agent-ready-codebase

To optimize a codebase for AI agent performance, you need to audit test coverage, file structure, type system usage, dev environment speed, and automated enforcement. This skill evaluates your project against these five pillars and provides a scorecard with prioritized, stack-specific improvements.

AI agents struggle with existing code often because of underlying weaknesses in the codebase itself. Issues like low test coverage, poor file structure, or slow dev environments hinder agent effectiveness, requiring a targeted audit to identify and resolve these specific bottlenecks.

The best way to audit a project for agentic coding readiness is applying a five-pillar framework that evaluates test coverage, file structure, type system usage, dev environment speed, and automated enforcement. This generates a detailed scorecard of findings with actionable, stack-specific guidance.

Improving test coverage and file structure for AI coding tools requires targeted, actionable steps based on an initial audit. This skill guides you through specific enhancements adapted to your project's language and stack, directly addressing weaknesses that reduce agent efficiency.

The codebase audit adapts to your project's specific language and stack when generating improvement recommendations. By evaluating fundamental principles like type system usage and dev environment speed, it provides concrete, stack-specific guidance rather than generic advice.

You should evaluate your codebase for automated enforcement and dev environment speed when integrating AI agents into your workflow and noticing inefficiencies. Assessing these areas helps pinpoint why agents struggle and provides actionable steps to make the code agent-friendly.