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 a set of 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 First: Blocks speculative abstractions, unrequested configurability, and unnecessary error handling so code stays minimal. - Surgical Changes: Restricts edits to lines that trace directly to the user's request, matching existing style and leaving unrelated code untouched. - Goal-Driven Execution: Converts vague tasks into verifiable success criteria, such as writing a failing test before fixing a bug. - Use Case: When asking an AI to refactor a function or fix a bug, activate these guidelines so the change is minimal, assumptions are stated explicitly, and success is verified by tests rather than by "it looks right". ## Quick Start Ask the AI to apply the karpathy-guidelines skill while implementing or reviewing your next code change so it states assumptions, keeps edits surgical, and defines verifiable success criteria.
