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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
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.
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Expert process for ML anomaly detection: attribute incidents to entities, explain scores and model behavior, diagnose
job lifecycle failures, and manage jobs. Read anomaly results from POST /.ml-anomalies-*/_search (Serverless-safe)
and job/datafeed state from ML REST APIs. When the user embeds fixture evidence (influencer rows, job stats) in the
prompt, apply the judgment below directly — do not re-fetch fields already supplied.
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.
| User intent | Mode |
|---|---|
| "What broke?" / RCA / cross-job / blast radius / influencers / log categories | Investigate |
| "Why score high/low?" / renormalization / model bounds / forecasts | Explain |
| Missing docs / memory limit / datafeed stopped / lifecycle / calendars | Troubleshoot |
| Create a job / configure a datafeed / start analysis / retrieve results | Manage |
| Security framing (attack chains, MITRE, exfil) | Investigate + references/security-anomaly-expert.md |
| Observability/SRE framing (degradation, capacity, deployment regression) | Investigate + references/observability-anomaly-expert.md |
When a question spans modes: Investigate → Explain → Troubleshoot. Finish one mode before blending logic.
Serverless note: Legacy
/_ml/anomaly_detectors/{job_id}/results/*endpoints return HTTP 410 in Serverless. Always query.ml-anomalies-*viaPOST /.ml-anomalies-*/_searchwithresult_typefilters.
record_score bands: >75 critical · 50–75 warning · 25–50 minor · <25 informationalmulti_bucket_impact ≥ 3 → sustained shift (not a transient spike)initial_record_score >> record_score → renormalization (model saw worse anomalies later)actual << typical with count/low_count/low_mean → absence/outage, not just a low valueFull score definitions, renormalization mechanics, and
anomaly_score_explanationcomponents: references/score-reference.md.
Treat .ml-anomalies-* as layered result types via result_type in search queries:
result_type | Scope | Key fields |
|---|---|---|
bucket | Time window | anomaly_score, initial_anomaly_score, timestamp |
record | Detector row | record_score, initial_record_score, actual, typical, anomaly_score_explanation |
influencer | Entity × bucket | influencer_field_name, influencer_field_value, influencer_score |
model_plot | Bounds | model_lower, model_upper, actual |
category_definition | Log patterns | category_id, terms, regex, examples |
Read scores this way:
anomaly_score / record_score = current normalized values (move as the model sees new extremes).initial_anomaly_score / initial_record_score = immutable snapshots from detection time.influencer_score ranks entity responsibility within a bucket — the highest score is the primary suspect, not the
bucket-level anomaly_score alone.partition_field_value / by_field_value / over_field_value.multi_bucket_impact (-5 to +5) to separate single-bucket spikes from sustained trends.When: "what broke?", "which entity caused this?", cross-job correlation, blast radius, attack/cascade chains.
Discover jobs. Call GET /_ml/anomaly_detectors when the job ID is unknown. Call
GET /_ml/anomaly_detectors/{job_id} and GET /_ml/datafeeds/datafeed-{job_id} to learn source indices, entity
fields (by_field_name, over_field_name, partition_field_name), and bucket_span. The decision: identify the
related job group — jobs sharing a datafeed index or entity field monitor the same system from different angles.
Scope the incident window. Call POST /.ml-anomalies-*/_search with result_type: bucket, a time range, and
optional minimum anomaly_score. The decision: fix the incident start/end and count how many jobs co-fire in that
window. Low scores across many jobs simultaneously often indicate a systemic root cause.
Attribute to entities (critical for RCA). For the anomalous bucket timestamp, call
POST /.ml-anomalies-*/_search with result_type: influencer, the job ID(s), and the bucket time range. Sort by
influencer_score descending. The decision: name the entity with the highest influencer_score as the
likely cause — it ranks how unusual each entity is in that bucket. Do not restate only the bucket anomaly_score
without attributing responsibility. Recommend drilling into that entity's records next.
Cross-job confirmation. Re-query influencers (or bucket records) across related job IDs for the same entity values and time window. Entities anomalous in 2+ jobs are prime suspects (resource fault or systemic failure); single-job entities are often downstream victims. See references/protocols/investigation.md.
Drill into records. Call POST /.ml-anomalies-*/_search with result_type: record, exact job ID, entity filters
(partition_field_value, by_field_value), and low minimum record_score (25 or lower). Read
multi_bucket_impact ≥ 3 as sustained behavioral shift. Read actual vs typical for fault class (spike vs
absence/outage).
