AgentLint

What it does

I analyzed 200+ versions of Claude Code updates and context optimization papers to build AgentLint. It runs 33 data-backed checks to ensure your codebase is "AI-Ready"—slashing hallucinations and boosting agent reasoning by optimizing repo structure.

AgentLint audits your CLAUDE.md, AGENTS.md, .cursor/rules, CI workflows, hooks, and .gitignore against 33 data-backed checks. The first linter for your AI agent harness.

Your AGENTS.md , CLAUDE.md , CI, and hooks are the agent harness. AgentLint lints it — for Claude Code, Cursor, and Codex. Audits CLAUDE.md , AGENTS.md , .cursor/rules , .github/copilot-instructions.md , plus your CI workflows, pre-commit hooks, and .gitignore — every check cites a primary source. In February 2026, Mitchell Hashimoto (HashiCorp) coined harness engineering . Days later, OpenAI formalized it. LangChain gave the cleanest definition: if you're not the model, you're the harness — and the harness is where the real bottleneck lives. For a coding agent, the harness isn't a framework — it's already in your repo. AGENTS.md and CLAUDE.md are the persistent rules. .cursor/rules and…from agentlint.app

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