dt-obs-genai

# 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. --- ## Core Capabilities ### Golden Signals The four classic observability signals — traffic, errors, latency, and saturation — apply directly to GenAI applications. Traffic is request throughput over time; errors are spans where `span.status_code == "error"`; latency is the `duration` field (a Grail duration value — divide by the `1ms` literal, `duration / 1ms`, for a numeric millisecond value); saturation is proxied by total token throughput per minute (input + output tokens combined). ```dql fetch spans, from: now()-24h | filter isNotNull(gen_ai.request.model) | summarize total = count(), errors = countIf(span.status_code == "error"), by: {gen_ai.request.model} | fieldsAdd error_rate_pct = if(total > 0, errors * 100.0 / total, else: 0.0) | sort error_rate_pct desc ``` → **Full traffic, latency, and saturation queries:** See [references/golden-signals.md](references/golden-signals.md) ### LLM Signals LLM signals describe which model and provider served each request, what operation type was invoked (`chat`, `execute_tool`, `invoke_agent`, `create_agent`), and how tokens were consumed. Use these to benchma...