What problem does it solve?
This Skill helps reduce common coding mistakes made by Large Language Models (LLMs) by providing a set of behavioral guidelines. It aims to improve code quality, clarity, and maintainability.
Core Features & Use Cases
- Pre-coding best practices: Encourages explicit assumption stating, surfacing tradeoffs, and clarifying confusion before implementation.
- Simplicity focus: Guides towards writing minimum viable code, avoiding speculative features or over-abstraction.
- Surgical editing: Promotes touching only necessary code, cleaning up only one's own mess, and matching existing styles.
- Goal-driven execution: Emphasizes defining verifiable success criteria and looping until verified.
- Use Case: When asking an LLM to refactor a piece of code, use these guidelines to ensure the LLM focuses only on the requested changes and doesn't introduce unnecessary complexity or side effects.
Quick Start
Apply the karpathy-guidelines skill to ensure the LLM writes simple, surgical, and goal-driven code.