overreliance

What problem does it solve?

LLMs often generate outputs that users treat as ground truth. This skill prevents systems from acting on unverified model results by enforcing explicit gates, disclaimers, and human review when the stakes are high.

Core Features & Use Cases

• Gate on confidence and domain to ensure that risky outputs are routed to humans rather than returned raw.
• Attach a clear "AI-generated — verify before acting" disclaimer to every returned content piece.
• Audit-log all inputs, outputs, and confidence signals to enable full traceability and re-readability of decisions.
• Use cases include code review decisions, UI-facing content, and automated pipelines where LLMS outputs could impact real-world actions.

Quick Start

Ask the AI to validate the current LLM output, apply a confidence gate, and route high-stakes results to human review before returning content.