AI Observability (GenAI) Skill

Analyze AI Observability signals from customer GenAI applications using DQL — golden signals, LLM signals, token and cost analytics (with usage attribution and prompt-caching economics), agent signals (including loop/runaway detection and Smartscape topology), conversation/session-level analytics, guardrails, and evaluation quality.

When to Use

Use this skill for observability questions about customer GenAI applications — anything reading OpenTelemetry GenAI spans (gen_ai.*) or LLM evaluation bizevents. Example triggers:

• "LLM latency", "error rate by model"
• "token usage by model", "token throughput / TPM", "am I hitting rate limits", "provider throttling or 429s"
• "cost by model and provider", "who is driving token spend", "do I have prompt caching"
• "cost per conversation", "most expensive sessions"
• "failing agent tool calls", "find runaway agents"
• "responses truncated or blocked", "finish reasons"
• "failed evaluations", "quality scores"