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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.
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Go to GitHub BlogThis ProductTapas newsletter article advocates for Claude Code as an essential PM productivity tool. The author claims to have saved over 200 hours in...
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 free short course from DeepLearning.AI teaches how to use large language models through the OpenAI API. Taught by Isa Fulford (OpenAI) and Andrew...