10x-stack-assess

To evaluate codebase agent-friendliness, assess the project stack against four quality gates: typed, convention-based, popular in training data, and well-documented. This generates a detailed report with scores, gaps, and specific improvement strategies.

An agent-friendly tech stack passes four quality gates: it is typed, convention-based, popular in training data, and well-documented. Meeting these criteria ensures the AI agent can understand and manipulate the codebase effectively.

Assess a brownfield project stack by analyzing its existing codebase against four agent-friendliness quality gates. The evaluation requires access to the project codebase and specific documentation to generate a comprehensive analysis with optimization insights.

Yes, analyzing existing project documentation is part of the stack assessment process. The evaluation uses documentation alongside the codebase to identify gaps in the four quality gates and provides specific strategies to enhance agent effectiveness.

The stack assessment requires direct access to the project codebase and specific documentation to function. Without these inputs, the evaluation cannot generate the detailed report with scores, gaps, and improvement recommendations.

The best way to identify quality gates is to run a stack assessment that checks if the project is typed, convention-based, popular in training data, and well-documented. This produces a detailed report highlighting gaps and next-step recommendations.