testing-skills-with-subagents

Name: testing-skills-with-subagents
Availability: InStock
Author: kanutocl

Test AI agent skills using Red-Green-Refactor and pressure simulations.

Updated Jan 19, 2026

Search Tags:#testing #tdd #refactoring #validation #skills #agent

Ready to run

npx skills add https://github.com/KanuToCL/SonoTag --skill testing-skills-with-subagents-kanutocl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: testing-skills-with-subagents
Source: https://github.com/KanuToCL/SonoTag/tree/main/.claude/skills/testing-skills-with-subagents
Command: npx skills add https://github.com/KanuToCL/SonoTag --skill testing-skills-with-subagents-kanutocl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that your AI agent skills are robust, resistant to rationalization, and consistently applied, preventing common failure modes and ensuring reliable performance under pressure.

Core Features & Use Cases

TDD for Skills: Applies the Red-Green-Refactor cycle to skill documentation and agent behavior.
Pressure Testing: Simulates real-world scenarios with multiple pressures (time, sunk cost, authority) to expose weaknesses.
Rationalization Countermeasures: Identifies and explicitly blocks common excuses agents use to bypass rules.
Use Case: Before deploying a new skill that enforces coding standards, use this Skill to simulate scenarios where an agent might be tempted to cut corners (e.g., tight deadlines, urgent fixes) and ensure the skill prevents these violations.

Quick Start

Use the testing-skills-with-subagents skill to test a new skill by running baseline scenarios without the skill, then writing the skill to address observed failures, and finally refactoring to close any loopholes.

Frequently Asked Questions about testing-skills-with-subagents

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

FAQPage Schema

How do I test AI agent skills to prevent rationalization under pressure?▼

Testing AI agent skills involves applying a Red-Green-Refactor methodology to skill documentation. This process simulates real-world pressures like time and sunk costs to identify rationalizations and ensure strict rule compliance.

What is the Red-Green-Refactor cycle for skill validation?▼

The Red-Green-Refactor cycle for skill validation involves running baseline scenarios to observe failures, writing skill documentation to address them, and refactoring to close loopholes. This iterative process bulletproofs agent behavior against common failure modes.

How do I validate agent behavior before deploying coding standard enforcement?▼

Validate agent behavior by simulating scenarios where cutting corners is tempting, such as urgent fixes or tight deadlines. This pressure testing ensures your skill explicitly blocks common excuses and prevents rule violations before deployment.

Can I use TDD methodology to improve AI agent documentation?▼

Yes, you can apply TDD to AI agent documentation by iteratively refining skill logic. By testing baseline scenarios, writing rules to address observed failures, and refactoring, you build explicit counter-measures against common failure modes.

Why does my AI agent bypass skill rules when facing tight deadlines?▼

AI agents bypass skill rules under pressure due to common rationalizations like sunk cost or authority bias. Without explicit counter-measures and pressure testing during development, agents will find loopholes to cut corners in urgent scenarios.

What are the limitations of testing skills with subagents?▼

The limitations include the requirement for iterative refinement and the challenge of anticipating every real-world pressure. You must continuously refactor skill logic to close loopholes, as static documentation cannot anticipate all rationalization paths.