Open-source templates, conventions, and validation rules that turn coding-agent sessions into reliable, reviewable changes. Works with Claude Code, Codex, Cursor, and other AI coding agents.
Code quality and agent-readiness overlap, but they solve different problems. Learn what AI coding agents need beyond clean code, tests, and maintainable architecture.
→ Read more A practical audit checklist for finding the gaps that make Claude Code, Codex, Cursor, and other coding agents fail inside a repository.
→ Read more Backend and frontend repositories need different AGENTS.md instructions. This guide shows what to include for APIs, services, UI apps, design systems, and full-stack projects.
→ Read more AGENTS.md is not a one-time setup file. This guide shows when to update it, what to review, and how to keep coding-agent instructions aligned with tests, architecture, tools, and releases.
→ Read more Most AGENTS.md files fail because they are too vague, stale, or unverifiable. Here are 10 mistakes that break Claude Code, Codex, Cursor, and other coding agents — and how to fix each one.
→ Read more Ten real AGENTS.md files from public open-source repositories, each annotated for what works, what does not, and what to copy. The fastest way to learn AGENTS.md is to read the best ones.
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Every major coding agent has its own context file format. AGENTS.md, CLAUDE.md, .cursorrules, .github/copilot-instructions.md — they all aim to give agents durable project context but work differently. Here is how to choose.
→ Read more OpenAI Codex needs structured repository context to produce reliable code changes. Here is how to prepare your repository so Codex understands your codebase, follows conventions, and hands back reviewable output.
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Claude Code needs more than a README to work reliably. Here is how to prepare your repository so Claude Code understands your codebase, respects your conventions, and produces reviewable output.
→ Read more AGENTS.md and Cursor Rules both aim to give coding agents better context. They work differently, live in different places, and serve different scopes. Here is how to choose.
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Harness engineering applies context engineering to the actual coding-agent workflow — feature intake, story packets, validation, review, and decision recording.
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Most coding-agent failures are not model failures. They are repo-context failures. Better prompts help. But repositories need instructions, architecture context, and validation rules.
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Context engineering for coding agents means shaping the repo environment so agents receive durable, relevant context before they write code.
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Most AGENTS.md files nobody reads. Here is how to write one that coding agents actually use — specific, concise, and validated by real sessions.
→ Read more An agent-ready repository gives coding agents the instructions, architecture context, validation rules, and decision history they need to make useful, reviewable changes.
→ Read more A practical AGENTS.md template for agent-ready repositories — copy and adapt for Claude Code, Codex, Cursor, and other coding agents.
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A working harness combines tests, docs, scripts, conventions, fixtures, examples, and decision records.
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