I reverse-engineered popular coding-agent instruction repos and distilled their best ideas into Awesome AGENTS.md, a stack-agnostic ruleset for AI coding agents. 📦

The core rules cover small, focused changes, security boundaries, debugging, and verification before an agent says a task is done. More specialized guidance lives in rules/ and is meant to be read when a task calls for it.

Take it for a spin 👉 https://github.com/khasky/awesome-agents-md

What makes this ruleset different:

One shared core. I can use the same rules with Claude Code, Codex, Gemini, and Cursor. The README explains how to set up each tool.
Specialized rules on demand. The framework-agnostic core stays lean. Agents read additional modules only when a task calls for them.
Less code by default. Before adding a new helper or dependency, the agent checks what already exists in the project and what the standard library can do. The goal is the smallest change that solves the problem.
Evidence before "done". The agent is told to run a relevant check, read the result, and report what passed. A confident "should work now" isn't verification.
Simple updates. With a local clone, I can pull the latest changes and start a new session. There are no modes to switch between.
Refined on real projects. I've revised the rules over many iterations while working on real applications. The public commit history shows how they've changed, and I keep revisiting them as models improve.

Try it, make it your own, and tell me what you'd improve. Issues and PRs are welcome! 😉

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