What problem does it solve? Answering "how big is this repo" or "what languages does this project use" requires manually counting files and lines, which is slow and error-prone on large codebases. This Skill automates repository size and composition analysis with pygount, producing accurate language breakdowns without crawling dependency directories. ## Core Features & Use Cases - Language Breakdown Summaries: Generate per-language tables with file counts, code lines, comment lines, and percentages using pygount's summary format. - Targeted Language Filtering: Restrict analysis to specific file types (e.g., only Python files) with the --suffix flag for faster, focused scans. - Structured JSON Output: Emit machine-readable results for integration into automation pipelines and reporting scripts. - Use Case: A developer inherits an unfamiliar monorepo and needs to understand its tech stack composition. The Skill runs pygount with proper folder exclusions and returns a clear table showing the project is 70% Python, 20% TypeScript, and 10% YAML, with code-to-comment ratios per language. ## Quick Start Ask the AI to analyze the current repository's size and language breakdown using pygount, excluding dependency folders like node_modules and venv.