codebase-mapping

name	codebase-mapping
description	Repository structure and dependency analysis for understanding a codebase's architecture. Use when needing to (1) generate a file tree or structure map, (2) analyze import/dependency graphs, (3) identify entry points and module boundaries, (4) understand the overall layout of an unfamiliar codebase, or (5) prepare for deeper architectural analysis.

Codebase Mapping

Maps repository structure and dependencies to enable targeted architectural analysis.

Quick Start

Generate a structural map:

python scripts/map_codebase.py /path/to/repo --output structure.json

Process

1. Clone or access the target repository
2. Generate file tree excluding noise (node_modules, pycache, .git, etc.)
3. Parse imports to build dependency graph
4. Identify entry points (main.py, index.ts, setup.py, pyproject.toml)
5. Detect boundaries - package structure and public APIs

Output Artifacts

The skill produces:

• file_tree.txt - Annotated directory structure
• dependencies.json - Import graph in adjacency list format
• entry_points.md - Identified entry points with descriptions
• module_map.md - Package boundaries and public interfaces

Key Patterns to Identify

Entry Point Detection

Look for these patterns:

• Python: if __name__ == "__main__", setup.py, pyproject.toml
• Node: package.json main/bin fields, index.js
• Frameworks: app.py (Flask), manage.py (Django), main.ts (Nest)

Dependency Classification

Classify imports as:

• External: Third-party packages (from package manager)
• Internal: Project modules (relative imports)
• Standard: Language standard library

Noise Exclusion

Always exclude:

node_modules/
__pycache__/
.git/
.venv/
venv/
dist/
build/
*.egg-info/
.mypy_cache/
.pytest_cache/

Integration with Other Skills

This skill provides the foundation for:

• data-substrate-analysis → Focus on types.py, models.py
• execution-engine-analysis → Focus on runner files
• control-loop-extraction → Focus on agent.py, loop files
• component-model-analysis → Focus on base classes

Example Output

## Repository: langchain

### Structure Summary
- 342 Python modules across 28 packages
- Primary entry: langchain/__init__.py
- Core packages: agents, chains, llms, tools

### Key Files for Analysis
- Types: langchain/schema.py, langchain/types.py
- Execution: langchain/agents/executor.py
- Tools: langchain/tools/base.py