kibana-anomaly-detection

Elastic ML Anomaly Detection

Single skill covering all anomaly detection work against Kibana Agent Builder MCP at {KIBANA_URL}/api/agent_builder/mcp. Use the Mode Selector below to pick the right approach for the user's question — modes share the same tool surface and concepts.

Platform

• Read path: ES|QL against .ml-anomalies-, .ml-config, .ml-notifications-, .ml-annotations-*
• Always-available: platform.core.execute_esql (plus additional platform tools for search, index mapping, and

documentation — see scripts/agent_builder_constants.json)

• ML API spec (if available): .kibana_ai_openapi_spec_elasticsearch — see

references/anomaly-detection-openapi-spec-discover.md for discovery pattern.

• Run ad_validate_ml_tool_permissions first when tools return empty/misleading results — missing privileges are

the most common cause of false negatives. Full permissions matrix: references/permissions-matrix.md.

Mode Selector

When a question spans modes: Investigate → Explain → Troubleshoot. Don't blend mode logic — finish one before moving on.

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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 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.

Core concepts

Treat .ml-anomalies-* as three layers, accessed via result_type:

• bucket — bucket-level unusualness per bucket_span. anomaly_score is the aggregate across all detectors.
• record — finest-grained rows with actual vs typical, probability, record_score,

anomaly_score_explanation.

• influencer — entity contributions ranked within a bucket (influencer_score).

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.
• Compare actual to typical; use probability for raw likelihood.
• 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.

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Mode: Investigate — RCA

When: "what broke?", "which entity caused this?", cross-job correlation, blast radius, attack/cascade chains.

Tool chain

Protocol

Follow the 14-step sequence in references/protocols/investigation.md. High level: ad_get_available_metadata → pair ad_discover_jobs_by_datafeed_index with ad_discover_related_jobs → ad_query_anomaly_timeline → rank with ad_rca_multi_job_entities (min_job_count=2) → ad_rca_detector_fingerprint → drill with ad_query_anomaly_records + ad_query_influencers (low min_score=25) → profile with ad_rca_entity_profile → order with ad_rca_correlation → confirm with ad_rca_source_evidence. When by_field_name == "mlcategory", compare with ad_get_categories + paired ad_search_log_category_examples (baseline vs. anomaly window).

Finish with a written RCA: root cause entity · affected jobs · temporal progression · fault class (resource/network/application) · severity · recommended actions. Worked example: references/worked-example.md. Full ES|QL templates and parameters: references/investigate-anomaly-esql-tools.md.

Rules

1. Multi-job entities are prime suspects; single-job entities are usually victims. Use min_job_count=2.
2. Earliest anomaly timestamp wins — sort ad_rca_correlation by timestamp; first-appearing entity = origin.
3. multi_bucket_impact ≥ 3 = sustained behavioral shift, weight higher than transient spikes.
4. Never close an RCA without ad_rca_source_evidence — raw source documents are ground truth.
5. Use low min_score (25 or lower) for influencer queries — high thresholds miss correlated entities.

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Mode: Explain — Score / model behavior

When: "why is my score 30/90?", "score dropped overnight", "what is renormalization?", "why wasn't this detected?".

Score types

anomaly_score_explanation components

Why a score looks wrong

• Unexpectedly low: high_variance_penalty, renormalization, <3 weeks training for weekly seasonality,

bucket_span too large, wrong detector function (mean vs high_mean), incomplete_bucket_penalty, suppression by custom_rules.

• Unexpectedly high: insufficient history (early training over-flags), high-cardinality split (too few points per

entity), use_null: true on a sparse field.

Tool chain

Decision tree (ad_wf_troubleshoot_anomaly_score)

1. ad_get_jobs — ≥3 weeks data for weekly seasonality?
2. ad_ts_model_memory_health — memory_status healthy?
3. ad_ts_delayed_data_annotations — no incomplete buckets?
4. ad_query_anomaly_records — compare record_score vs initial_record_score.
5. ad_get_job_datafeed_config — bucket_span, detector function, custom_rules, use_null.
6. ad_get_model_plot — wide bounds → high_variance_penalty.
7. ad_rca_score_reassessment — renormalization drift across history.
8. Explain anomaly_score_explanation factors.

