explainability

What problem does it solve?

This Skill addresses the need for transparency and auditability in AI decision-making, ensuring that critical choices can be understood, debugged, and trusted.

Core Features & Use Cases

• Rationale Generation: Automatically appends a concise explanation to key AI decisions.
• Evidence Citation: Links decisions back to the specific data or logic that informed them.
• Use Case: When an AI rejects a code merge request, this Skill ensures the rejection includes a clear reason, such as "Confidence below threshold; missing rollback plan," along with references to the evidence that led to this conclusion.

Quick Start

Use the explainability skill to add a rationale to the AI's decision to reject the current code review.