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Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"elastic","Elastic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Felastic.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Dashboards","dashboards","tag",{"name":17,"slug":18,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Kibana","kibana",531,"https:\u002F\u002Fgithub.com\u002Felastic\u002Fagent-skills","2026-07-12T07:46:48.710564",null,41,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Official Elastic Skills","https:\u002F\u002Fgithub.com\u002Felastic\u002Fagent-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fkibana\u002Fkibana-dashboards","---\nname: kibana-dashboards\ndescription: >\n  Create and manage Kibana Dashboards and visualizations. Use when you need to define\n  dashboards and visualizations declaratively, version control them, or automate their\n  deployment.\nmetadata:\n  author: elastic\n  version: 0.1.2\n---\n\n# Kibana Dashboards and Visualizations\n\n## Overview\n\nThe Kibana dashboards and visualizations APIs provide a declarative, Git-friendly format for defining dashboards and\nvisualizations. Definitions are minimal, diffable, and suitable for version control and LLM-assisted generation.\n\n**Key Benefits:**\n\n- Minimal payloads (no implementation details or derivable properties)\n- Easy to diff in Git\n- Consistent patterns for GitOps workflows\n- Designed for LLM one-shot generation\n- Robust validation via OpenAPI spec\n\n**Version Requirement:** Kibana 9.4+ (SNAPSHOT)\n\n## Important Caveats\n\n> **ES|QL Visualizations:** ES|QL-based visualizations cannot be created via `\u002Fapi\u002Fvisualizations`. They must be created\n> as inline panels within dashboards using the Dashboard API.\n>\n> **Inline vs Saved Object References:** When embedding visualization panels in dashboards, prefer inline definitions\n> over `ref_id` references. Inline definitions are more reliable and self-contained.\n\n## Quick Start\n\n### Environment Configuration\n\nKibana connection is configured via environment variables. Run `node scripts\u002Fkibana-dashboards.js test` to verify the\nconnection. If the test fails, suggest these setup options to the user, then stop. Do not try to explore further until a\nsuccessful connection test.\n\n#### Option 1: Elastic Cloud (recommended for production)\n\n```bash\nexport KIBANA_CLOUD_ID=\"deployment-name:base64encodedcloudid\"\nexport KIBANA_API_KEY=\"base64encodedapikey\"\n```\n\n#### Option 2: Direct URL with API Key\n\n```bash\nexport KIBANA_URL=\"https:\u002F\u002Fyour-kibana:5601\"\nexport KIBANA_API_KEY=\"base64encodedapikey\"\n```\n\n#### Option 3: Basic Authentication\n\n```bash\nexport KIBANA_URL=\"https:\u002F\u002Fyour-kibana:5601\"\nexport KIBANA_USERNAME=\"elastic\"\nexport KIBANA_PASSWORD=\"changeme\"\n```\n\n#### Option 4: Local Development with start-local\n\nUse [start-local](https:\u002F\u002Fgithub.com\u002Felastic\u002Fstart-local) to spin up Elasticsearch\u002FKibana locally, then source the\ngenerated `.env`:\n\n```bash\ncurl -fsSL https:\u002F\u002Felastic.co\u002Fstart-local | sh\nsource elastic-start-local\u002F.env\nexport KIBANA_URL=\"$KB_LOCAL_URL\"\nexport KIBANA_USERNAME=\"elastic\"\nexport KIBANA_PASSWORD=\"$ES_LOCAL_PASSWORD\"\n```\n\nThen run `node scripts\u002Fkibana-dashboards.js test` to verify the connection.