How to Generate a CLAUDE.md File for Your Repository

For years, the README.md has served as the primary entry point for human developers. It describes the "what" and the "how" of a project, providing just enough context to get a local environment running. But as we transition into an era where AI agents, like Claude Code, Cursor, and Cline, are responsible for writing a significant portion of our code, the traditional README is proving insufficient.

Part of the Auto Wiki guide.

AI agents don't just need to know how to install dependencies; they need to understand the architectural boundaries, the "why" behind specific design patterns, and the location of critical hotspots that are prone to regression. This is why the CLAUDE.md file has emerged as a new standard. It acts as a specialized instruction manual for AI, ensuring that when you ask an agent to "refactor the auth module," it doesn't just guess where to start.

New to these files? Start with the CLAUDE.md & AGENTS.md complete guide for the full picture, then come back here for the generation workflow.

However, writing a comprehensive CLAUDE.md by hand is a losing game. Codebases evolve daily, and static documentation becomes stale the moment a PR is merged. In this guide, we’ll explore how to leverage repowise to automate the generation and maintenance of your CLAUDE.md, ensuring your AI agents always have the highest-fidelity context possible.

What Is CLAUDE.md and Why It Matters

Popularized by Anthropic’s "Claude Code" CLI, CLAUDE.md is a dedicated context file located in the root of a repository. While a README is for humans, a CLAUDE.md is optimized for LLMs. It typically contains project summaries, build commands, testing instructions, and, most importantly, coding conventions.

The Context Problem for AI Agents

When an AI agent enters a repository, it starts with zero knowledge. To provide useful suggestions, it must perform a "crawl" of your file structure. In large codebases, this is both slow and expensive. If the agent misses a critical utility file or an established design pattern, it will likely generate code that is technically functional but architecturally "wrong" (e.g., using axios when the project has a custom fetch wrapper).

How CLAUDE.md Solves It

The CLAUDE.md file serves as a "cheat sheet" that is automatically loaded or referenced by the agent. It provides:

1. Instant Orientation: The agent knows the tech stack and entry points immediately.
2. Constraint Enforcement: It defines linting rules, naming conventions, and architectural "no-gos."
3. Efficiency: By providing a high-level map, the agent spends less time searching and more time solving problems.

The AI Context Gap

Manual CLAUDE.md vs. Auto-Generated

You could certainly open your editor and start typing out your project's rules. But for any professional-scale project, manual maintenance is a bottleneck.

Writing by Hand: Time-Consuming and Drifts

A manual CLAUDE.md is only as good as the last time a developer remembered to update it. If you move your services from /lib to /core, but forget to update the file, your AI agent will continue looking in the wrong place. This "documentation drift" is the primary cause of AI hallucinations in local development.

Auto-Generated: Always Accurate, Zero Effort

This is where repowise comes in. Rather than treating documentation as a static artifact, repowise treats it as a derivative of your source code and git history. By using static analysis and LLM-powered synthesis, repowise generates a CLAUDE.md that reflects the actual state of the code, not the intended state from six months ago.

Check our architecture page to understand how repowise works under the hood to bridge the gap between raw code and AI-ready intelligence.

How repowise Generates CLAUDE.md

Generating a CLAUDE.md with repowise is designed to be a "set and forget" process (the quickstart walks through the full setup). It integrates with your existing workflow and uses your preferred LLM provider (OpenAI, Anthropic, or even local models via Ollama) to synthesize the documentation.

Installation

First, install the repowise CLI from PyPI:

pip install repowise

Running repowise init

The init command indexes your repository and, by default, writes a CLAUDE.md alongside the rest of the .repowise index. During this process, you can specify which languages you are using (repowise supports 16, across full, good, and partial parsing tiers) and which LLM provider you want to use for synthesis. You don't need an API key to get started: repowise init --no-prose -y builds a deterministic CLAUDE.md and wiki from structure and git history alone, with no LLM calls.

repowise init --no-prose -y

If you already have an index and just want to regenerate CLAUDE.md on its own, without re-running full init, use the dedicated generate-claude-md command:

repowise generate-claude-md

For incremental refreshes after code changes, repowise update is usually the better fit since it re-analyzes only what changed. If you want init to skip writing CLAUDE.md entirely, pass --no-claude-md.

What Happens Under the Hood

When you run the generation command, repowise performs several high-intensity tasks:

1. Static Analysis: It parses your imports to build a directed dependency graph.
2. Git Mining: It analyzes your git history to find "hotspots": files with high churn and high complexity.
3. Symbol Extraction: It identifies every class, function, and module.
4. LLM Synthesis (skipped with --no-prose): It sends the gathered metadata (not the entire source code, to save tokens) to an LLM to generate a human-readable (and AI-consumable) summary of the architecture.

See auto-generated docs for FastAPI to understand what repowise produces at scale.