Confirm with source evidence. Call on the datafeed source index for the suspect entity and time window. Raw source documents are ground truth — never close an RCA without them.
influencer_score, not anomaly_score, for "which entity?" — bucket score is aggregate; influencer
score attributes cause.multi_bucket_impact ≥ 3 = sustained behavioral shift, weight higher than transient spikes.When: "why is my score 30/90?", "score dropped overnight", "what is renormalization?", "why wasn't this detected?".
Decide fetch vs interpret. If the user supplies a record with record_score, initial_record_score, actual,
and typical, interpret directly. Otherwise load config with GET /_ml/anomaly_detectors/{job_id} and records with
POST /.ml-anomalies-*/_search (result_type: record).
Always show both initial_record_score and record_score. The gap is the renormalization story. Large positive
drift (initial_record_score >> record_score) means a later, more extreme anomaly rescale this record downward —
expected healthy behavior, not a broken model.
Classify the pattern before speculating.
| Pattern | Interpretation |
|---|---|
initial_record_score >> record_score | Renormalization — explain before suggesting config changes |
actual << typical with low_count/count/low_mean | Absence/outage anomaly — investigate the outage, not score tuning |
high_variance_penalty: true in anomaly_score_explanation | Noisy metric — wide bounds absorbed the spike |
incomplete_bucket_penalty: true | Ingest lag or sparse bucket — score legitimately reduced |
Only cite anomaly_score_explanation factors present in the record.
Quantify renormalization (optional). Re-query records sorted by timestamp; compute
score_drift = initial_record_score - record_score and flag large drift.
Add visual context when needed. If model_plot_config.enabled, query result_type: model_plot and compare
actual to model_lower/model_upper. For categorization jobs, query result_type: category_definition.
Check job health when scores look wrong persistently. Call GET /_ml/anomaly_detectors/{job_id}/_stats —
model_size_stats.memory_status of corrupts learning and can invalidate scores. Escalate to
Troubleshoot mode.
anomaly_score_explanation components| Component | Effect | What it means |
|---|---|---|
anomaly_length | ↑ score |
name: kibana-anomaly-detection description: > 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. metadata: author: elastic version: 0.3.0 universal: true compatibility: Elasticsearch 8.x–9.x or Elastic Cloud Serverless with ML anomaly detection; Kibana 8.x–9.x for saved-object context only
---
name: kibana-anomaly-detection
description: >
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.
metadata:
author: elastic
version: 0.3.0
universal: true
compatibility: Elasticsearch 8.x–9.x or Elastic Cloud Serverless with ML anomaly detection;
Kibana 8.x–9.x for saved-object context only
---
# Elastic ML Anomaly Detection
Expert process for ML anomaly detection: attribute incidents to entities, explain scores and model behavior, diagnose
job lifecycle failures, and manage jobs. Read anomaly **results** from `POST /.ml-anomalies-*/_search` (Serverless-safe)
and **job/datafeed state** from ML REST APIs. When the user embeds fixture evidence (influencer rows, job stats) in the
prompt, apply the judgment below directly — do not re-fetch fields already supplied.
<!-- begin-partial: preamble -->
## Environment Configuration
This skill executes Elasticsearch operations through the `elastic` CLI. If the
[`elastic` CLI](https://github.com/elastic/cli#configuration) 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](#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.
<!-- end-partial: preamble -->
## Mode selector
| User intent | Mode |
| ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| "What broke?" / RCA / cross-job / blast radius / influencers / log categories | **Investigate** |
| "Why score high/low?" / renormalization / model bounds / forecasts | **Explain** |
| Missing docs / memory limit / datafeed stopped / lifecycle / calendars | **Troubleshoot** |
| Create a job / configure a datafeed / start analysis / retrieve results | **Manage** |
| Security framing (attack chains, MITRE, exfil) | Investigate + [references/security-anomaly-expert.md](references/security-anomaly-expert.md) |
| Observability/SRE framing (degradation, capacity, deployment regression) | Investigate + [references/observability-anomaly-expert.md](references/observability-anomaly-expert.md) |
When a question spans modes: **Investigate → Explain → Troubleshoot**. Finish one mode before blending logic.
> **Serverless note:** Legacy `/_ml/anomaly_detectors/{job_id}/results/*` endpoints return HTTP 410 in Serverless.
