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🛠️ Tool
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A new Claude Code plugin provides a CLAUDE.md file designed to address specific LLM coding pitfalls recently highlighted by Andrej Karpathy. The tool targets common issues in AI-assisted coding where models make silent assumptions, overcomplicate implementations, and make unnecessary orthogonal edits to unrelated code sections. By enforcing strict behavioral principles, the plugin aims to make AI agents more reliable and predictable when working inside existing codebases.
Key Features:
• Think Before Coding: LLMs frequently pick an interpretation silently and proceed without verification. This principle forces the model to state assumptions explicitly and present multiple interpretations when ambiguity exists. It requires the model to push back if a simpler approach is available and to stop and request clarification when confused.
• Simplicity First: To combat the tendency toward overengineering, this principle restricts speculative features and single-use abstractions. The model is instructed not to add error handling for impossible scenarios or introduce configurability that was not requested. The test applied here is whether a senior engineer would consider the implementation overcomplicated. If so, the model must rewrite it.
• Surgical Changes: When modifying existing code, models often refactor or format adjacent sections as a side effect. This principle restricts edits strictly to the user's request. The model must not improve unrelated code, must match the existing style even if it differs from its own preference, and must only mention unrelated dead code rather than deleting it. It is only permitted to remove orphans created by its own current changes.
• Goal-Driven Execution: This principle transforms imperative tasks into verifiable goals using a tests-first approach. Instead of executing a vague command like "add validation", the model is instructed to write tests that reproduce the issue or define the expected behavior, and then implement the code to make those tests pass. For multi-step tasks, the model must state a brief plan with verification checks for each step.
Technical Implementation:
The guidelines are contained within a single CLAUDE.md file. Users can install this configuration directly within Claude Code. The process involves adding the marketplace repository using the command /plugin marketplace add forrestchang/andrej-karpathy-skills, followed by executing /plugin install andrej-karpathy-skills@. This integrates the behavioral rules directly into the agent's system context.
Use Cases:
• Maintaining codebase integrity during AI-assisted refactoring by preventing the model from touching orthogonal code.
• Reducing code bloat by ensuring the model implements only the requested functionality without speculative abstractions.
• Enabling longer autonomous loops by providing the model with strong, verifiable success criteria based on test execution.
Limitations:
The overall effectiveness of these guidelines depends heavily on the underlying model's adherence to system prompts and context instructions. Complex, multi-step tasks may still require human intervention to evaluate whether the success criteria were genuinely met. Note: haven't tested personally.
🔹 tool
#claudecode #llm #ai_coding #prompt_engineering
🔗 Source:
https://github.com/multica-ai/andrej-karpathy-skills/blob/main/CLAUDE.md
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