Skill UI

Phoenix CLI streamlines debugging Phoenix-based LLM observability: it fetches traces, spans, sessions, datasets, experiments, prompts, and GraphQL data while piping JSON to tools like jq so you can triage errors, inspect performance, and verify deployments quickly.

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.