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JuanJoseGonGi
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authoring-skills

Guide creation, testing, and packaging of AI Agent Skills with YAML frontmatter and Markdown.

Updated Aug 23, 2026
Search Tags:#testing#documentation#development workflow#TDD#ai agent#skill authoring
One-click install
npx skills add https://github.com/JuanJoseGonGi/skills --skill authoring-skills-juanjosegongi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: authoring-skills
Source: https://github.com/JuanJoseGonGi/skills/tree/main/authoring-skills
Command: npx skills add https://github.com/JuanJoseGonGi/skills --skill authoring-skills-juanjosegongi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide to creating, testing, and packaging AI Agent Skills, ensuring they are discoverable, efficient, and robust.

Core Features & Use Cases

  • Skill Anatomy: Understand the structure of a Skill (SKILL.md, scripts/, references/, assets/).
  • Workflow: Follow the RED-GREEN-REFACTOR cycle for skill development.
  • Testing: Learn TDD-based testing with subagents and pressure scenarios.
  • Use Case: You need to create a new AI Skill to help with code reviews. This Skill will guide you through defining its name, description, writing the instructions, and testing its effectiveness.

Quick Start

Use the authoring skills to create a new skill named 'code-review-assistant'.

Frequently Asked Questions about authoring-skills

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

FAQPage Schema
How do I create and package AI agent skills according to official specifications?▼

To create AI agent skills, follow a structured workflow covering skill anatomy like SKILL.md, scripts, and references, ensuring proper YAML frontmatter and Markdown instructions for robust packaging and discoverability.

What is the RED-GREEN-REFACTOR cycle for testing AI agent skills?▼

The RED-GREEN-REFACTOR cycle is a TDD-based development workflow that guides creating, testing, and refining AI agent skills using subagents and pressure scenarios to ensure robustness against rationalization.

How do I optimize AI agent skills for discoverability using Claude Search Optimization?▼

Claude Search Optimization (CSO) techniques are applied during skill authoring to enhance discoverability, ensuring the skill's naming conventions and progressive disclosure structure align with search algorithms.

Do I need Python to author and validate AI agent skills?▼

Yes, Python3 is required for skill authoring. Python scripting is used to initialize the skill structure and run validation checks, ensuring the YAML frontmatter and Markdown content meet official specifications.

What is progressive disclosure in AI skill anatomy and when should I apply it?▼

Progressive disclosure in skill anatomy structures information hierarchically, revealing complex instructions only when needed. Apply it during skill creation to maintain efficiency and prevent overwhelming the AI agent.

How do I bulletproof AI agent skills against rationalization during testing?▼

Bulletproofing against rationalization involves using TDD-based testing with subagents and pressure scenarios. This testing methodology validates the skill's instructions under stress to ensure consistent and accurate execution.

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