What is the main goal of testing skills with subagents?

The goal is to ensure AI agents follow specific process rules even under pressure or when they are tempted to take shortcuts by rationalizing their behavior.

Why is the RED phase baseline test necessary?

You must observe how an agent naturally fails without the skill to understand the specific rationalizations and logic gaps your documentation needs to address.

What defines a high-quality pressure scenario?

A great test combines at least three types of pressure, such as time constraints, sunk costs, and authority overrides, to force the agent to make a difficult choice.

How does this skill handle AI rationalizations?

It uses a Refactor cycle to capture agent excuses verbatim and then plugs those holes using explicit negations, rationalization tables, and red-flag lists.

Testing Skills with Subagents

Name: Testing Skills with Subagents
Author: lifangda

bylifangda

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Security & Testing

Applies the Red-Green-Refactor TDD cycle to verify and bulletproof process documentation against AI rationalizations.

About

This skill provides a rigorous framework for testing and validating Claude Code skills using subagents. By treating process documentation like code, it ensures that AI agents follow instructions even under high-pressure scenarios where they might otherwise rationalize bypassing rules. It guides users through creating realistic pressure tests, documenting agent failures verbatim, and iteratively refactoring instructions to close logic loopholes and ensure absolute compliance.

Key Features

RED Phase baseline testing to identify natural AI failure patterns
Meta-testing framework for continuous skill refinement
Explicit negation patterns to prevent spirit vs. letter violations
High-pressure scenario creation involving time, cost, and authority constraints
16 GitHub stars
Rationalization tables to document and counter common AI excuses

Use Cases

Bulletproofing compliance documentation for sensitive security protocols
Ensuring strict adherence to TDD workflows in production environments
Hardening skills that enforce discipline over speed or pragmatism

About

Key Features

RED Phase baseline testing to identify natural AI failure patterns
Meta-testing framework for continuous skill refinement
Explicit negation patterns to prevent spirit vs. letter violations
High-pressure scenario creation involving time, cost, and authority constraints
16 GitHub stars
Rationalization tables to document and counter common AI excuses

Use Cases

Bulletproofing compliance documentation for sensitive security protocols
Ensuring strict adherence to TDD workflows in production environments
Hardening skills that enforce discipline over speed or pragmatism