coding-guardrails

Name: coding-guardrails
Availability: InStock
Author: LinoWhy

Guide code writing, review, and refactoring with explicit assumptions and verifiable success criteria.

Updated Aug 17, 2024

Search Tags:#refactoring #llm #safety #coding #best-practices #code-review #guardrails

Ready to run

npx skills add https://github.com/LinoWhy/.dotfiles --skill coding-guardrails

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: coding-guardrails
Source: https://github.com/LinoWhy/.dotfiles/tree/main/agents/.agents/local-skills/coding-guardrails
Command: npx skills add https://github.com/LinoWhy/.dotfiles --skill coding-guardrails

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Behavioral guidelines to reduce common LLM coding mistakes. Use during writing, reviewing, or refactoring code to avoid overcomplication, surface assumptions, and define verifiable success criteria.

Core Features & Use Cases

Think Before Coding: explicitly surface assumptions, present interpretations, and ask when unclear.
Simplicity First: require minimal, well-scoped changes with no unnecessary abstractions.
Surgical Changes: touch only essential code and clean up after.
Goal-Driven Execution: convert tasks into verifiable goals and plan steps with checks.

Quick Start

State a brief plan outlining steps and verifiable checks before executing code edits.

Frequently Asked Questions about coding-guardrails

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent LLM coding mistakes during refactoring?▼

To prevent LLM coding mistakes during refactoring, apply behavioral guardrails that enforce minimal, well-scoped changes and require explicit assumptions before executing code edits. This surfaces hidden tradeoffs and stops overcomplication.

What is the best way to review code generated by AI models?▼

The best way to review code generated by AI models is to use guardrails that convert tasks into verifiable goals with a clear success checklist. This ensures generated changes are surgical and touch only essential code.

How do I set verifiable success criteria for LLM code generation?▼

Set verifiable success criteria for LLM code generation by stating a brief plan outlining steps and checks before executing edits. This goal-driven execution exposes assumptions and validates that surgical changes meet intended outcomes.

Why do language models introduce unnecessary abstractions when writing code?▼

Language models introduce unnecessary abstractions when writing code due to missing behavioral guidelines enforcing simplicity first. Applying coding guardrails restricts modifications to surgical, essential changes and prevents scope creep.

Can I use coding guardrails for both writing and reviewing code?▼

Yes, you can use coding guardrails for both writing and reviewing code. They guide developers to surface interpretations, ask when unclear, and define verifiable success criteria across the entire development lifecycle.

What are the limitations of relying on LLMs for code refactoring without guardrails?▼

Without guardrails, LLM code refactoring risks overcomplication and unverified assumptions. Limitations arise from touching non-essential code and lacking a plan-driven execution, leading to unchecked errors and failed success criteria.