\n\n#### Optional: Skip TLS verification (development only)\n\n```bash\nexport KIBANA_INSECURE=\"true\"\n```\n\n### Basic Workflow\n\n```bash\n# Test connection and API availability\nnode scripts\u002Fkibana-dashboards.js test\n\n# Dashboard operations\nnode scripts\u002Fkibana-dashboards.js dashboard get \u003Cid>\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js dashboard create -\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js dashboard update \u003Cid> -\nnode scripts\u002Fkibana-dashboards.js dashboard delete \u003Cid>\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js dashboard upsert \u003Cid> -\n\n# Visualization operations (standalone saved objects)\nnode scripts\u002Fkibana-dashboards.js vis list\nnode scripts\u002Fkibana-dashboards.js vis get \u003Cid>\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js vis create -\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js vis update \u003Cid> -\nnode scripts\u002Fkibana-dashboards.js vis delete \u003Cid>\necho '\u003Cjson>' | node scripts\u002Fkibana-dashboards.js vis upsert \u003Cid> -\n```\n\n## Dashboards API\n\n### Dashboard Definition Structure\n\nThe API expects a flat request body with `title` and `panels` at the root level. The response wraps these in a `data`\nenvelope alongside `id`, `meta`, and `spaces`.\n\n```json\n{\n  \"title\": \"My Dashboard\",\n  \"panels\": [ ... ],\n  \"time_range\": {\n    \"from\": \"now-24h\",\n    \"to\": \"now\"\n  }\n}\n```\n\n> **Note:** Dashboard IDs are auto-generated by the API. The script also accepts the legacy wrapped format\n> `{ id?, data: { title, panels }, spaces? }` and unwraps it automatically.\n\n### Dashboard with Inline Visualization Panels (Recommended)\n\nUse inline definitions (properties directly in `config`) for self-contained, portable dashboards:\n\n```json\n{\n  \"title\": \"My Dashboard\",\n  \"panels\": [\n    {\n      \"type\": \"vis\",\n      \"id\": \"metric-panel\",\n      \"grid\": { \"x\": 0, \"y\": 0, \"w\": 12, \"h\": 6 },\n      \"config\": {\n        \"title\": \"\",\n        \"type\": \"metric\",\n        \"data_source\": { \"type\": \"esql\", \"query\": \"FROM logs | STATS total = COUNT(*)\" },\n        \"metrics\": [{ \"type\": \"primary\", \"column\": \"total\", \"label\": \"Total Count\" }]\n      }\n    },\n    {\n      \"type\": \"vis\",\n      \"id\": \"chart-panel\",\n      \"grid\": { \"x\": 12, \"y\": 0, \"w\": 36, \"h\": 8 },\n      \"config\": {\n        \"title\": \"Events Over Time\",\n        \"type\": \"xy\",\n        \"axis\": {\n          \"x\": { \"scale\": \"temporal\", \"domain\": { \"type\": \"fit\", \"rounding\": false } }\n        },\n        \"layers\": [\n          {\n            \"type\": \"area\",\n            \"data_source\": {\n              \"type\": \"esql\",\n              \"query\": \"FROM logs | WHERE @timestamp \u003C= ?_tend AND @timestamp > ?_tstart | STATS count = COUNT(*) BY BUCKET(@timestamp, 75, ?_tstart, ?_tend)\"\n            },\n            \"x\": { \"column\": \"BUCKET(@timestamp, 75, ?_tstart, ?_tend)\", \"label\": \"@timestamp\" },\n            \"y\": [{ \"column\": \"count\" }]\n          }\n        ]\n      }\n    }\n  ],\n  \"time_range\": { \"from\": \"now-24h\", \"to\": \"now\" }\n}\n```\n\n### Dashboard Grid System\n\nDashboards use a **48-column, infinite-row grid**. On 16:9 screens, approximately **20-24 rows** are visible without\nscrolling. Design for density—place primary KPIs and key trends above the fold.\n\n| Width   | Columns | Height   | Rows  | Use Case                 |\n| ------- | ------- | -------- | ----- | ------------------------ |\n| Full    | 48      | Large    | 14-16 | Wide time series, tables |\n| Half    | 24      | Standard | 10-12 | Primary charts           |\n| Quarter | 12      | Compact  | 5-6   | KPI metrics              |\n| Sixth   | 8       | Minimal  | 4-5   | Dense metric rows        |\n\n> **Target:** 8-12 panels above the fold. Use descriptive panel titles on the charts themselves instead of adding\n> markdown headers.