SKILL.md
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"