ring:testing-skills-with-subagents
A meta-skill for building robust AI agent skills using a TDD approach: define failure (RED), implement the skill (GREEN), and plug rationalization loopholes (REFACTOR).
Introduction
The testing-skills-with-subagents skill provides a rigorous, process-oriented framework for developers and AI engineers to create, validate, and harden agentic skills. Borrowing directly from the Test-Driven Development (TDD) cycle, this skill treats process documentation as software that must be verified against agent behavior. The core philosophy is that an agent's true compliance can only be proven by observing its failure modes in a controlled environment before the corrective skill is applied.
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Implements the RED-GREEN-REFACTOR cycle specifically for AI prompt engineering and behavioral compliance.
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Facilitates the creation of pressure scenarios—realistic, high-stakes tasks designed to force agents into common anti-patterns like bypassing tests or rationalizing quality shortcuts.
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Provides a structured methodology for identifying and documenting 'rationalization tables'—the verbatim excuses agents use to circumvent rules, which are then used to build airtight logic.
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Designed for complex engineering workflows where enforcing discipline (like TDD, security reviews, or regulatory compliance) is critical and prone to human-like avoidance behaviors.
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Use this skill when developing new instructions, system prompts, or behavioral guidelines that agents might be incentivized to bypass due to time constraints, exhaustion, or perceived pragmatism.
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Requires familiarity with ring:test-driven-development as a prerequisite to ensure consistent application of the iterative cycle.
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Ideal for building high-reliability agent systems where consistent adherence to standards (such as API documentation usage, coding patterns, or compliance protocols) is mandatory.
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Expected output involves the systematic accumulation of 'red flags' and corresponding counter-measures that ensure long-term skill stability against evolving agent behaviors.
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Input requirements include clearly defined behavioral goals and a set of pressure-testing scenarios that emulate real-world production environments.
Repository Stats
- Stars
- 181
- Forks
- 20
- Open Issues
- 7
- Language
- Python
- Default Branch
- main
- Sync Status
- Idle
- Last Synced
- Apr 30, 2026, 10:09 AM