数据与研究

observability-llm-obs

elastic/agent-skills

Answer questions about LLM and agentic-application behavior from data already ingested into Elastic: latency and error rate, token and cost utilization, response quality and guardrail events, and agentic call-chain orchestration. Use when the user asks about LLM monitoring, GenAI observability, token spend or AI cost, model latency, prompt or guardrail failures, or how an agent's tool-call chain executed.

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安装命令

npx skills add https://github.com/elastic/agent-skills --skill observability-llm-obs

SKILL.md

LLM and Agentic Observability

Answer questions about monitoring LLMs and agentic components using data actually ingested into Elastic — nothing else. The four questions this skill answers are LLM performance, cost and token utilization, response quality, and call chaining or agentic workflow orchestration.

A given deployment typically uses one or more ingestion paths: APM/OTLP traces, and/or integration metrics and logs. Which one exists is a discovery result, not an assumption — never assume both are present. For ES|QL syntax, commands, and query patterns, use the elasticsearch-esql skill. For service-level latency and error triage that is not LLM-specific, use the observability-sre-triage skill.

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.