GitHub Copilot app for Beginners: How to teach Copilot about your codebase

GitHub Copilot can adapt to unique project rules by reading guidance stored directly in the repository. Adding a .github/copilot-instructions.md file lets teams define standards such as architecture preferences, naming conventions, or testing expectations. Path-specific instructions can also be placed in .github/instructions folders, applying rules only to relevant file types. This setup ensures Copilot follows established patterns without requiring repeated explanations for each task.

For specialized workflows like quality checks or debugging, Copilot supports custom agent skills. Skills are stored in .github/skills folders, each with its own instructions and optional scripts. When Copilot encounters a task matching a skill’s description, it loads the relevant process automatically. This allows the AI to run predefined commands instead of guessing the correct steps, improving consistency in routine procedures.

Larger or multi-file tasks, such as accessibility reviews, benefit from custom agents. These agents are defined in .github/agents and can include tailored instructions, tools, and project context. A custom agent acts as a specialized version of Copilot, suited for recurring work that spans multiple files or requires specific tools. This approach helps maintain focus and accuracy for complex, repetitive tasks.

Beyond the repository, Copilot can connect to external tools using MCP servers like Playwright. These servers enable the AI to interact with applications in a browser, run manual tests, or access documentation. Once configured, Copilot can verify not only code correctness but also user interface behavior. Developers can explore pre-built configurations in the Copilot app or adapt community-shared resources from Awesome Copilot to streamline setup.