Trace Review & Dataset Promotion

This skill streamlines the post-evaluation workflow for AI models by providing a structured environment for judging flagged trace outputs. It bridges the gap between raw execution logs and high-quality datasets through an interactive Q&A protocol that supports binary, categorical, and continuous scoring. By allowing developers to perform deep error analysis, triage pending review queues, and record judgment rationale, this skill ensures that only validated outputs are promoted back into datasets for future testing, fine-tuning, or benchmarking.

Key Features

Use Cases