Expert Coding Governance for AI Agents
Does your AI agent sometimes over-engineer simple tasks or make sweeping, unnecessary changes to your codebase? llm-coding-principles is a high-performance directive standard that installs a "Senior Engineer" persona into your agent's workflow. It replaces guesswork with a rigorous four-pillar framework: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution.
What it does
- Think Before Coding: Forces the agent to surface assumptions and evaluate technical trade-offs before a single line is written.
- Simplicity First: Eliminates speculative abstractions and "future-proofing" to keep your codebase lean and maintainable.
- Surgical Changes: Ensures diffs are precise, touching only what is necessary to satisfy the requirements while avoiding unrelated "noise" or style drift.
- Goal-Driven Execution: Mandates a Red-Green-Refactor cycle where success criteria are defined and verified before the task is marked complete.
Why use this skill
Standard LLM prompting often leads to "hallucinated improvements"—refactoring code you didn't ask to change or adding dependencies you don't need. This skill acts as a non-negotiable quality gate. It’s ideal for developers using Cursor, Claude Code, or custom CLI agents who want consistent, production-grade output that mirrors the discipline of a human senior developer. It integrates seamlessly with TDD workflows and systematic debugging.