Skill UI

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This skill performs multi-baseline counterfactual cost analysis for AI model routing. It compares actual session spending against hypothetical scenarios using single models like Haiku, Sonnet, or Opus. It calculates savings percentages to determine if routing decisions are cost-effective. Negative savings indicate over-escalation, while positive savings show router efficiency. Useful for quarterly reviews, CI gates, and validating routing configurations.

Monitor AI execution sessions and measure deviation from initial goals. Provides status updates and drift analysis to ensure alignment with the Seed prompt. Suggests corrective actions like interviews or evolution when drift exceeds thresholds.

This tool guides the analysis of work sessions by detecting user feedback (corrections, successes, edge cases). It evaluates these signals against quality criteria (Complete, Precise, Atomic, Stable) to propose structured improvements, update skill documentation (SKILL.md), and maintain a changelog.