elastic-agent-skills

Elastic Agent Skills

Elastic Agent Skills — built by the people who built Elastic — deliver native platform expertise directly to your AI coding agent. This is the official Agent Skills library, compatible with agentic IDEs such as Cursor, GitHub Copilot, Windsurf, Gemini CLI, and more. Skills follow the Agent Skills open standard.

[!NOTE] Technical Preview

These skills are in early release and under active development. Expect changes as skills are codified with robust evaluations and as the model landscape evolves. Check back frequently for updates.

About

This repository contains curated skills that are packages of instructions, context, and tooling that teach any AI agent how to correctly work with Elasticsearch, Kibana, Elastic Observability, and Elastic Security. Drop them into the agent runtime you already use, and your assistant stops using outdated patterns and starts getting it right.

What are skills?

Skills are self-contained packages that give AI agents the knowledge and tools to complete specific tasks in a repeatable way. Each skill lives in its own folder with a SKILL.md file containing metadata and instructions the agent follows.

For more background on the Agent Skills standard, see agentskills.io.

Scope

Skills in this repository focus on:

• Interacting with Elasticsearch APIs (search, indexing, cluster management)
• Building and managing Kibana content such as alerts, connectors, and more
• Patterns for Elastic Observability, Elastic Security, and Agent Builder

Available Skills

Skill	Description	Version	Author
cloud-onboarding	Onboard an Elastic Cloud organization: configure the elastic CLI's Cloud context and API key, establish a default region, then invite users, assign predefined or custom Serverless project roles, and create or revoke Cloud API keys. Use when setting up Cloud authentication or when granting, modifying, or auditing user access to an organization and its projects.	0.3.0	elastic
cloud-provisioning	Provision and operate Elastic Cloud infrastructure: create, connect to, update, and delete Serverless projects (Elasticsearch, Observability, Security); manage traffic filters (IP and AWS PrivateLink network security); and manage the lifecycle of Elastic Cloud Hosted deployments. Use when creating or performing day-2 operations on serverless projects or hosted deployments, or restricting their network access.	0.3.0	elastic

Skill	Description	Version	Author
elasticsearch-anomaly-detection	Create and manage Elastic ML anomaly detection jobs via the API. Use when setting up jobs on an index or data stream, configuring jobs and datafeeds, or opening, starting, or stopping them.	1.1.0	elastic
elasticsearch-anomaly-detection-explainer	Explain Elasticsearch ML anomaly detection scores, model behavior, and result interpretation. Use when the user asks why a score is high or low, how the model learns, what the numbers mean, or how to troubleshoot unexpected anomaly scores.	0.3.0	elastic
elasticsearch-cluster-health	Diagnose a non-green Elasticsearch cluster and surface the single most likely cause with remediation. Use when an operator reports yellow or red status, unassigned shards, allocation failures, or wants read-only triage before deeper investigation. Teaches replica-vs-primary impact, allocation decider classification, and data-loss awareness.	0.1.0	elastic
elasticsearch-esql	Execute ES|QL (Elasticsearch Query Language) queries, use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ES|QL results.	0.7.0	elastic
elasticsearch-index-design	Design and review Elasticsearch index mappings for stated access patterns: correct field types, text+keyword multi-fields, doc_values tuning, mapping-explosion avoidance, and explicit shard settings. Use when creating a new index, reviewing a mapping for storage or query performance, fixing wrong field types, or when the user asks which type to use for search, filter, sort, or aggregation on a field.	0.1.0	elastic
elasticsearch-ingest	Load CSV and JSON files into Elasticsearch indices using the bulk API and explicit mappings when field types matter. Use when batch-importing local files, converting CSV rows or JSON arrays to NDJSON bulk format, or verifying document counts and mappings after ingest — not for Logstash pipelines, Beats, custom scripts, or index-to-index reindex.	0.1.0	elastic
elasticsearch-onboarding	Help developers new to Elasticsearch get from zero to a working search experience. Guide them through understanding their intent, mapping their data, and building a search experience with best practices baked in. Use this when the user shows intent to build search-related functionality, asks about Elasticsearch-related concepts for their use case, or expresses the need for help getting started with Elasticsearch.	0.1.0	elastic
elasticsearch-query-optimization	Diagnose slow Elasticsearch Query DSL searches and propose measured fixes. Use when a search is slow, profile output shows an expensive clause, exact-match filters sit in scoring context, or leading wildcards dominate latency. Ground every recommendation in search profiling — move non-scoring clauses to filter context, eliminate leading wildcards, and re-profile to confirm improvement.	0.1.0	elastic
elasticsearch-reindex	Guide Elasticsearch reindex for performance: local and remote, slicing, throttling, task API. Use when copying or migrating indices, changing mappings, or transforming during reindex.	0.2.0	elastic
elasticsearch-search-relevance	Improve Elasticsearch search relevance for content and catalog indices: pin or promote results with query rules (correct rule type, criteria, and rule-query wiring) and tune organic ranking with multi_match, field boosts, and analysis grounded in the index mapping. Use when search results rank poorly, a specific document must appear first for a query, or the user asks to tune full-text matching — not for ES|QL analytics, index ingest, or cluster health.	0.1.0	elastic

Skill	Description	Version	Author
kibana-agent-builder	Create and manage Kibana Agent Builder agents and custom tools. Use when asked to create, update, delete, test, or inspect agents or tools in Agent Builder, or when the user wants to understand what agents or tools already exist.	0.3.0	elastic
kibana-alerting-rules	Create and manage Kibana alerting rules. Use when creating, updating, or managing rule lifecycle (enable, disable, mute, snooze), choosing metric threshold rule types and params, or read-only find/list with tag filters.	0.3.0	elastic
kibana-anomaly-detection	Elastic ML anomaly detection — investigation/RCA, score explanation, job lifecycle troubleshooting, and job operations. Use when answering "what broke?"/"which entity?"/RCA, "why is score high/low?"/renormalization, "datafeed stopped"/"memory limit"/hard_limit, or configuring ML anomaly detection jobs. Reads results from .ml-anomalies-* and job state from ML REST APIs.	0.3.0	elastic
kibana-dashboards	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.	0.3.0	elastic
kibana-workflows	Author, validate, test, run, and inspect Elastic Workflow YAML definitions. Use when the user wants to turn natural language into a Kibana workflow, fix workflow YAML, understand triggers or steps, or run a quick test loop against a real Kibana.	0.5.0	elastic

Skill	Description	Version	Author
observability-k8s-investigation	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.	0.5.1	elastic
observability-llm-obs	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.	0.3.1	elastic
observability-onboarding	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.	0.3.0	elastic
observability-service-reliability	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.	0.4.1	elastic