What problem does it solve? LLM-generated code often suffers from overcomplication, unrequested features, silent assumptions, and sprawling edits that touch unrelated code. This Skill provides behavioral guidelines, derived from Andrej Karpathy's observations on LLM coding pitfalls, that keep AI-assisted coding focused, minimal, and verifiable. ## Core Features & Use Cases - Think Before Coding: Surfaces assumptions, ambiguities, and simpler alternatives before implementation instead of silently picking an interpretation. - Simplicity and Surgical Changes: Enforces minimum-code solutions and restricts edits to only what the request requires, matching existing style. - Goal-Driven Execution: Transforms vague tasks into verifiable success criteria with test-driven loops and explicit step-by-step plans. - Use Case: When asking an AI to fix a bug or refactor a module, activate these guidelines so the agent writes a reproducing test first, changes only the relevant lines, and verifies the fix against defined criteria. ## Quick Start Apply the karpathy guidelines while refactoring this function, keeping changes minimal and defining verifiable success criteria first.
