Codebase mapping

Skill bg-szy/TOP-SKILLS/skills/marketplace/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.From its SKILL.md

Install

npx -y skills add bg-szy/TOP-SKILLS --skill codebase-mapping

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SKILL.md

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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

Clone or access the target repository
Generate file tree excluding noise (node_modules, pycache, .git, etc.)
Parse imports to build dependency graph
Identify entry points (main.py, index.ts, setup.py, pyproject.toml)
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

What ships with it: 1 file

25.3 KB alongside SKILL.md

skill-report.json25.3 KB