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Found 12 Skills
Activate when the user asks Claude to talk like a caveman, use caveman mode, say "less tokens please", or invoke "/elastic-caveman". Also activate when the user wants faster, terser responses while still working with Elasticsearch, Kibana, Elastic Security, Elastic Observability, or any part of the Elastic stack. In caveman mode all Elasticsearch-specific technical terms, API names, field names, index patterns, query DSL structures, ESQL syntax, and error messages are preserved verbatim — only filler words and pleasantries are removed. Stop caveman mode when the user says "stop caveman" or "normal mode".
Triage Elastic Security alerts — gather context, classify threats, create cases, and acknowledge. Use when triaging alerts, performing SOC analysis, or investigating detections.
Create and manage Agent Builder agents and custom tools in Kibana. Use when asked to create, update, delete, test, or inspect agents or tools in Agent Builder.
Assess APM service health using SLOs, alerts, ML, throughput, latency, error rate, and dependencies. Use when checking service status, performance, or when the user asks about service health.
Investigate Kubernetes workload, node, and control-plane issues using OTel telemetry (EDOT). Use when diagnosing pod failures (CrashLoopBackOff, OOMKilled, Error), node pressure, resource exhaustion, image pull failures, admission rejections, autoscaling anomalies, or correlating K8s state with application signals. OTel ingest path only — the legacy ECS Kubernetes integration shape is out of scope.
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
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.
Create and manage Kibana Dashboards and Lens visualizations. Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.
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.
Validate code samples in Elastic documentation markdown files. Checks language identifiers, substitution attributes, callout usage, JSON validity, ES|QL syntax, and Painless scripts. Use when reviewing docs PRs, auditing content, or writing new examples.
Investigate cloud-provider (CSP) service issues — AWS today (the reasoning applies to any AWS service; schema and failure signatures are deepest for RDS, Lambda, SQS, ALB, EC2), and in future GCP and Azure — using OTel-schema cloud metrics in Elastic. Use when diagnosing cloud-resource problems, whether alert-triggered or ad hoc: database connection/CPU/memory pressure, function error rates and throttling, queue backlog and consumer lag, load-balancer 5xx, instance saturation. Correlates the resource under investigation against its own baseline, classifies the failure mode, and rules out co-occurring but unrelated conditions.
Query Elastic Context Engine AI Indices, and the Knowledge Indicators (KIs) they store. Use when asked to search, explore, count, or answer questions from an AI Index / KI / Context Engine knowledge index, and when writing the ES|QL that queries one.