AGENTS.md rules and skills for AI coding agents, distilled from classic software engineering books.
agent-rules-books is easy to set up with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
gh repo clone ciembor/agent-rules-booksOpen or clone the template repository.
Move the relevant AGENTS.md content into your project root.
Update project-specific commands, environment rules, and sensitive areas.
This repository provides ready-to-use rule sets for AI coding agents like Codex, Cursor, and Claude Code. The rules are inspired by well-known programming books on clean code, refactoring, domain-driven design, and system architecture. Each rule set comes in three versions: full, mini, and nano, so you can choose the level of detail that fits your needs.
This repository contains AGENTS.md rules and skills for AI coding agents, distilled from classic software engineering books about refactoring, architecture, domain-driven design, and code quality. It provides ready-to-use rule sets inspired by books such as Clean Code, Refactoring, Domain-Driven Design, Clean Architecture, and Designing Data-Intensive Applications. Each rule set is available in three tool-agnostic Markdown versions: full (canonical complete source), mini (recommended for most real tasks), and nano (compact fallback for tight context budgets). The repository includes setup instructions for Codex, Claude Code, and Cursor, covering always-on vs on-demand usage, skills, scoped rules, MCP or RAG patterns. It is MIT licensed and aims to improve code quality and architecture adherence when using AI coding assistants.
Strong trust signals; still review the README and permissions before production use.
Last commit was about 66 days ago.
1774 GitHub stars indicate community interest.
3 open issues signal maintenance load.
MIT license detected.
Improve code quality by injecting clean code principles into AI agent suggestions.
Guide AI agents to follow domain-driven design patterns in new projects.
Ensure AI-generated code adheres to refactoring best practices.
Teach AI agents about data-intensive application design concepts.
Standardize AI agent behavior across a team using shared rule sets.
Agent instruction templates should be reviewed before use because they can change coding-agent behavior across an entire repository.
Avoid copying rules that grant broad filesystem, shell, network, or secret access without adapting them to your project.
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A drop-in AGENTS.md file that makes coding agents behave like senior engineers, eliminating sycophancy and forcing verification.
Convert and sync AI coding-agent rule files between formats with zero dependencies.
Generate AGENTS.md from your codebase in one command. Free, instant, no API key.
AGENTS.md rules and skills for AI coding agents, distilled from classic software engineering books.
Software developers using AI coding assistants, Tech leads wanting to enforce coding standards, Architects designing systems with AI help
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Agent instruction templates should be reviewed before use because they can change coding-agent behavior across an entire repository. Avoid copying rules that grant broad filesystem, shell, network, or secret access without adapting them to your project.
Similar or complementary options to evaluate include agents-md, agentsync, agentseed.
Jun 7, 2026
2 security/trust notes recorded.
Setup difficulty is 2/5.