Your Codebase Is Probably Fighting Claude (Part 1) (ambient-code.ai)

The author announced AgentReady, an open-source tool that evaluates how “AI-ready” a codebase is for generative-agent development (with the repo at github.com/ambient-code/agentready). AgentReady runs in seconds and scores repositories against 25 research-backed attributes across four categories: documentation quality, test coverage, architecture clarity, and development practices. It produces a prioritized, automation-friendly report with concrete fixes (e.g., add tests, update README/architecture docs, add CLAUDE.md) and weighs checks by their measured impact on AI-generated code quality. AgentReady also includes repomix, which builds a context-optimized representation of your repo, and skill-spotter, which detects reusable patterns to propose Claude Skills; a GitHub Actions continuous-learning pipeline keeps these artifacts up to date. This matters because AI coding is pattern matching: models succeed when the necessary patterns and validation mechanisms exist in the codebase. The author provides a simple A/B test protocol—run three real tasks before and after fixing top AgentReady issues to measure pass rates, iteration counts, and how much generated code works without modification—demonstrating how documentation, tests, and architecture docs materially improve agent performance. The project targets practical, research-aligned levers (CI tests, TDD/spec-kit, clear architecture) and invites community feedback to tune checks and extend coverage.