rule-editing-guide

Guide writing and editing AI agent rules using nine principles.

2|1|Updated Jan 16, 2026
One-click install
npx skills add https://github.com/tundraray/overture --skill rule-editing-guide
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rule-editing-guide
Source: https://github.com/tundraray/overture/tree/main/skills/rule-editing-guide
Command: npx skills add https://github.com/tundraray/overture --skill rule-editing-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides teams in writing and refining rules that govern how AI agents behave, improving execution accuracy and reducing ambiguity in prompts and command definitions.

Core Features & Use Cases

  • Principle-based framework encapsulates the 9 core principles for rule editing (context efficiency, unified notation, duplication avoidance, aggregate responsibilities, measurable criteria, NG patterns, explicit assumptions, priority by importance, and scope boundaries).
  • Scope and consistency provides explicit boundaries and a unified style to ensure rules are applied consistently across skills and prompts.
  • Use cases when creating or editing any skill, agent instruction, or command definition, apply the guide to improve clarity, reduce errors, and facilitate review.

Quick Start

Apply the rule-editing-guide when drafting or revising any rule: outline the principles, check for duplication, verify scope, and ensure measurable criteria.

Frequently Asked Questions about rule-editing-guide

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

FAQPage Schema
What is the best way to write AI agent rules for reliable behavior?

Writing AI agent rules reliably requires applying a principle-based framework that enforces context efficiency, unified notation, and measurable criteria to reduce ambiguity and execution errors.

How do I edit LLM prompts to improve execution accuracy?

To edit LLM prompts for execution accuracy, apply nine core editing principles: verify scope boundaries, eliminate duplication, check NG patterns, and ensure explicit assumptions and priority by importance.

Why does my AI agent ignore command definitions or behave inconsistently?

AI agents behave inconsistently when command definitions lack explicit scope boundaries and unified notation, making rule application ambiguous across skills and prompts.

Can I use a rule-writing framework for any domain of agent prompts?

Yes, this rule-writing framework applies when creating or modifying skills, agent prompts, or command definitions across any domain to maximize execution accuracy and consistency.

How to check LLM prompts for duplication and scope boundaries?

Checking LLM prompts for duplication and scope boundaries involves reviewing rule definitions against aggregate responsibilities and explicit scope principles to ensure no overlapping instructions exist.