Codebase Context Extractor Skill

Overview

This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.

Trigger Words

• “extract context”
• “codebase context”
• “code context”
• “analyze codebase”
• “codebase analysis”
• “code structure”
• “dependency analysis”
• “code relationships”
• “understand codebase”
• “map codebase”

When to Use This Skill

Use this skill when you need to:

• Understand the structure and organization of a large codebase
• Extract relevant context for a specific function, class, or module
• Analyze dependencies and relationships between code components
• Generate documentation or summaries of code sections
• Prepare context for code modifications or debugging
• Identify entry points and execution flows
• Map out API surfaces and public interfaces
• Understand data flow and state management

Instructions

When this skill is triggered, execute the context_extractor.py script with appropriate parameters.

Basic Usage

python /projects/workspace/codebase-context-extractor/context_extractor.py \
  --target-path <path_to_codebase> \
  --mode <extraction_mode> \
  --output <output_file>

Extraction Modes

1. full – Complete codebase analysis with all components
2. targeted – Focus on specific files, functions, or classes
3. dependency – Map dependencies and imports
4. flow – Trace execution flows and call chains
5. api – Extract public interfaces and API surfaces
6. data – Analyze data structures and models
7. hierarchy – Show class hierarchies and inheritance
8. summary – Generate high-level overview

Parameters

• --target-path (required): Path to the codebase to analyze
• --mode (required): Extraction mode (see above)
• --output (optional): Output file path (default: stdout)
• --focus (optional): Specific file, class, or function to focus on
• --depth (optional): Maximum depth for traversal (default: unlimited)
• --include-tests (optional): Include test files in analysis (default: false)
• --language (optional): Programming language (auto-detected if not specified)
• --format (optional): Output format (markdown, json, yaml, text) (default: markdown)
• --exclude (optional): Patterns to exclude (comma-separated)

Examples

1. Full codebase analysis:

python context_extractor.py --target-path ./my-project --mode full --output context.md

2. Targeted analysis of a specific class:

python context_extractor.py --target-path ./my-project --mode targeted --focus "UserService" --output user_service_context.md

3. Dependency mapping:

python context_extractor.py --target-path ./my-project --mode dependency --format json --output dependencies.json

4. Execution flow analysis:

python context_extractor.py --target-path ./my-project --mode flow --focus "main" --depth 5

Output Structure

The extractor generates structured output including:

For Full/Targeted Mode

• Project Overview: Language, structure, entry points
• File Organization: Directory structure and file purposes
• Key Components: Important classes, functions, modules
• Dependencies: External and internal dependencies
• Code Metrics: Lines of code, complexity estimates
• Context Summary: High-level understanding

For Dependency Mode

• Dependency Graph: Visual representation of dependencies
• Import Analysis: All imports and their usage
• Circular Dependencies: Detection and reporting
• Unused Dependencies: Potential cleanup targets

For Flow Mode

• Call Chains: Function call sequences
• Entry Points: Main execution paths
• Exit Points: Return and error handling
• Branch Analysis: Conditional execution paths

For API Mode

• Public Interfaces: Exported functions and classes
• API Documentation: Signatures and docstrings
• Usage Examples: How to use the API
• Versioning Info: API version and compatibility

Advanced Features

Smart Context Window Management

The extractor automatically manages context size to fit within LLM token limits:

• Prioritizes most relevant code sections
• Provides summaries for less critical parts
• Includes breadcrumb navigation for context

Multi-Language Support

Supports analysis of:

• Python
• JavaScript/TypeScript
• Java
• C#
• Go
• Rust
• C/C++
• Ruby
• PHP
• And more (extensible)

Intelligent Filtering

• Excludes generated code, build artifacts, and vendor directories
• Focuses on business logic and core functionality
• Configurable exclusion patterns

Integration with Other Tools

The context extractor output can be used with:

• Documentation generators
• Code review tools
• Refactoring assistants
• Bug tracking systems
• Development environments

Best Practices

1. Start with Summary Mode: Get a high-level overview before diving deep
2. Use Targeted Mode for Specific Tasks: Focus on relevant code sections
3. Combine with Dependency Analysis: Understand impact of changes
4. Leverage Flow Analysis for Debugging: Trace execution paths
5. Regular Updates: Re-run analysis as codebase evolves

Notes

• Large codebases may take time to analyze
• Consider using depth limits for very large projects
• JSON output is best for programmatic processing
• Markdown output is best for human reading
• The tool respects .gitignore patterns by default