codebase-mapping
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.
Best use case
codebase-mapping is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using codebase-mapping should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/codebase-mapping/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How codebase-mapping Compares
| Feature / Agent | codebase-mapping | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Codebase Mapping Maps repository structure and dependencies to enable targeted architectural analysis. ## Quick Start Generate a structural map: ```bash 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 ```markdown ## 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 ```
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