elastic

Standalone quality review for Elastic integrations. Classifies files by domain, loads domain-specific skills and review checklists, applies cross-domain consistency rules, CEL version verification, API conformance, and severity calibration. Input-agnostic: works on local packages, PR diffs, or branch comparisons. Use when reviewing integration quality independently of any build or fix workflow.

Research a vendor, product, or feature to collect all information needed before building an Elastic integration. Investigates data collection methods, API or log documentation, sample data formats, field schemas, ECS mapping candidates, and configuration requirements. Outputs a structured research brief to research_results/<product>/. Invoke manually with /research-integration.

Package specification compliance for Elastic integration packages. Covers manifest structure (format_version, conditions, variables, var_groups, provider_permissions, routing rules), changelog schema and semantic version bumps, and alignment with the upstream elastic/package-spec. Use when building or reviewing manifest.yml, changelog.yml, or debugging elastic-package lint/check errors on package metadata.

Use when reviewing, fixing, or improving an EXISTING Elastic integration package. Covers quality reviews, targeted fixes (pipelines, field mappings, CEL programs, manifests, changelogs), full improvement passes, and minor adjustments. Use create-integration instead when creating a new package or adding a new data stream from scratch.

Use when creating, running, or debugging elastic-package tests — pipeline fixture authoring and expected output, system tests with mock API wiring, and script tests for failure paths and upgrades. Load the reference file for the test type you are working on.

Input template configuration for Elastic integrations. Covers agent stream templates (agent/stream/*.yml.hbs) for all non-CEL input types: HTTPJSON, AWS S3, CloudWatch, Azure Blob, Azure EventHub, GCS, GCP Pub/Sub, TCP, UDP, HTTP Endpoint, Filestream, Logfile, Journald, Winlog, and WebSocket. Also covers Federated Identity (Cloud Connectors) for agentless AWS integrations, including the auth.aws / use_cloud_connectors block on CEL stream templates. For CEL program logic, use the cel-programs skill.

Use when designing or modifying Elasticsearch ingest pipelines, including single-path parsing, branching logic, sub-pipelines, enrichment processors, and robust on_failure handling.

Use when adding or auditing ECS entity.* fields (entity inventory / entity store) on Elastic integrations — classifying a data stream as entity vs event, mapping vendor fields to entity.attributes.*, entity.lifecycle.*, and entity.relationships.*, or running an entity-coverage gap analysis on an existing package. Invoke manually with /entity-mappings.

Use when developing or validating Elastic integrations with elastic-package commands such as build, check, lint, format, test, stack, service, install, profiles, and benchmark.

Use when defining field mappings for data streams, populating ecs.yml with ECS field references, selecting ECS categorization values, choosing custom field types, or troubleshooting mapping validation failures.

Use when reviewing dashboard JSON changes in a PR or branch. Extracts structured descriptions with kbdash, compares before/after, and checks guideline compliance.

Use when creating or reviewing Kibana assets in packages, including dashboard export structure, naming, and data stream alignment.

Use when creating a new Elastic integration package, scaffolding data streams, answering package layout or structure questions, or running the end-to-end integration build workflow. Covers package topology, scaffold commands, post-scaffold edits, and full orchestration of CEL/pipeline/test subagents.

Use for all CEL and mito work on integrations that collect from APIs — writing CEL programs, cel.yml.hbs templates, manifest configuration, mock-first development with the mito CLI, system test mock setup, and answering CEL/mito questions. Load this skill whenever any data stream uses the cel input type.

Anonymize and sanitize customer-provided log files. Performs a line-by-line review and replaces all sensitive values inline, preserving log structure and format exactly — never reformats, re-indents, or restructures content. Use when sanitizing customer logs, sample events, or test fixtures. Invoke manually with /anonymize-logs.

Generate sample security events, attack scenarios, and synthetic alerts for Elastic Security. Use when demoing, populating dashboards, testing detection rules, or setting up a POC.

Create, tune, and manage Elastic Security detection rules (SIEM and Endpoint). Use for false positives, exceptions, new coverage, noisy rules, or rule management via Kibana API.

Create, search, update, and manage SOC cases via the Kibana Cases API. Use when tracking incidents, linking alerts to cases, adding investigation notes, or managing triage output.

Triage Elastic Security alerts — gather context, classify threats, create cases, and acknowledge. Use when triaging alerts, performing SOC analysis, or investigating detections.

Triage a degraded or suspect service end to end: read SLO status and burn rate, check active alerting rules and ML anomalies, measure throughput, latency, and error rate, assess dependency health and infrastructure saturation, and funnel logs down to the failures that explain it. Use when someone asks whether a service is healthy, why it is slow or erroring, what is in its logs, or which attribute distinguishes the requests that are failing. Also use when someone asks for the query behind any of those signals — throughput, latency percentiles, error rate, dependency health, or log volume — over APM/OTel traces, metrics, or logs.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Guide Elasticsearch reindex for performance: local and remote, slicing, throttling, task API. Use when copying or migrating indices, changing mappings, or transforming during reindex.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.