Elasticsearch Anomaly Detection

ElasticOfficialCreated by elastic

Skill
Tool integration
Data and AI
Apache-2.0
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From the author

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.

— the description by elastic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured process for creating, configuring, and managing machine learning anomaly detection jobs within Elasticsearch. It emphasizes the correct lifecycle sequence—job creation, datafeed setup, opening the job, and starting the datafeed—to ensure reliable monitoring of time-series data. The skill guides users through selecting appropriate detector functions based on whether they are tracking spikes or drops in event volume, and provides the necessary CLI commands to maintain these jobs.

What it does

Configures and creates ML anomaly detection jobs for time-series data.
Sets up datafeeds to link indices to anomaly detection jobs.
Manages the lifecycle of jobs including opening, starting, closing, and stopping.
Provides guidance on selecting detector functions like high_count or low_count.
Verifies job and datafeed status using statistical commands.
Maps HTTP API operations to the equivalent elastic CLI commands.

When to use it

When you need to detect unusual spikes or drops in event volume.
When setting up new ML anomaly detection jobs on specific indices.
When you need to manage the operational state of existing ML jobs.
When you need to ensure jobs and datafeeds are correctly configured and running.

Advantages

Ensures adherence to the mandatory job-to-datafeed lifecycle order.
Prevents common configuration errors by identifying immutable fields.
Standardizes the use of the elastic CLI for all ML operations.
Provides clear logic for choosing detector directions based on user intent.

Usage examples

“Create an anomaly detection job to monitor for traffic spikes on the web-logs index.”: The agent will list indices to confirm the target, define a high_count detector, create the job, set up the datafeed, and start both, confirming the status via stats.
“How do I stop my anomaly detection job for the server-metrics index?”: The agent will guide you to stop the associated datafeed first, then close the job, ensuring a clean shutdown of the ML process.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/elastic/elasticsearch-anomaly-detection/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip elasticsearch-anomaly-detection.zip -d .agents/skills

Documentation by elastic

Elasticsearch Anomaly Detection

Create, open, and start ML anomaly detection jobs on time-series data. Choose the right count-family detector direction, configure bucket span and time field, wire the datafeed to the correct index, and confirm running state from stats — not from assumptions.

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping, GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API directly.

Prerequisite: ML anomaly detection requires a Platinum-equivalent license on self-managed clusters. Serverless projects include ML. The caller needs manage_ml to create and manage jobs.
Related skill: For interpreting anomaly scores, influencers, and model behavior after a job is running, use elasticsearch-anomaly-detection-explainer — not this skill.

Process

Discover the target index and time field. List candidate indices with GET /_cat/indices (pass a pattern when the user names one). Fetch field types for the chosen index with GET /{index}/_mapping. The decision: confirm the index exists, identify the time field (often @timestamp), and verify document volume is sufficient for baseline learning. Never guess index or field names — they vary across deployments.
Choose detector function and direction. Match the user's intent to a count-family detector in analysis_config.detectors:
- Spike, surge, unusual increase in event volume → high_count (or count, which flags both directions but is acceptable when the user cares about spikes). Do not use low_count — it will miss spikes.
- Drop, outage, absence of events, traffic stops → low_count. Do not use high_count — it will miss drops and silence.
- Metric deviation (CPU, latency, a numeric field) → mean-family functions (mean, high_mean, low_mean) with field_name set — only when the user asks about a numeric metric, not raw event volume.
The decision: pick one primary detector whose direction matches the anomaly type. For volume spike/drop questions on document counts, stay in the count family — mean detectors are unsuited to "how many events" questions.
Set immutable job shape before creation. These fields cannot change after PUT /_ml/anomaly_detectors/{job_id}:
- analysis_config.bucket_span — use the interval the user specifies (e.g. 15m for 15-minute buckets). Match the granularity of anomalies they care about; too short is noisy, too long is slow to detect.
- data_description.time_field — the time field from the mapping (commonly @timestamp).
- analysis_config.detectors — the function and direction from step 2.
Example job body for a volume-spike detector:
```
{
  "analysis_config": {
    "bucket_span": "15m",
    "detectors": [{ "function": "high_count" }]
  },
  "data_description": { "time_field": "@timestamp" }
}
```
Example for an outage / drop detector:
```
{
  "analysis_config": {
    "bucket_span": "15m",
    "detectors": [{ "function": "low_count" }]
  },
  "data_description": { "time_field": "@timestamp" }
}
```
Create the job. Call PUT /_ml/anomaly_detectors/{job_id} with the job id the user requested (or a descriptive id you propose). The job starts in closed state — creating it does not start analysis.
Create the datafeed. Call PUT /_ml/datafeeds/datafeed-{job_id} immediately after job creation. Set job_id to the same id, indices to the target index (exact name or pattern from step 1), and a query that selects the relevant documents (typically match_all). The datafeed id convention is datafeed-{job_id}.
```
{
  "job_id": "{job_id}",
  "indices": ["{index}"],
  "query": { "match_all": {} }
}
```
Open the job, then start the datafeed — in that order. This sequence is mandatory; do not skip or reorder:
1. POST /_ml/anomaly_detectors/{job_id}/_open — transitions the job to opened.
2. POST /_ml/datafeeds/datafeed-{job_id}/_start — transitions the datafeed to started.
Opening before the datafeed exists fails. Starting the datafeed before opening the job fails. Do not report success after only creating resources — the job is not running until both are active.
Confirm running state from stats. Verify the outcome with:
- GET /_ml/anomaly_detectors/{job_id}/_stats — expect state: "opened".
- GET /_ml/datafeeds/datafeed-{job_id}/_stats — expect state: "started".
Optionally call GET /_ml/anomaly_detectors/{job_id} to confirm configuration (detectors, bucket_span, time_field, datafeed indices). Report both stats states explicitly — "created" is not the same as "opened" and "started".

