testing-skills-with-subagents

Validate skills by running baseline scenarios with subagents before deployment.

Updated Nov 5, 2025
One-click install
npx skills add https://github.com/mikeyobrien/ghclip --skill testing-skills-with-subagents-mikeyobrien
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: testing-skills-with-subagents
Source: https://github.com/mikeyobrien/ghclip/tree/main/.claude/skills/testing-skills-with-subagents
Command: npx skills add https://github.com/mikeyobrien/ghclip --skill testing-skills-with-subagents-mikeyobrien

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures your AI skills are robust, reliable, and resistant to "rationalization" by applying Test-Driven Development (TDD) principles directly to process documentation. It verifies skills work under pressure, closes loopholes, and guarantees consistent AI behavior, saving you from ineffective skill deployments.

Core Features & Use Cases

  • TDD for Skills: Applies the RED-GREEN-REFACTOR cycle to documentation, ensuring skills are written to prevent observed failures.
  • Pressure Scenarios: Tests skills with realistic, multi-pressure scenarios to expose weaknesses and agent rationalizations.
  • Rationalization Defense: Identifies and explicitly counters common excuses agents might use to bypass skill rules, making skills "bulletproof."
  • Use Case: You've created a new skill for enforcing Test-Driven Development. Use this skill to rigorously test it, ensuring agents follow the TDD process even under tight deadlines or other pressures, preventing them from finding excuses to skip tests.

Quick Start

Use the testing-skills-with-subagents skill to test my new 'secure-coding-practices' skill. Start with a baseline test scenario where an agent is tempted to skip security checks.

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 skills to prevent agents from rationalizing away requirements?

Test-Driven Development for skills applies RED-GREEN-REFACTOR cycles to documentation, identifying failures before deployment. This skill validates that agents follow rules consistently, even under pressure, by running baseline scenarios with subagents and capturing rationalizations to close behavioral loopholes.

What are pressure scenarios and why should I test my AI skills under them?

Pressure scenarios simulate realistic constraints—tight deadlines, competing priorities—that expose when agents skip steps or find excuses to bypass skill rules. Testing under pressure reveals weaknesses and ensures your skill documentation is robust enough to prevent agents from rationalizing noncompliance.

Can I use this skill to validate documentation-based AI workflows?

Yes. This skill processes documentation directly through TDD principles, running baseline tests and pressure scenarios with subagents to verify your skill meets structured requirements. It yields actionable outcomes showing where documentation fails to enforce consistent AI behavior.

How do I identify when an AI agent is rationalizing instead of following my skill?

This skill captures rationalizations—common excuses agents use to bypass rules—during pressure testing. By comparing baseline scenarios against pressure-test results, you surface inconsistencies in agent behavior and explicitly counter loopholes in your skill documentation.

What inputs do I need to prepare before testing a skill with this tool?

Prepare baseline test scenarios, pressure scenarios that introduce constraints, and documented skill rules. The skill applies these inputs through subagent validation cycles, identifying failures and rationalizations to ensure your skill is bulletproof before deployment.

Why use TDD principles for AI skill testing instead of manual validation?

TDD for skills systematically closes gaps in documentation by writing tests that expose failures first, then refactoring documentation to prevent them. This structured approach captures agent rationalizations under pressure and guarantees consistent behavior across scenarios, reducing deployment risk.