kibana-dashboards
Create and manage Kibana Dashboards and Lens visualizations.
Skill by elasticOfficial
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Files it touches
~/.claude/skills/kibana-dashboards
What's inside
- Skill 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.
- License
- Apache-2.0
- 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).
- Metadata
- author: elastic · version: 0.3.0 · universal: true
- Instructions
- 215 lines in SKILL.md · view on GitHub
Files (12)
- SKILL.md
- bar-chart-esql.json · assets/
- dashboard-basic.json · assets/
- dashboard-with-visualizations.json · assets/
- datatable.json · assets/
- demo-dashboard.json · assets/
- ecommerce-analytics-dashboard.json · assets/
- line-chart-timeseries.json · assets/
- metric-esql.json · assets/
- chart-types-reference.md · references/
- dashboard-api-reference.md · references/
- lens-api-reference.md · references/
SKILL.md (first lines)
# 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](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 -->
## Prerequisites
**Version requirement:** Kibana 9.4+ (Dashboards and Visualizations APIs).
**ES|QL placement:**
- Standalone library charts: `PUT kbn:/api/visualizations/{id}` with `data_source.type: "esql"`.
- ES|QL panels embedded in a dashboard: inline `vis` panel `config` with `data_source.type: "esql"` via
`PUT 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
1. **Verify Kibana connectivity.** Call `GET kbn:/api/status`. If the call fails, stop and surface the error — do not
guess endpoints or credentials. Read `version.number` to confirm the cluster meets the 9.4+ requirement.
2. **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 than `POST` (which
auto-generates ids).
3. **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"` with `ref_id` | `metrics: [{ type: "primary", operation: "count" }]` (or other aggregation operations) |
| Ad-hoc index pattern | `data_source.type: "data_view_spec"` with `index_pattern` and `time_field` | Same aggregation `operation` fields |
| ES\|QL query (explicit or complex logic) | `data_source.type: "esql"` with `query` | `metrics: [{ type: "primary", column: "<alias>" }]` or layer axes `{ column: "<alias>" }` — **never** `operation: "count"` on the metric |
Write the aggregation in the ES|QL query (`STATS count = COUNT()`), then reference the resulting column by name.
4. **Build a dashboard body when creating or updating dashboards.** The request body is flat — `title`, `panels`, and
optional `time_range` at 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" }`). Supplying `time_range` persists the dashboard time filter on open
(equivalent to enabling time restore in the UI).
**Upsert with a deterministic id:**
```json
{
"title": "Sales Overview",
"panels": [],
"time_range": { "from": "now-7d", "to": "now" }
}
```
Call `PUT kbn:/api/dashboards/eval-sales-overview` with the body above when the user supplies that id.
**Inline ES|QL metric panel example** (inside `panels`):
```json
{
"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 `config` properties over `config.ref_id` for portable dashboards. Read
[Dashboard API Reference](references/dashboard-api-reference.md) for panel types, grid layout, and copy workflows.
5. **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; otherwise `POST kbn:/api/visualizations` and report
the generated id from the response.
**ES|QL metric (total count from logs):**
```json
{
"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-requests` when 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](references/lens-api-reference.md) and
[Chart Types Reference](references/chart-types-reference.md) for xy, gauge, heatmap, and other chart schemas.From elastic/agent-skills, Apache-2.0.
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Signals
- Stars
- 600
- Forks
- 55
- Open issues
- 4
- Last commit (this folder)
- Sep 4, 2026 (1 month ago)
- Last release
- Sep 4, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 0.3.0
- License
- Apache-2.0
- Repository
- github.com/elastic/agent-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
kibana-dashboards skill. Agent Mart. https://agentmart.dev/skills/elastic-kibana-dashboards (last updated 2026-10-24)