Teardown

When stopping or deleting a job, reverse the startup order:

POST /_ml/datafeeds/datafeed-{job_id}/_stop — stop the datafeed first.
POST /_ml/anomaly_detectors/{job_id}/_close — then close the job.

Stop the datafeed before closing the job. Close the job before resetting or deleting it.

Guidelines

Required lifecycle order (create): job → datafeed → open job → start datafeed. Every new job follows this sequence.
Detector direction is the highest-impact decision for volume anomalies. Re-read the user's wording: "spike", "surge", and "unusual increase" → high direction; "drop", "outage", "stops", "absence" → low direction.
Immutable fields (bucket_span, detectors, time_field) require delete-and-recreate if wrong — validate mapping and intent before the first PUT.
Datafeed index must match the user's target. Point indices at the exact index or pattern they named — not a nearby guess.
Entity-level analysis (by_field_name, over_field_name, partition_field_name) and advanced tuning live in references/anomaly-detection-reference.md.

Full Reference

For API paths, request/response fields, score semantics, and field interactions, read references/anomaly-detection-reference.md.

Operations

HTTP API (shorthand)	`elastic` CLI command
`GET /_cat/indices`	`elastic es cat indices --index '<pattern>'`
`GET /{index}/_mapping`	`elastic es indices get-mapping --index '<index>'`
`PUT /_ml/anomaly_detectors/{job_id}`	`elastic es ml put-job --job-id '<job_id>' --analysis-config '<json>' --data-description '<json>'`
`PUT /_ml/datafeeds/datafeed-{job_id}`	`elastic es ml put-datafeed --datafeed-id 'datafeed-<job_id>' --job-id '<job_id>' --indices '<index>' --query '<json>'`
`POST /_ml/anomaly_detectors/{job_id}/_open`	`elastic es ml open-job --job-id '<job_id>'`
`POST /_ml/datafeeds/datafeed-{job_id}/_start`	`elastic es ml start-datafeed --datafeed-id 'datafeed-<job_id>'`
`GET /_ml/anomaly_detectors/{job_id}`	`elastic es ml get-jobs --job-id '<job_id>'`
`GET /_ml/anomaly_detectors/{job_id}/_stats`	`elastic es ml get-job-stats --job-id '<job_id>'`
`GET /_ml/datafeeds/datafeed-{job_id}/_stats`	`elastic es ml get-datafeed-stats --datafeed-id 'datafeed-<job_id>'`
`POST /_ml/datafeeds/datafeed-{job_id}/_stop`	`elastic es ml stop-datafeed --datafeed-id 'datafeed-<job_id>'`
`POST /_ml/anomaly_detectors/{job_id}/_close`	`elastic es ml close-job --job-id '<job_id>'`

Files

ATTRIBUTION.mdAdded by AutoGineer1016 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md9.4 KiB
references/anomaly-detection-reference.md6.3 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Requires Elasticsearch 8.x+ or Elastic Cloud Serverless with ML anomaly detection. Uses the ML REST API only. User needs manage_ml privilege to create and manage jobs.

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Attribution and licence

Created by

elastic

Publisher

Elastic (Official)

Source

elastic/agent-skills · skills/elasticsearch/elasticsearch-anomaly-detection · commit 09d879d

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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What it does

When to use it

Advantages

Usage examples

Install

Documentation by elastic

Elasticsearch Anomaly Detection

Environment Configuration

Process

Teardown

Guidelines

Full Reference

Operations

Files

LICENSE

Requirements and compatibility

Security

Attribution and licence

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