AI Agent Skill HUB
multiAI Summary Pending

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.

231 stars
byaiskillstore
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Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/codebase-mapping/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/dowwie/codebase-mapping/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/codebase-mapping/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How codebase-mapping Compares

Feature / Agentcodebase-mappingStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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.

Which AI agents support this skill?

This skill is compatible with multi.

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
```