Rules

1. Always show both initial_record_score and record_score — the gap is the renormalization story.
2. Explain renormalization before diagnosing config — score drift is the most common "score dropped" cause and needs

no config change.

1. actual << typical with count/low_count is an absence anomaly — distinguish outages from value spikes.
2. high_variance_penalty and incomplete_bucket_penalty explain most "low score" surprises without remediation.
3. Weekly seasonality needs ≥3 weeks of training data — flag young jobs as the cause.

For detector function selection details, see references/anomaly-detection-functions.md.

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Mode: Troubleshoot — Job ops

When: "missing documents", "datafeed stopped", "hard_limit", "results look wrong", lifecycle changes, calendars, CCS.

Common issues → fast paths

hard_limit corrupts model state and causes downstream missing-doc false alarms (categorizer silently skips events for unknown categories). Fix memory before fixing query_delay.

Memory concepts

Prefer ad_estimate_memory_requirement (samples cardinality from source, calls Estimate Model Memory API) over heuristics like peak_model_bytes * 1.3 — the heuristic ignores pure influencer and categorization memory.

Datafeed & timing concepts

• query_delay — how far behind real time the datafeed queries. Too small → missing docs; too large → slower

alerts. Set to P95 ingest latency + buffer (default 60s–120s).

• delayed_data_check_config — how aggressively the datafeed checks for late data.
• bucket_span — analysis interval. Align with data granularity and detection window.
• frequency — defaults to min(query_delay, bucket_span / 2).

Lifecycle for config changes (memory limit, query_delay)

1. Stop datafeed: ad_manage_datafeed (action=_stop)
2. Close job
3. Update config: ad_update_model_memory_limit, ad_update_datafeed_query_delay,

ad_update_delayed_data_check_config

1. Open job: ad_open_job
2. Start datafeed: ad_manage_datafeed (action=_start)

Recover a corrupted period without resetting the whole model: ad_revert_model_snapshot.

Tool surface

Full parameter tables, ES|QL templates, and REST step lists: references/troubleshoot-anomaly-tool-reference.md.

Rules

1. ad_validate_ml_tool_permissions first — missing privileges produce misleading empty results.
2. Fix memory before query_delay — hard_limit corrupts state; query_delay fixes on a memory-limited job are

wasted.

1. Stop the datafeed before updating it. Updating a running datafeed is rejected.
2. Close the job before updating memory limit. Sequence above.
3. *Prefer workflow tools (ad_wf_) over manually chaining diagnostics** for complex decisions.
4. ad_preview_datafeed_with_latency before starting — confirm the datafeed returns data after config changes.

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Mode: Manage — Create / configure jobs

When: "set up a job", "create an ML detector", "monitor X over time", "detect rare/unusual/anomalous values".

4-step workflow

PUT  _ml/anomaly_detectors/<job_id>          # 1. Define job        (ad_create_job)
PUT  _ml/datafeeds/datafeed-<job_id>         # 2. Define datafeed   (ad_create_datafeed)
POST _ml/anomaly_detectors/<job_id>/_open    # 3a. Open job         (ad_open_job)
POST _ml/datafeeds/datafeed-<job_id>/_start  # 3b. Start datafeed   (ad_manage_datafeed action=_start)
GET  _ml/anomaly_detectors/<job_id>/results/records  # 4. Read results

Process

1. Build configs. Parse the user request into job + datafeed JSON with no null fields.
2. Apply smart defaults:

1. Choose detector function from user intent — full table in

references/anomaly-detection-functions.md:

• "high CPU" / "unusually large" → high_mean or high_sum
• "rare logins" / "unusual values" → rare (variants below)
• "too many requests" / "spike in count" → high_count

rare variants:

• Infrequent globally → rare by_field_name: X
• Infrequent vs peers → rare by_field_name: X over_field_name: Y
• Infrequent per segment → rare by_field_name: X partition_field_name: Y
• Infrequent per segment vs peers → rare by_field_name: X over_field_name: Y partition_field_name: Z

1. Validate. platform.core.get_index_mapping on the target index to verify field existence/types →

ad_validate_job_spec. If errors, fix and re-validate (max 3 attempts).

