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. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "codebase-mapping" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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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