kibana-dashboards
elastic/agent-skills
Create and manage Kibana dashboards and Lens visualizations declaratively with version control and automation.
What is kibana-dashboards?
Build, update, and delete Kibana dashboards and standalone Lens visualizations using the Kibana 9.4+ Dashboards and Visualizations APIs. Use this skill when you need to define dashboards declaratively, version control them, or automate their deployment across environments.
- Create and update dashboards with inline panel definitions and ES|QL queries
- Build standalone Lens visualizations (metrics, charts, gauges, heatmaps) and reference them in dashboards
- Choose between data-view aggregations and ES|QL text-based queries based on use case
- Manage dashboard time ranges and grid layout (48-column grid system)
- List, export, import, and delete dashboards and visualizations via API
How to install kibana-dashboards
npx skills add https://github.com/elastic/agent-skills --skill kibana-dashboards- Kibana 9.4 or later with matching Elasticsearch (self-managed, Elastic Cloud Hosted, or Serverless)
- elastic CLI version 0.3 or later with stack kb support (dashboards and visualizations commands)
- Valid credentials configured for the elastic CLI
How to use kibana-dashboards
- 1.Verify Kibana connectivity by calling GET kbn:/api/status and confirming version 9.4+
- 2.Classify your task: dashboard (collection of panels), standalone Lens visualization, or both
- 3.Choose dataset type before building metrics: data_view_reference for saved views, data_view_spec for ad-hoc patterns, or esql for ES|QL queries
- 4.Build the dashboard or visualization body with required fields (title, panels, data_source, metrics)
- 5.Execute the write operation using PUT kbn:/api/dashboards/{id} or PUT kbn:/api/visualizations/{id}
- 6.Confirm success with a read-back GET request and report the id and title to the user
Use cases
- Automate dashboard creation for monitoring and observability workflows
- Define reusable Lens visualizations as library items for consistent reporting
- Version control dashboard definitions in Git for reproducible deployments
- Build ES|QL-based dashboards for ad-hoc analysis without saved data views
- Bulk export or import dashboards across Kibana instances
- DevOps and SRE teams automating observability infrastructure
- Data analysts building reproducible reporting dashboards
- Platform engineers managing Kibana at scale
- Developers integrating dashboard creation into CI/CD pipelines
kibana-dashboards FAQ
Use data_source.type: 'esql' when the user explicitly requests ES|QL or needs complex logic; use data_view_reference for simple counts or aggregations on saved data views. Always write aggregations in the ES|QL query (e.g., STATS count = COUNT()) and reference the column by name in metrics, never use operation: 'count' on the metric itself.
Yes, you can reference standalone Lens visualizations in dashboard panels via ref_id, but the skill prefers inline panel definitions with embedded config for portability and version control.
Kibana dashboards use a 48-column grid. On 16:9 screens, roughly 20–24 rows fit above the fold; target 8–12 panels in that band for optimal layout.
Include time_range at the root of the dashboard body: { 'from': 'now-7d', 'to': 'now' }. This persists the time filter when the dashboard opens.
Inform the user that the elastic CLI is required and provide installation instructions. Do not attempt to call the HTTP API directly or guess credentials.
Full instructions (SKILL.md)
Source of truth, from elastic/agent-skills.
name: kibana-dashboards
description: >
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.
metadata:
author: elastic
version: 0.3.0
universal: true
compatibility: Kibana 9.4 or later (Dashboards and Visualizations APIs) with matching
Elasticsearch, self-managed, Elastic Cloud Hosted, or Elastic Cloud Serverless.
Requires the elastic CLI ≥ 0.3 with stack kb support (dedicated dashboards
and visualizations commands).
Kibana Dashboards and Lens Visualizations
Create, update, and delete Kibana dashboards and standalone Lens visualizations using the Kibana 9.4+ Dashboards and Visualizations APIs. Produce minimal, diffable JSON bodies; prefer inline panel definitions over library references; and choose the correct dataset type (data view vs ES|QL) before writing metrics or chart layers.
<!-- begin-partial: preamble -->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.
Prerequisites
Version requirement: Kibana 9.4+ (Dashboards and Visualizations APIs).
ES|QL placement:
- Standalone library charts:
PUT kbn:/api/visualizations/{id}withdata_source.type: "esql". - ES|QL panels embedded in a dashboard: inline
vispanelconfigwithdata_source.type: "esql"viaPUT kbn:/api/dashboards/{id}. - Do not use
data_source.type: "data_view_reference"or index-pattern aggregations when the user explicitly requests ES|QL — the persisted Lens state must use a text-based ES|QL datasource (textBased/esql), not a data-view count operation.
