How Andrej Karpathy Really Uses Claude – The Game-Changing CLAUDE.md Guide

Purpose of CLAUDE.md

CLAUDE.md is a project‑level instruction file read automatically by Claude‑based coding assistants. It contains strict, non‑suggestive rules that keep generated code consistent with the existing codebase and prevent production‑time failures.

Problem

Large language models frequently generate code that looks correct but does not fit the project's style, APIs, or conventions because they start coding without first reading the repository. This leads to code that must be rewritten or causes hidden bugs.

Guidelines

Read before you write – Thoroughly read the files you intend to modify, examine existing implementations of similar functionality, reuse established APIs and imports, and review the test suite to understand the true expected behavior.

Think before coding – Explicitly state assumptions (e.g., which authentication method is required), explain trade‑offs (e.g., cache adds speed but introduces invalidation complexity), list up to three alternative solutions with brief recommendations, and pause to ask for clarification when confused.

Keep it simple – Write the minimal code that solves the concrete problem. Avoid premature abstraction such as creating a generic EmailService class with strategy patterns for a single sendWelcomeEmail use case, or adding unnecessary configuration parameters.

Surgical modifications – Keep diffs tiny: change one line, test, then move to the next. Only touch code directly related to the task, match the existing style, and avoid reformatting unrelated sections.

Testing discipline – Before fixing a bug, write a failing test that reproduces the issue, then fix the bug and verify the test passes. Run the full test suite before and after changes to catch regressions. Avoid tests that only check implementation details; focus on observable behavior.

Debugging process – Read the entire error message and stack trace, reproduce the problem, change only one thing at a time, and do not add work‑arounds before understanding the root cause.

Dependency management – Add new dependencies only after confirming the goal cannot be achieved with existing libraries or the standard library. Verify maintenance status, size, and relevance before adding a package.

Communication practices – Explain what was changed and why, point out hidden risks, express uncertainty precisely, and tailor explanations to the audience’s knowledge level. Write clear commit messages that describe the specific fix.

Common failure patterns

Large‑scale refactoring when only a small change is needed.

Creating generic abstractions for a single use case (e.g., over‑engineered EmailService).

Introducing hidden architectural decisions without documentation.

Optimistic‑path bias that ignores error handling.

Hallucinating APIs, parameters, or library features that do not exist.

Style drift that conflicts with the project’s conventions.

Uncontrolled refactor cascades that lose focus on the original task.

Examples of over‑design and unnecessary complexity

Premature abstraction – Writing a full‑featured service class for a single email‑sending function.

Imagined error handling – Wrapping every statement in try/catch for errors that cannot occur.

Unnecessary configurability – Exposing batch size or retry count as environment variables when they never change.

Dead‑weight flexibility – Defining generic interfaces or abstract base classes that have only one implementation.

Verification and testing workflow

Convert vague tasks into concrete, testable specifications (e.g., “reject missing or invalid email with HTTP 400 and a clear error message, and add tests for both cases”).

For bug fixes, write a test that reproduces the reported behavior, ensure it fails, apply the fix, then confirm the test passes while the existing suite remains green.

If a task involves multiple steps (e.g., adding a new database field), list each step explicitly: migration, model update, API change, validation, tests, full suite run.

Debugging checklist

Read the full error message and stack trace.

Reproduce the issue before changing code.

Change only one thing at a time and test after each change.

Avoid adding work‑arounds until the root cause is understood.

When stuck, articulate the attempts made and the remaining uncertainty.

Dependency addition checklist

Can the task be completed with existing project code?

Can the standard library satisfy the requirement?

Is the dependency actively maintained (recent commits, issue activity, responsive maintainers)?

Is the package size reasonable for the functionality it provides?

When a new dependency is justified, document the reason (e.g., “adding zod for runtime schema validation because no existing library offers this capability”).

Commit message guidance

Write specific messages that describe the exact change (e.g., “Fix null pointer in user lookup when email contains uppercase characters”). Vague messages like “Fix bug” are unhelpful.

Community validation

A community‑maintained repository at https://github.com/multica-ai/andrej-karpathy-skills/tree/main contains a CLAUDE.md template derived from Karpathy’s observations. Tests reported by the repository claim that applying these rules reduces Claude’s code error rate from 41 % to 11 %.

Conclusion

Following the CLAUDE.md rules—reading existing code, thinking through assumptions and trade‑offs, keeping implementations minimal, making surgical edits, rigorously testing, managing dependencies carefully, and communicating changes precisely—helps LLM‑assisted developers produce code that aligns with project conventions, remains maintainable, and passes thorough testing before deployment.