Llm Coding Principles
by Roy Yuen
Enforce senior-level coding standards (Surgical, Simple, Goal-Driven) on every AI-generated code change.
Free
Works with the AI tools you already use
See it in action
You say
Add a 'status' field to the User model, but don't touch any other parts of the database schema or the existing API endpoints.
Your agent does
Approaches: 1. Add nullable column (safest). 2. Add with default 'active'. Choosing #2 for immediate utility.
Success Criteria:
- migration adds
statustousers. - User model includes
status.
Diff:
- status = Column(String, default="active") Verification: Migration tested; field persists.
About this skill
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.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
9 installs
Downloaded by developers to date
Free forever
No account required to browse
Trust & safety
Security scanned
Verified clean 1 month ago
- Free forever, no account required
Creator
Frequently Asked Questions
Popular in AI Agents & LLM Ops
designing-hybrid-context-layers
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
agentic-workflow
A risk-aware, evidence-based engineering lifecycle protocol for robust agentic task execution and safety.
ai-coding-checklist
A 5-gate pre-flight audit to ensure your AI agent has the context, scope, and safety boundaries needed to code successfully.
prompt-engineer
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.