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Found 5 Skills
Create and manage SLOs in Elastic Observability using the Kibana API. Use when defining SLIs, setting error budgets, or managing SLO lifecycle.
Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
Design and operate service reliability targets in Elastic Observability: choose an SLI type and a defensible target, pick a time window and budgeting method, create and maintain SLOs through the Kibana API, attach burn-rate alert rules, and decide when an SLO is the wrong instrument and a threshold rule, anomaly job, or synthetics monitor is right. Use when defining or reviewing SLOs and error budgets, tuning burn-rate alerting, reducing alert noise, or setting up availability monitoring for a user-facing endpoint.
Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
Onboard an application into Elastic Observability with the Elastic Distribution of OpenTelemetry (EDOT): route on language and runtime, detect and replace a classic Elastic APM agent, apply the required OTLP configuration, and then verify with ES|QL that traces, metrics, and logs actually arrive under the expected service name. Use when adding observability to a service, migrating off the classic Elastic APM agent, or debugging why an instrumented service is not showing up in Elastic.