10x-stack-assess

To evaluate your project tech stack for AI agent compatibility, this Skill assesses your configuration files against four quality gates: typed, convention-based, popular, and well-documented criteria, generating a detailed assessment report at context/foundation/stack-assessment.md.

A tech stack is considered AI agent-friendly when it passes four quality gates: being strongly typed, convention-based, popular, and well-documented, ensuring AI agents can effectively understand and interact with the codebase.

You can check if your project setup is ready for AI code generation by running this Skill in your project directory. It parses dependency manifests like pyproject.toml and Cargo.toml to identify stack gaps that might affect AI performance before initiating health-checks.

Yes, this stack evaluation tool supports Go and Node.js projects by analyzing go.mod and package.json dependency manifests, alongside other supported formats like Gemfile and composer.json, to assess overall AI compatibility.

From an AI compatibility stack assessment, you should expect an output report containing documented scores, identified quality gate gaps, and ready-to-paste entries for your AGENTS.md file to guide AI agents.

You should run a tech stack quality assessment for AI workflows after your initial project setup on brownfield projects, specifically before running health-checks, to proactively identify stack issues that might degrade AI agent performance.