1. Present and confirm. Show the complete job + datafeed bodies formatted as the exact API calls. Ask for

approval once. If feedback, incorporate and re-present (up to 3 rounds).

1. Deploy. After confirmation: ad_create_job → ad_create_datafeed → ad_open_job → ad_manage_datafeed

(action=_start). Report final job_id and datafeed_id.

For batch analysis on historical data, pass start and end to the datafeed start call.

Worked examples (rare-username, DNS exfil, large-downloads) with full JSON bodies and datafeed filters: references/job-creation-recipes.md.

Rules

1. Create job before datafeed. Datafeed references job by ID.
2. Open job before starting datafeed. Start on a closed job is rejected.
3. query_delay = P95 ingest latency + buffer (60s–120s safe default).
4. Forecasts require non-population jobs — over_field_name jobs cannot be forecasted; warn before attempting.
5. by_field_name vs over_field_name: by compares entity to its own history; over compares to peer group in

the same bucket. partition_field_name = fully independent sub-model with its own normalization.

1. bucket_span matches detection granularity — 15m for high-frequency, 1h for operational metrics, 1d for daily

patterns. Larger smooths short spikes; smaller increases noise.

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Registration (Kibana Agent Builder)

Requires Node.js 18+. Defaults to elastic/changeme when no credentials are supplied.

cd skills/kibana/kibana-anomaly-detection

# tools → workflows → skills
node scripts/kibana-agent-builder.mjs all register --kibana-url http://localhost:5601

# HTTPS with self-signed cert
node scripts/kibana-agent-builder.mjs all register --kibana-url https://localhost:5601 --insecure

all register runs tools register, then workflows register, then skills register. Kibana allows at most five tool_ids per skill; the script fills them by scanning SKILL.md for tool mentions (in document order), then appends ids from references/kibana/tools/esql/*.json until the cap (workflow-only tools omitted by default). If you run skills register alone, run tools register first so those ids exist.

Workflow tool exclusions and prefixes live in scripts/agent_builder_constants.json.

MCP API key permissions:

• Kibana: read_onechat, space_read
• Index: read, view_index_metadata on .ml-anomalies-, .ml-annotations-, .ml-notifications-*, .ml-config
• For source evidence: read on source data indices

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Tool inventory

ES|QL tool specs live under references/kibana/tools/esql/.json; workflow definitions under references/kibana/workflows/.yaml. Each Mode section above lists the tools it uses. Full surface: references/tools.md (ES|QL) and references/workflow-tools.md (workflows).

Key system indices

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Examples

RCA: "Something caused a spike in our error rate at 2pm — what broke?" → Investigate → ad_get_available_metadata → ad_query_anomaly_timeline → ad_rca_cross_job_entity_match → ad_rca_multi_job_entities → RCA report.

Score drop: "My anomaly score went from 90 to 55 — did the model change?" → Explain → ad_rca_score_reassessment for drift → explain renormalization if score_drift is large.

Memory limit: "Job status shows hard_limit and results look wrong." → Troubleshoot → ad_ts_model_memory_health → ad_wf_ts_field_cardinality → ad_estimate_memory_requirement → ad_update_model_memory_limit (lifecycle: stop datafeed → close → update → open → start).

New job: "Detect unusual error rates per host on nginx access logs." → Manage → high_count detector with by_field_name: "host.keyword" → validate → present → deploy.

Multi-mode: "We had an incident last night, scores were high but now low — is the job healthy?" → Investigate the incident → Explain the score drift → Troubleshoot if hard_limit or delayed data is suspected.

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Guidelines

1. Pick a mode first. Don't blend RCA logic with score-explanation logic in one response.
2. ad_validate_ml_tool_permissions first on empty results — privileges are the most common false-negative cause.
3. Score bands are absolute thresholds: >75 critical, 50–75 warning, 25–50 minor, <25 informational.
4. Multi-job entities are prime suspects. Use min_job_count=2 in ad_rca_multi_job_entities.
5. Show initial_record_score alongside record_score — the gap tells the renormalization story.
6. Fix memory before query_delay. hard_limit invalidates downstream diagnostics.
7. Stop datafeed → close job → update config → open job → start datafeed for any config change to memory or query

delay.

1. Confirm RCAs with ad_rca_source_evidence. Raw source documents are ground truth.