Anatomy of a Generated CLAUDE.md

A CLAUDE.md generated by repowise isn't just a list of files. It’s structured to provide maximum "signal" to the AI agent. Here is what a typical generated file includes:

Architecture Section

This section provides a high-level overview of the system design. Whether you’re using a Clean Architecture, Hexagonal, or a simple MVC pattern, repowise detects the relationships between directories to describe the flow of data.

Key Modules and Entry Points

repowise uses PageRank algorithms on your dependency graph to identify the "most important" files. These are usually your entry points (e.g., main.go, index.ts) or core utility hubs. Knowing these helps the AI understand where the "brain" of your application lives.

Tech Stack Detection

Instead of you listing your dependencies, repowise scans your package.json, go.mod, or Cargo.toml. It identifies not just the libraries, but their roles (e.g., "Primary Database: PostgreSQL via Prisma").

Hotspot Files

Using git intelligence, repowise flags files that are frequently changed and have high cyclomatic complexity. This warns the AI: "This file is fragile; proceed with caution."

MCP Tool Documentation

One of the most powerful features of repowise is its Model Context Protocol (MCP) integration. The generated CLAUDE.md includes instructions for the AI on how to use the ten flagship repowise tools, such as get_risk() or get_dead_code().

Anatomy of CLAUDE.md

Keeping CLAUDE.md Fresh

Documentation is a living organism. If it doesn't evolve with the code, it becomes a liability.

repowise update for Incremental Refresh

You don't need to re-generate the entire file every time. The repowise update command looks at the git diff since the last generation. It only re-analyzes the files that have changed, drastically reducing the time and token cost of updates. Most teams add this to a git pre-commit hook or a CI pipeline.

How Freshness Scoring Works

repowise assigns a Freshness Score to every documented module. If a module has been heavily modified but the documentation hasn't been updated, the freshness score drops. The CLAUDE.md can display these scores, signaling to the AI agent whether it should trust the documentation or perform a fresh scan of the source code.

CLAUDE.md + MCP Server: The Complete AI Context Stack

While CLAUDE.md provides the "static" context (the map), the repowise MCP Server provides the "dynamic" context (the GPS).

When you use an agent like Claude Code with repowise, the agent reads the CLAUDE.md to get its bearings. Then, it uses the ten flagship MCP tools to query deeper information on demand. For example:

• get_dead_code(): The agent can proactively suggest removing unused exports it finds during a refactor.
• get_dependency_path(source, target): The agent can see exactly how a change in the database schema will ripple through to the UI.
• get_why(query): The agent can search through historical architectural decisions to understand why a certain library was chosen.

See all ten MCP tools in action to see how this dynamic querying transforms the developer experience.

Tips for Getting the Best Results

To get the most out of your generated CLAUDE.md, consider these technical optimizations:

LLM Provider Choice

For the initial generation, we recommend using a high-reasoning model like Claude 3.5 Sonnet or GPT-4o. These models are significantly better at synthesizing architectural patterns from metadata. For daily incremental updates, a smaller, faster model (or a local model via Ollama) is usually sufficient.

Repo Size Considerations

In massive monorepos, a single CLAUDE.md can become too large, consuming too much of the agent's context window. repowise allows you to generate scoped documentation. You can have a root CLAUDE.md for the global architecture and sub-directory CLAUDE.md files for specific services or packages.

Scoped Context Strategy

Key Takeaways

1. AI Agents need their own README: CLAUDE.md is the standard for providing context to tools like Claude Code and Cursor.
2. Automation is mandatory: Manual files drift. Use repowise to keep your documentation in sync with your code and git history.
3. Intelligence over text: A good CLAUDE.md includes more than descriptions; it includes dependency insights, hotspot analysis, and tech stack maps.
4. Combine Static and Dynamic: Use CLAUDE.md for high-level orientation and the repowise MCP server for deep-dive codebase intelligence.

By automating your CLAUDE.md generation, you aren't just saving time: you're significantly increasing the reliability and "IQ" of the AI agents working on your team.

See what repowise generates on real repos in our live examples and start building a more intelligent codebase today.

FAQ: CLAUDE.md Generation

Does repowise send my entire codebase to an LLM?

No. repowise performs local static analysis first. It only sends metadata (file structures, dependency names, and symbol headers) to the LLM to generate the summaries, preserving your privacy and reducing token costs.

Can I use this with local LLMs?

Yes. repowise supports Ollama and other local providers, allowing you to generate documentation without your code ever leaving your machine.

How does this differ from a standard README?

A README is focused on human onboarding (setup, usage). A CLAUDE.md is focused on AI orientation (architectural boundaries, coding conventions, and tool instructions).

Does it support my language?

repowise currently supports 16 languages, including TypeScript/JavaScript, Python, Go, Rust, Java, C++, C, Ruby, and Kotlin. See our full list of supported languages for details.