\n\n**Grid Packing Rules:**\n\n- **Eliminate Dead Space:** Always calculate the bottom edge (`y + h`) of every panel. When starting a new row or\n  placing a panel below another, its `y` coordinate must exactly match the `y + h` of the panel immediately above it.\n- **Align Row Heights:** If multiple panels are placed side-by-side in a row (e.g., sharing the same `y` coordinate),\n  they should generally have the exact same height (`h`). If they do not, you must fill the resulting empty vertical\n  space before placing the next full-width panel.\n\n### Panel Schema\n\n```json\n{\n  \"type\": \"vis\",\n  \"id\": \"unique-panel-id\",\n  \"grid\": { \"x\": 0, \"y\": 0, \"w\": 24, \"h\": 15 },\n  \"config\": { ... }\n}\n```\n\n| Property | Type   | Required | Description                                      |\n| -------- | ------ | -------- | ------------------------------------------------ |\n| `type`   | string | Yes      | Embeddable type (e.g., `vis`, `markdown`, `map`) |\n| `id`     | string | No       | Unique panel ID (auto-generated if omitted)      |\n| `grid`   | object | Yes      | Position and size (`x`, `y`, `w`, `h`)           |\n| `config` | object | Yes      | Panel-specific configuration                     |\n\n## Visualizations API\n\n### Supported Chart Types\n\n| Type                                 | Description                 | ES\\|QL Support |\n| ------------------------------------ | --------------------------- | -------------- |\n| `metric`                             | Single metric value display | Yes            |\n| `xy`                                 | Line, area, bar charts      | Yes            |\n| `gauge`                              | Gauge visualizations        | Yes            |\n| `heatmap`                            | Heatmap charts              | Yes            |\n| `tag_cloud`                          | Tag\u002Fword cloud              | Yes            |\n| `data_table`                         | Data tables                 | Yes            |\n| `region_map`                         | Region\u002Fchoropleth maps      | Yes            |\n| `pie`, `treemap`, `mosaic`, `waffle` | Partition charts            | Yes            |\n\n> **Note:** To create donut charts, use `pie` with `styling.donut_hole` set to `\"s\"`, `\"m\"`, or `\"l\"` (small, medium,\n> large hole). Use `\"none\"` for a solid pie. Example: `\"styling\": { \"donut_hole\": \"m\" }`.\n\n### Dataset Types\n\nThere are three dataset types supported in the Visualizations API. Each uses different patterns for specifying metrics\nand dimensions.\n\n#### Data View Dataset\n\nUse `data_view_reference` with aggregation operations. Kibana performs the aggregations automatically.\n\n```json\n{\n  \"data_source\": {\n    \"type\": \"data_view_reference\",\n    \"ref_id\": \"90943e30-9a47-11e8-b64d-95841ca0b247\"\n  }\n}\n```\n\n**Available operations:** `count`, `average`, `sum`, `max`, `min`, `unique_count`, `median`, `standard_deviation`,\n`percentile`, `percentile_rank`, `last_value`, `date_histogram`, `terms`. See\n[Chart Types Reference](references\u002Fchart-types-reference.md) for details.\n\n#### ES|QL Dataset\n\nUse `esql` with a query string. Reference the output columns using `{ column: 'column_name' }`.\n\n```json\n{\n  \"data_source\": {\n    \"type\": \"esql\",\n    \"query\": \"FROM logs | STATS count = COUNT(), avg_bytes = AVG(bytes) BY host\"\n  }\n}\n```\n\n**ES|QL Column Reference Pattern:**\n\n```json\n{ \"column\": \"count\" }\n```\n\n> **Key Difference:** With ES|QL, you write the aggregation in the query itself, then reference the resulting columns.\n> With data view, you specify the aggregation operation and Kibana performs it.\n>\n> **Important:** ES|QL visualizations cannot be created via `\u002Fapi\u002Fvisualizations`. They must be created as inline panels\n> in dashboards via the Dashboard API.