Process
-
Verify Kibana connectivity. Call
GET kbn:/api/status. If the call fails, stop and surface the error — do not guess endpoints or credentials. Readversion.numberto confirm the cluster meets the 9.4+ requirement. -
Classify the task. Decide whether the user needs a dashboard (collection of panels, optional time range), a standalone Lens visualization (library item referenced by id or used alone), or both. Determine whether a deterministic saved-object id was supplied — when given, use upsert (
PUT) with that id rather thanPOST(which auto-generates ids). -
Choose the dataset type before building metrics or layers.
User intent Dataset Metric / axis pattern Simple count or aggregation on a saved data view data_source.type: "data_view_reference"withref_idmetrics: [{ type: "primary", operation: "count" }](or other aggregation operations)Ad-hoc index pattern data_source.type: "data_view_spec"withindex_patternandtime_fieldSame aggregation operationfieldsES|QL query (explicit or complex logic) data_source.type: "esql"withquerymetrics: [{ type: "primary", column: "<alias>" }]or layer axes{ column: "<alias>" }— neveroperation: "count"on the metricWrite the aggregation in the ES|QL query (
STATS count = COUNT()), then reference the resulting column by name. -
Build a dashboard body when creating or updating dashboards. The request body is flat —
title,panels, and optionaltime_rangeat the root. Do not wrap in{ data: ... }on write. Required fields:title— exact string the user requested.panels— array; use[]when the user asks for an empty dashboard (do not omit the key or invent panels).time_range— when the user specifies a default time filter, set{ "from": "<expr>", "to": "<expr>" }(for example{ "from": "now-7d", "to": "now" }). Supplyingtime_rangepersists the dashboard time filter on open (equivalent to enabling time restore in the UI).
Upsert with a deterministic id:
{ "title": "Sales Overview", "panels": [], "time_range": { "from": "now-7d", "to": "now" } }Call
PUT kbn:/api/dashboards/eval-sales-overviewwith the body above when the user supplies that id.Inline ES|QL metric panel example (inside
panels):{ "type": "vis", "id": "total-requests", "grid": { "x": 0, "y": 0, "w": 12, "h": 6 }, "config": { "title": "Total Requests", "type": "metric", "data_source": { "type": "esql", "query": "FROM logs* | STATS count = COUNT()" }, "metrics": [{ "type": "primary", "column": "count" }] } }Prefer inline
configproperties overconfig.ref_idfor portable dashboards. Read Dashboard API Reference for panel types, grid layout, and copy workflows. -
Build a standalone Lens visualization when the user asks for a library chart. Use the Visualizations API. Upsert with
PUT kbn:/api/visualizations/{id}when an id is supplied; otherwisePOST kbn:/api/visualizationsand report the generated id from the response.ES|QL metric (total count from logs):
{ "type": "metric", "title": "Total Requests", "data_source": { "type": "esql", "query": "FROM logs* | STATS count = COUNT()" }, "metrics": [{ "type": "primary", "column": "count" }] }Call
PUT kbn:/api/visualizations/eval-total-requestswhen that id is required. The API persists a Lens saved object whose datasource state uses ES|QL (textBased/esql), not an index-pattern aggregation.Read Lens API Reference and Chart Types Reference for xy, gauge, heatmap, and other chart schemas.
-
Execute and confirm. Perform the write with
PUT kbn:/api/dashboards/{id}orPUT kbn:/api/visualizations/{id}(orPOSTwhen no id is supplied). Confirm withGET kbn:/api/dashboards/{id}orGET kbn:/api/visualizations/{id}. Report the id and title back to the user — do not claim success without a successful read-back. -
List, export, or delete when requested. Call
GET kbn:/api/dashboardsorGET kbn:/api/visualizationsto discover existing objects. CallDELETE kbn:/api/dashboards/{id}orDELETE kbn:/api/visualizations/{id}to remove objects. For bulk export or import of saved objects, callPOST kbn:/api/saved_objects/_exportorPOST kbn:/api/saved_objects/_import.