> Always query `.ml-anomalies-*` via `POST /.ml-anomalies-*/_search` with `result_type` filters.
## Score quick reference
- `record_score` bands: **>75** critical · **50–75** warning · **25–50** minor · **<25** informational
- `multi_bucket_impact ≥ 3` → sustained shift (not a transient spike)
- `initial_record_score >> record_score` → renormalization (model saw worse anomalies later)
- `actual << typical` with `count`/`low_count`/`low_mean` → absence/outage, not just a low value
- Low scores across many jobs > one high score — composite cross-job signal often beats single-detector severity
> Full score definitions, renormalization mechanics, and `anomaly_score_explanation` components:
> [references/score-reference.md](references/score-reference.md).
## Core concepts
Treat `.ml-anomalies-*` as layered result types via `result_type` in search queries:
| `result_type` | Scope | Key fields |
| --------------------- | --------------- | ---------------------------------------------------------------------------------------- |
| `bucket` | Time window | `anomaly_score`, `initial_anomaly_score`, `timestamp` |
| `record` | Detector row | `record_score`, `initial_record_score`, `actual`, `typical`, `anomaly_score_explanation` |
| `influencer` | Entity × bucket | `influencer_field_name`, `influencer_field_value`, **`influencer_score`** |
| `model_plot` | Bounds | `model_lower`, `model_upper`, `actual` |
| `category_definition` | Log patterns | `category_id`, `terms`, `regex`, `examples` |
Read scores this way:
- `anomaly_score` / `record_score` = **current normalized** values (move as the model sees new extremes).
- `initial_anomaly_score` / `initial_record_score` = **immutable snapshots** from detection time.
- **`influencer_score` ranks entity responsibility within a bucket** — the highest score is the primary suspect, not the
bucket-level `anomaly_score` alone.
- Map entities via `partition_field_value` / `by_field_value` / `over_field_value`.
- Read `multi_bucket_impact` (-5 to +5) to separate single-bucket spikes from sustained trends.
---
## Mode: Investigate — RCA
**When:** "what broke?", "which entity caused this?", cross-job correlation, blast radius, attack/cascade chains.
### Process
1. **Discover jobs.** Call `GET /_ml/anomaly_detectors` when the job ID is unknown. Call
`GET /_ml/anomaly_detectors/{job_id}` and `GET /_ml/datafeeds/datafeed-{job_id}` to learn source indices, entity
fields (`by_field_name`, `over_field_name`, `partition_field_name`), and `bucket_span`. The decision: identify the
related job group — jobs sharing a datafeed index or entity field monitor the same system from different angles.
2. **Scope the incident window.** Call `POST /.ml-anomalies-*/_search` with `result_type: bucket`, a time range, and
optional minimum `anomaly_score`. The decision: fix the incident start/end and count how many jobs co-fire in that
window. Low scores across many jobs simultaneously often indicate a systemic root cause.
3. **Attribute to entities (critical for RCA).** For the anomalous bucket timestamp, call
`POST /.ml-anomalies-*/_search` with `result_type: influencer`, the job ID(s), and the bucket time range. Sort by
**`influencer_score` descending**. The decision: name the entity with the **highest `influencer_score`** as the
likely cause — it ranks how unusual each entity is in that bucket. Do not restate only the bucket `anomaly_score`
without attributing responsibility. Recommend drilling into that entity's records next.
4. **Cross-job confirmation.** Re-query influencers (or bucket records) across related job IDs for the same entity
values and time window. Entities anomalous in **2+ jobs** are prime suspects (resource fault or systemic failure);
single-job entities are often downstream victims. See
[references/protocols/investigation.md](references/protocols/investigation.md).
5. **Drill into records.** Call `POST /.ml-anomalies-*/_search` with `result_type: record`, exact job ID, entity filters
(`partition_field_value`, `by_field_value`), and low minimum `record_score` (25 or lower). Read
`multi_bucket_impact ≥ 3` as sustained behavioral shift. Read `actual` vs `typical` for fault class (spike vs
absence/outage).
6. **Confirm with source evidence.** Call `POST /{index}/_search` on the datafeed source index for the suspect entity
and time window. Raw source documents are ground truth — never close an RCA without them.
7. **Synthesize.** Report: **root cause entity · affected jobs · temporal progression · fault class · severity ·
recommended actions**. Worked walkthrough: [references/worked-example.md](references/worked-example.md). Query
templates: [references/investigation-queries.md](references/investigation-queries.md).
