Export and inspect Arize trace and span data using the ax CLI.
npx skills add https://github.com/ethan-rio/skills --skill arize-trace-ethan-rio
Please help me install this Agent Skill. Skill: arize-trace Source: https://github.com/ethan-rio/skills/tree/main/skills/arize/arize-trace Command: npx skills add https://github.com/ethan-rio/skills --skill arize-trace-ethan-rio
When working with LLM applications, it is difficult to get visibility into runtime execution, especially when debugging errors, unexpected outputs, or behavior regressions. This skill solves that by enabling easy download, export, and inspection of Arize trace and span data to understand exactly how your LLM app is performing.
Use the arize-trace skill to export all error spans from your Arize project for the last 24 hours to debug failing LLM requests.
High-intent search queries and answers about installing and using this skill.
Investigate LLM execution path failures by exporting Arize trace data to inspect span attributes, LLM inputs and outputs, tool call parameters, and error messages. This allows you to review the exact execution path that caused the failure.
Filter Arize trace exports by applying custom SQL-like filters, or by specifying exact trace IDs, span IDs, and session IDs. This targeted export capability ensures you only download the specific LLM runtime data needed.
Guardrails are applied during trace inspection to handle untrusted span content safely. This ensures that downloading and analyzing potentially malicious or malformed LLM execution data does not compromise your debugging environment.
The best way to inspect incorrect LLM chatbot responses is to export the relevant Arize trace and inspect the LLM input messages and tool call parameters. This helps identify exactly where the application logic is breaking down.
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