Filesystem Navigation
Guidelines for systematically exploring and understanding directory structures.
Best use case
Filesystem Navigation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Guidelines for systematically exploring and understanding directory structures.
Teams using Filesystem Navigation 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/filesystem_navigation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Filesystem Navigation Compares
| Feature / Agent | Filesystem Navigation | 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?
Guidelines for systematically exploring and understanding directory structures.
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
# Filesystem Navigation ## When to Use This Skill When you are asked to explore, understand, or map a project's file structure. This includes requests like "what is this project?", "show me the structure", or "help me find where X is". ## Strategy ### Start at the Root Always begin by listing the top-level directory. The root reveals the project type faster than anything else: - `README.md` or `README.rst` → start here, it's the author's own summary - `requirements.txt`, `pyproject.toml`, `package.json` → tells you the language and dependencies - `Dockerfile`, `docker-compose.yml` → the project is containerized - `Makefile`, `justfile` → there are predefined commands to run - `.env.example` → environment variables are needed; never read `.env` itself ### Explore Breadth Before Depth List all top-level directories before diving into any single one. Build a mental map: - `src/` or `app/` → application code lives here - `tests/` or `test/` → test suite - `config/` or `conf/` → configuration - `docs/` → documentation - `scripts/` or `bin/` → utility scripts - `migrations/` or `alembic/` → database migrations ### Go Deeper with Purpose Don't read every file. Choose what to read based on what you're trying to answer: - To understand **what the project does** → README, then entry point - To understand **how it's structured** → list `src/` recursively - To understand **how to run it** → README, Makefile, Dockerfile, config - To understand **dependencies** → requirements.txt, package.json, pyproject.toml ## Things to Avoid - Don't assume a file's purpose from its name alone — `utils.py` could contain anything - Don't read binary files (images, compiled files, databases) - Don't read `.env` files — they may contain secrets - Don't try to read `node_modules/`, `__pycache__/`, `.git/`, or other generated directories - Don't list deeply nested directories all at once — go level by level ## Signals That Help - A `__main__.py` or `if __name__ == "__main__"` block indicates an entry point - A file named `app.py`, `main.py`, or `server.py` is usually the entry point - `__init__.py` files in Python indicate a package; they may re-export key symbols - Hidden files (`.gitignore`, `.flake8`, `.pre-commit-config.yaml`) reveal tooling choices
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