What problem does it solve? Teams adopting AI coding agents on existing (brownfield) projects often don't know whether their current tech stack helps or hinders agent workflows. 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 - Automatic stack detection: Reads project markers like package.json, Cargo.toml, pyproject.toml, go.mod, and Gemfile 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 CLAUDE.md/AGENTS.md rule entries so agents can work effectively despite the gap. - Use Case: A team with an Express.js codebase runs the assessment, learns their stack fails the typed and convention-based gates, and receives ready-to-paste instruction file rules covering middleware order, error handling, and validation conventions. ## Quick Start Run the stack assessment on my current project directory and tell me how agent-friendly my stack is.
