What problem does it solve? Teams adopting AI coding agents on existing (brownfield) projects often don't know whether their current stack supports agent workflows well. This Skill inspects the codebase, scores each stack component against four quality gates (typed, convention-based, popular in training data, well-documented), and produces concrete compensation strategies instead of recommending a stack rewrite. ## Core Features & Use Cases - File-driven stack detection: Reads project markers like package.json, pyproject.toml, Cargo.toml, go.mod, and config files to identify language, framework, build tool, test runner, package manager, CI/CD, and deployment target. - Quality gate scoring with evidence: Scores each component against the four agent-friendly criteria, citing the specific file or config that proves each pass or fail. - Ready-to-paste compensation rules: For every failed gate, generates concrete AGENTS.md instruction entries (e.g., routing conventions, type annotation rules) so agents can work effectively despite gaps. - Use Case: A team with an Express.js codebase runs the assessment, learns the stack fails the typed and convention-based gates, and receives paste-ready AGENTS.md rules documenting middleware order and validation patterns before running a health check. ## Quick Start Ask the AI to assess my stack for agent-friendliness in the current project directory.
