Deep Context Generation with PMAT

Generate compressed codebase context using PMAT to produce LLM-optimized summaries that reduce code volume by 60-80%. Run `pmat context --output context.md --format llm-optimized` to scan your repository, honor .gitignore, and extract architecture diagrams, complexity heatmaps, dependency graphs, and key abstractions in highly compressed Markdown.

Architecture context generation produces visual outputs including ASCII architecture trees, complexity heatmaps, and dependency graphs that reveal hotspots and relationships without manual code review. PMAT extracts public APIs and key abstractions to accelerate onboarding and refactoring planning across codebases of varying languages and sizes.

Yes. Context generation produces onboarding summaries and architectural overviews suitable for new team members, refactoring planning, and project documentation. The compressed, LLM-optimized output format works across multiple programming languages and project scales.

Yes. The context generation scans the entire repository while honoring .gitignore rules, ensuring only relevant files are included. This produces accurate dependency graphs and architectural summaries that reflect your actual project scope.

Context generation produces highly compressed Markdown (60-80% reduction), ASCII architecture diagrams, complexity heatmaps showing code hotspots, dependency graphs revealing relationships, and extracted key abstractions and public APIs for LLM consumption.

PMAT compresses codebase context by 60-80% while preserving architectural meaning, complexity patterns, and dependencies. This reduces token usage and improves LLM focus on essential project structure, making context windows more efficient for onboarding, analysis, and refactoring workflows.