Dashboard grid
Dashboards use a 48-column grid. On 16:9 screens, roughly 20–24 rows fit above the fold — target 8–12 panels in that band.
| Width | Columns | Height (rows) | Use case |
|---|---|---|---|
| Full | 48 | 14–16 | Wide time series, tables |
| Half | 24 | 10–12 | Primary charts |
| Quarter | 12 | 5–6 | KPI metrics |
| Sixth | 8 | 4–5 | Dense metric rows |
Grid packing: When stacking rows, set the next panel's y to the previous panel's y + h. Panels sharing a row
should use the same h. Do not add markdown panels as dashboard titles — use descriptive chart titles instead.
ES|QL patterns
Time series bucket (dashboard time picker injects ?_tstart / ?_tend):
FROM logs*
| WHERE @timestamp <= ?_tend AND @timestamp > ?_tstart
| STATS count = COUNT() BY BUCKET(@timestamp, 75, ?_tstart, ?_tend)
Set "scale": "temporal" on the x-axis for time-series xy charts. See
Chart Types Reference for axis and layer details.
Static reference values — use EVAL in the query, then reference the column:
FROM logs* | STATS count = COUNT() | EVAL goal = 15000
Examples
Example JSON definitions live under assets/: demo-dashboard.json, dashboard-with-visualizations.json,
metric-esql.json, bar-chart-esql.json, line-chart-timeseries.json.
Guidelines
- Match the user's id and title exactly when supplied — do not substitute auto-generated ids.
- Honor empty panels — when the user asks for
panels: [], send an empty array; do not add placeholder panels. - ES|QL when requested — use
data_source.type: "esql"and column references; never satisfy an ES|QL request withoperation: "count"on a data view. - Minimal payloads — omit derivable defaults; let the API inject styling and metadata.
- Confirm writes — always read back with
GETafter create or update. - Read references before complex charts — metric and xy schemas differ between data view and ES|QL; consult Chart Types Reference before generating partition or table charts.
Common issues
| Error | Likely cause | Fix |
|---|---|---|
| 404 on GET after PUT | Wrong id or space | Confirm id and retry GET kbn:/api/dashboards/{id} |
| 400 validation | ES|QL column mismatch | Align metrics[].column / layer column with STATS aliases in the query |
| ES|QL panel saved as data view | Wrong dataset type | Use data_source.type: "esql", not data_view_reference |
| Empty dashboard missing time filter | Omitted time_range | Include { "from": "now-7d", "to": "now" } when a default range is required |
| XY chart failure | Missing layer data_source | Put data_source inside each layer, not only at the root |
Operations
As of CLI v0.3.0 the Dashboards and Visualizations APIs have dedicated elastic kb dashboards and
elastic kb visualizations commands for listing, reading, updating, and deleting objects by id. The create-*-redirect
commands do not accept a request body yet, so to write a new object supply an id and use the update-*-redirect (PUT)
command, which carries the JSON body via --input-file. To author several objects at once, build a saved-object NDJSON
and import it with post-saved-objects-import (read it back with post-saved-objects-export).
| HTTP API (shorthand) | elastic CLI command |
|---|---|
GET kbn:/api/status | elastic kb system get-status |
POST kbn:/api/saved_objects/_import | elastic kb saved-objects post-saved-objects-import --file '<path.ndjson>' --overwrite |
POST kbn:/api/saved_objects/_export | elastic kb saved-objects post-saved-objects-export --objects '[{"type":"<type>","id":"<id>"}]' |
GET kbn:/api/dashboards | elastic kb dashboards get-dashboards-redirect |
GET kbn:/api/dashboards/{id} | elastic kb dashboards get-dashboard-redirect --id '<id>' |
PUT kbn:/api/dashboards/{id} | elastic kb dashboards update-dashboard-redirect --id '<id>' --input-file '<path.json>' |
DELETE kbn:/api/dashboards/{id} | elastic kb dashboards delete-dashboard-redirect --id '<id>' |
POST kbn:/api/dashboards (no id) | create-dashboard-redirect takes no body yet — supply an id and use update-dashboard-redirect, or author via post-saved-objects-import (type dashboard) |
GET kbn:/api/visualizations | elastic kb visualizations get-visualizations-redirect |
GET kbn:/api/visualizations/{id} | elastic kb visualizations get-visualization-redirect --id '<id>' |
PUT kbn:/api/visualizations/{id} | elastic kb visualizations update-visualization-redirect --id '<id>' --input-file '<path.json>' |
DELETE kbn:/api/visualizations/{id} | elastic kb visualizations delete-visualization-redirect --id '<id>' |
POST kbn:/api/visualizations (no id) | create-visualization-redirect takes no body yet — supply an id and use update-visualization-redirect, or author via post-saved-objects-import (type lens) |
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