · ★ 32.8k
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🤖 Awesome GitHub Copilot A community created collection of custom agents and instructions to supercharge your GitHub Copilot experience across different domains, languages, and use cases. 🚀 What is Awesome GitHub Copilot? This repository provides a comprehensive toolkit for enhancing GitHub Copilot with specialized: 👉 Awesome Agents - Specialized GitHub Copilot agents that integrate with MCP servers to provide enhanced capabilities for specific workflows and tools 👉 Awesome Instructions - Comprehensive coding standards and best practices that apply to specific file patterns or entire projects 👉 Awesome Hooks - Automated workflows triggered by specific events during development, testing, and deployment 👉 Awesome Agentic Workflows - AI-powered repository automations that run coding agents in GitHub Actions with natural language instructions 👉 Awesome Skills - Self-contained folders with instructions and bundled resources that enhance AI capabilities for specialized tasks 👉 [Awesome Plugins](docs/READ
| Stars | 32,778 |
| Forks | 3,997 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 54.12/100 |
| Open Issues | 46 |
| Last Updated | 2026-05-12 |
| Created | 2025-06-11 |
| Platforms | python |
| Est. Tokens | ~4133k |
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awesome-copilot is Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.. It is categorized as a Agent Tool with 32.8k GitHub stars.
awesome-copilot is primarily written in Python. It covers topics such as agent-skills, agents, ai.
You can find installation instructions and usage details in the awesome-copilot GitHub repository at github.com/github/awesome-copilot. The project has 32.8k stars and 3997 forks, indicating an active community.
awesome-copilot is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-copilot on Agent Skills Hub include mindsdb, kubesphere, awesome-ai-apps. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.