Generate structured markdown context from a directory for AI analysis.
npx skills add https://github.com/igorls/context-builder --skill context-builder-igorls
Please help me install this Agent Skill. Skill: context-builder Source: https://github.com/igorls/context-builder/tree/main Command: npx skills add https://github.com/igorls/context-builder --skill context-builder-igorls
This skill generates a comprehensive markdown context file from any directory, enabling effective AI analysis by streamlining code review and knowledge discovery processes.
Use the context-builder skill to generate a context file for the directory '/path/to/project' and output to 'project-context.md'.
High-intent search queries and answers about installing and using this skill.
Generating a markdown context for AI analysis involves recursively processing your project directory to aggregate files into a structured output. The context-builder skill filters by relevance and optionally adds AST signatures for deeper AI code review.
Preparing a large project for AI code review requires context budgeting to fit the AI model's context window. The context-builder skill prioritizes critical project files and applies ignoring patterns to optimize the generated markdown output.
Yes, context-builder supports AST signatures and structural summaries. These AST-based features provide deeper analysis by embedding code structure metadata within the generated markdown context file for AI processing.
Yes, you need the context-builder CLI tool installed to use this skill. The skill requires the CLI dependency, available at https://github.com/igorls/context-builder, to process directories and generate the markdown output.
Yes, you can filter files and apply ignoring patterns when creating a markdown context. The context-builder skill supports file filtering and ignore patterns to ensure only relevant code is included for the AI analysis context window.
AI analysis may fail when a codebase context exceeds the model's context window. Context-builder resolves this by applying context budgeting and relevance-based file ordering to limit and prioritize the markdown output within the AI's limits.
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