### Rules
1. **Rank by `influencer_score`, not `anomaly_score`, for "which entity?"** — bucket score is aggregate; influencer
score attributes cause.
2. **Multi-job entities are prime suspects; single-job entities are usually victims.**
3. **Earliest anomaly timestamp wins** — reconstruct chronology from record timestamps across jobs.
4. **`multi_bucket_impact ≥ 3` = sustained behavioral shift**, weight higher than transient spikes.
5. **Use low score thresholds (25 or lower) for influencer/record queries** — high thresholds miss correlated entities.
6. **Never close an RCA without source evidence** from the datafeed index.
---
## Mode: Explain — Score / model behavior
**When:** "why is my score 30/90?", "score dropped overnight", "what is renormalization?", "why wasn't this detected?".
### Process
1. **Decide fetch vs interpret.** If the user supplies a record with `record_score`, `initial_record_score`, `actual`,
and `typical`, interpret directly. Otherwise load config with `GET /_ml/anomaly_detectors/{job_id}` and records with
`POST /.ml-anomalies-*/_search` (`result_type: record`).
2. **Always show both `initial_record_score` and `record_score`.** The gap is the renormalization story. Large positive
drift (`initial_record_score >> record_score`) means a later, more extreme anomaly rescale this record downward —
expected healthy behavior, not a broken model.
3. **Classify the pattern before speculating.**
| Pattern | Interpretation |
| ------------------------------------------------------------ | ----------------------------------------------------------------- |
| `initial_record_score >> record_score` | Renormalization — explain before suggesting config changes |
| `actual << typical` with `low_count`/`count`/`low_mean` | Absence/outage anomaly — investigate the outage, not score tuning |
| `high_variance_penalty: true` in `anomaly_score_explanation` | Noisy metric — wide bounds absorbed the spike |
| `incomplete_bucket_penalty: true` | Ingest lag or sparse bucket — score legitimately reduced |
Only cite `anomaly_score_explanation` factors **present** in the record.
4. **Quantify renormalization (optional).** Re-query records sorted by `timestamp`; compute
`score_drift = initial_record_score - record_score` and flag large drift.
5. **Add visual context when needed.** If `model_plot_config.enabled`, query `result_type: model_plot` and compare
`actual` to `model_lower`/`model_upper`. For categorization jobs, query `result_type: category_definition`.
6. **Check job health when scores look wrong persistently.** Call `GET /_ml/anomaly_detectors/{job_id}/_stats` —
`model_size_stats.memory_status` of `hard_limit` corrupts learning and can invalidate scores. Escalate to
Troubleshoot mode.
### `anomaly_score_explanation` components
| Component | Effect | What it means |
| -------------------------------- | ------- | ------------------------------------------------------------ |
| `anomaly_length` | ↑ score | Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
75/100
Strong
Trust
67/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"Permission surface needs review: secrets or environment access, shell or command execution",
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"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 81,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 75,
"label": "Strong"
},
"supply": {
"track": "Data, BI, and analytics",
"scenario": "Research agents",
"maintenance": "14d since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
],
"agent_contract": {
"task_input": "Use kibana-anomaly-detection in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 75/100 Strong shortlist",
"Audit: 81/100 Needs review",
"Safety: 33/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "elastic-kibana-anomaly-detection (kibana-anomaly-detection)",
"install_command": "npx skills add elastic/agent-skills --skill kibana-anomaly-detection",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "elastic-kibana-anomaly-detection",
"task": "Use kibana-anomaly-detection in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/elastic-kibana-anomaly-detection",
"api": "https://www.openagentskill.com/api/agent/skills/elastic-kibana-anomaly-detection",
"audit": "https://www.openagentskill.com/skills/elastic-kibana-anomaly-detection/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=elastic-kibana-anomaly-detection&task=Use%20kibana-anomaly-detection%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20kibana-anomaly-detection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20kibana-anomaly-detection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/elastic-kibana-anomaly-detection/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/elastic-kibana-anomaly-detection"
}
}Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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[](https://www.openagentskill.com/skills/elastic-kibana-anomaly-detection/audit)
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POST /{index}/_searchSynthesize. Report: root cause entity · affected jobs · temporal progression · fault class · severity · recommended actions. Worked walkthrough: references/worked-example.md. Query templates: references/investigation-queries.md.
hard_limitAudit
81/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.