Loading...
Loading...
This GitHub Blog article analyzes best practices for creating `agents.md` files—configuration files that define custom AI agents for GitHub Copilot—based on patterns from over 2,500 repositories. The focus is on building effective specialized AI assistants with specific personas and clear operational guidelines rather than generic helpers.
Key insights include: successful agents require specific personas ("test engineer," not "helpful assistant"), executable commands, and concrete code examples. The most effective configurations include explicit constraints—what agents should never touch (secrets, vendor directories, production configs). The article identifies six core components for winning agents: commands, testing practices, project structure, code style, git workflows, and safety boundaries. It provides actionable templates and emphasizes iterative improvement over perfect upfront planning.
Building on foundational concepts, this resource explores technical skills at a deeper level. It's designed for PMs who have some AI experience and want to develop more sophisticated skills.
Ready to explore this resource?
Go to GitHub BlogThis guide teaches practitioners how to build effective AI prototypes through a structured, 12-step execution pipeline. Rather than creating impressiv...
This free interactive course teaches product managers how to use Claude Code—Anthropic's CLI tool—for AI-powered PM work. Uniquely, the course is taug...
This official Anthropic engineering guide provides battle-tested patterns for using Claude Code effectively. It covers project customization through C...