result-normalizer

What problem does it solve?

This Skill streamlines the process of converting raw worker outputs and watcher reports into concise, evidence-first packets suitable for decision-making.

Core Features & Use Cases

• Claims Preservation: Maintains claims connected to concrete evidence and notes unsupported or rejected claims.
• Noise Reduction: Removes or downgrades reassurance, duplicate logs, and inaccessible data to clarify the verification status.
• Use Case: Facilitates quality control in AI workflows by ensuring only valid, evidence-backed information influences decisions, especially during human review or final approval stages.

Quick Start

Provide worker outputs and watcher reports to the result-normalizer for efficient evidence synthesis and verification.