Elastic skills

Manage Serverless network security (traffic filters): create, update, and delete IP filters and AWS PrivateLink VPC filters. Use when restricting network access or configuring private connectivity.

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.

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.

Prioritize and rank the Elastic Security alert queue by weighted risk — base risk score, MITRE tactic boost, entity risk, and asset criticality — then cluster alerts into entity groups. Use when an analyst asks what to focus on, which alerts are most urgent, or wants a ranked starting point across the queue before deeper investigation begins.

Instrument a .NET application with the Elastic Distribution of OpenTelemetry (EDOT) .NET SDK for automatic tracing, metrics, and logs. Use when adding observability to a .NET service that has no existing APM agent.

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.

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.

Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms. Use when loading files or batch importing data — not for reindexing, general ingest pipeline design, or bulk API patterns.

Creates Elastic Cloud Serverless projects (Elasticsearch, Observability, or Security) via the REST API, saves credentials to file, and bootstraps a scoped Elasticsearch API key. Use when creating a new serverless project, provisioning a search or observability environment, or spinning up a new Elastic Cloud project.

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.

Migrate a .NET application from the classic Elastic APM .NET agent to the EDOT .NET SDK. Use when switching from Elastic.Apm.* packages to Elastic.OpenTelemetry.

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.

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.

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.

Enable, configure, and query Elasticsearch security audit logs. Use when the task involves audit logging setup, event filtering, or investigating security incidents like failed logins.

Enable and configure Kibana audit logging for saved object access, logins, and space operations. Use when setting up Kibana audit, filtering events, or correlating Kibana and ES audit logs.

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.

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.

Create and manage SLOs in Elastic Observability using the Kibana API. Use when defining SLIs, setting error budgets, or managing SLO lifecycle.

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.

Diagnose and resolve Elasticsearch security errors: 401/403 failures, TLS problems, expired API keys, role mapping mismatches, and Kibana login issues. Use when the user reports a security error.

List, inspect, enable, disable, and resync Kibana Streams via the REST API. Use when the user needs stream details, ingest settings, query-stream settings, or linked attachments (dashboards, rules, SLOs).

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.

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.