By owl-listener
Design ethical guardrails and safety boundaries for AI products by creating bias mitigation workflows, escalation strategies, transparency patterns, harm risk matrices, and value-aligned rules. Generate complete guardrail specs, run red-teaming exercises to expose failures, and draft behavior policies.
npx claudepluginhub owl-listener/ai-design-skills --plugin ai-alignment-reasoningCreate a complete guardrail specification for an AI feature.
Run a structured red-teaming exercise to find alignment failures.
Draft an AI behavior policy covering safety, tone, and boundaries.
Designing review workflows to surface and mitigate bias in AI outputs.
Designing for informed user consent, opt-out, and human override.
When and how AI should escalate to humans, refuse, or ask for clarification.
Defining behavioral boundaries — what the AI should and shouldn't do.
Proactively identifying failure modes, misuse, and unintended consequences.
Showing users what the AI knows, doesn't know, and how confident it is.
Translating organisational values and user expectations into system constraints.
Share bugs, ideas, or general feedback.
Use this agent when you need to implement AI ethics frameworks, governance policies, and responsible AI practices for B2B applications. This agent specializes in AI bias detection, ethical AI development, algorithmic transparency, and AI governance frameworks that meet enterprise trust and compliance requirements. Examples:
Design AI-first interfaces that build ongoing relationships through memory, trust evolution, and collaborative planning.
AI ethics and fairness validation
Use this agent when you need to implement AI ethics frameworks, governance policies, and responsible AI practices for B2B applications. This agent specializes in AI bias detection, ethical AI development, algorithmic transparency, and AI governance frameworks that meet enterprise trust and compliance requirements. Examples:
Agent capability frameworks: STAR framework for understanding implicit intent (framework-initiative), critical thinking architecture with reasoning selection (framework-critical-thinking), and adversarial review protocol with mandatory bug quota (adversarial-review). Build smarter, self-correcting agents and validate plans before execution.