\n\n#### Index Dataset\n\nUse `index` for ad-hoc index patterns without a saved data view:\n\n```json\n{\n  \"data_source\": {\n    \"type\": \"data_view_spec\",\n    \"index_pattern\": \"logs-*\",\n    \"time_field\": \"@timestamp\"\n  }\n}\n```\n\n## Examples\n\nFor detailed schemas and all chart type options, see [Chart Types Reference](references\u002Fchart-types-reference.md).\n\n**Metric (Data View):**\n\n```json\n{\n  \"type\": \"metric\",\n  \"data_source\": { \"type\": \"data_view_reference\", \"ref_id\": \"90943e30-9a47-11e8-b64d-95841ca0b247\" },\n  \"metrics\": [{ \"type\": \"primary\", \"operation\": \"count\", \"label\": \"Total Requests\" }]\n}\n```\n\n**Metric (ES|QL):**\n\n```json\n{\n  \"type\": \"metric\",\n  \"data_source\": { \"type\": \"esql\", \"query\": \"FROM logs | STATS count = COUNT()\" },\n  \"metrics\": [{ \"type\": \"primary\", \"column\": \"count\", \"label\": \"Total Requests\" }]\n}\n```\n\n**XY Bar Chart (Data View):**\n\n```json\n{\n  \"title\": \"Top Hosts\",\n  \"type\": \"xy\",\n  \"axis\": { \"x\": { \"title\": { \"visible\": false } }, \"y\": { \"title\": { \"visible\": false } } },\n  \"layers\": [\n    {\n      \"type\": \"bar_horizontal\",\n      \"data_source\": { \"type\": \"data_view_reference\", \"ref_id\": \"90943e30-9a47-11e8-b64d-95841ca0b247\" },\n      \"x\": { \"operation\": \"terms\", \"fields\": [\"host.keyword\"], \"limit\": 10 },\n      \"y\": [{ \"operation\": \"count\" }]\n    }\n  ]\n}\n```\n\n**XY Time Series (ES|QL):**\n\n```json\n{\n  \"title\": \"Requests Over Time\",\n  \"type\": \"xy\",\n  \"axis\": {\n    \"x\": { \"title\": { \"visible\": false }, \"scale\": \"temporal\", \"domain\": { \"type\": \"fit\", \"rounding\": false } },\n    \"y\": { \"title\": { \"visible\": false } }\n  },\n  \"layers\": [\n    {\n      \"type\": \"line\",\n      \"data_source\": {\n        \"type\": \"esql\",\n        \"query\": \"FROM logs | WHERE @timestamp \u003C= ?_tend AND @timestamp > ?_tstart | STATS count = COUNT() BY BUCKET(@timestamp, 75, ?_tstart, ?_tend)\"\n      },\n      \"x\": { \"column\": \"BUCKET(@timestamp, 75, ?_tstart, ?_tend)\", \"label\": \"@timestamp\" },\n      \"y\": [{ \"column\": \"count\" }]\n    }\n  ]\n}\n```\n\n> **Tip:** Always hide axis titles when the panel title is descriptive. Use `bar_horizontal` for categorical data with\n> long labels. Use `axis` for axis configuration.\n\n## Full Documentation\n\n- [Dashboard API Reference](references\u002Fdashboard-api-reference.md) — Dashboard endpoints and schemas\n- [Visualizations API Reference](references\u002Fvisualizations-api-reference.md) — Visualization endpoints\n- [Chart Types Reference](references\u002Fchart-types-reference.md) — Detailed schemas for each chart type\n- [Example Definitions](assets\u002F) — Ready-to-use definitions\n\n### Key Example Files\n\nSee `assets\u002F` for ready-to-use definitions: `demo-dashboard.json`, `dashboard-with-visualizations.json`,\n`metric-esql.json`, `bar-chart-esql.json`, `line-chart-timeseries.json`.\n\n## Common Issues\n\n| Error                   | Solution                                                                    |\n| ----------------------- | --------------------------------------------------------------------------- |\n| \"401 Unauthorized\"      | Check KIBANA_USERNAME\u002FPASSWORD or KIBANA_API_KEY                            |\n| \"404 Not Found\"         | Verify dashboard\u002Fvisualization ID exists                                    |\n| \"409 Conflict\"          | Dashboard\u002Fviz already exists; delete first or use update                    |\n| Schema validation error | Ensure column names match query output; use `{ column: 'name' }` for ES\\|QL |\n| Metric chart structure  | Requires `metrics` array: `[{ type: 'primary', ... }]`                      |\n| XY chart fails          | Put `data_source` inside each layer, use `axis` (singular)                  |\n| ref_id panels missing   | Prefer inline definitions (properties in `config`) over `ref_id`            |\n\n## Guidelines\n\n1. **Design for density** — Operational dashboards must show 8-12 panels above the fold (within the first 24 rows). Use\n   compact panel heights: metrics MUST be `h=4` to `h=6`, and charts MUST be `h=8` to `h=12`.\n2. **Never use Markdown for titles\u002Fheaders** — Do NOT add `markdown` panels to act as dashboard titles or section\n   dividers. This wastes critical vertical space. Use descriptive panel titles on the charts themselves.\n3. **Prioritize above the fold** — Primary KPIs and key trends must be placed at `y=0`. Deep-dives and data tables\n   should be placed below the charts.\n4. **Use descriptive chart titles, hide axis titles** — Write titles that explain what the chart shows (e.g., \"Requests\n   by Response Code\"). A good panel title makes axis titles redundant. Always set `axis.x.title.visible: false` and\n   `axis.y.title.visible: false`.\n\n5. **Choose the right dataset type** — Use `data_view_reference` for simple aggregations, `esql` for complex queries\n6. **Inline definitions** — Prefer inline properties in `config` over `config.ref_id` for portable dashboards\n7. **Test connection first** — Run `node scripts\u002Fkibana-dashboards.js test` before creating resources\n8. **Get existing examples** — Use `vis get \u003Cid>` to see the exact schema for different chart types (the CLI subcommand\n   is `vis`)\n9. **Avoid redundant metric labels** — For ES|QL metrics, avoid using both a panel title and an inner metric label, as\n   it wastes space. Set the panel `title` to `\"\"` and configure the human-readable label by aliasing the ES|QL column\n   name using backticks (e.g., ``STATS `Total Requests` = COUNT()`` and `\"column\": \"Total Requests\"`).\n10. **Format numbers with units** — Use the `format` property on metrics and y-axis columns to display proper units\n    instead of raw numbers. Types: `bytes`, `bits`, `number`, `percent`, `duration`, `custom`. Example:\n    `\"format\": { \"type\": \"bytes\", \"decimals\": 0 }`. See [Chart Types Reference](references\u002Fchart-types-reference.md) for\n    the full format table.\n\n### Schema Differences: Data View vs ES|QL\n\n| Aspect              | Data View                                             | ES\\|QL                                            |\n| ------------------- | ----------------------------------------------------- | ------------------------------------------------- |\n| **Dataset**         | `{ type: 'data_view_reference', ref_id: '...' }`      | `{ type: 'esql', query: '...' }`                  |\n| **Metric chart**    | `metrics: [{ type: 'primary', operation: 'count' }]`  | `metrics: [{ type: 'primary', column: 'col' }]`   |\n| **XY columns**      | `{ operation: 'terms', fields: ['host'], limit: 10 }` | `{ column: 'host' }`                              |\n| **Static values**   | `{ operation: 'static_value', value: 100 }`           | Use `EVAL` in query (see below)                   |\n| **XY data_source**  | Inside each layer                                     | Inside each layer                                 |\n| **Tagcloud**        | `tag_by: { operation: 'terms', ... }`                 | `tag_by: { column: '...' }`                       |\n| **Datatable props** | `metrics`, `rows` arrays                              | `metrics`, `rows` arrays with `{ column: '...' }` |\n\n> **Key Pattern:** ES|QL uses `{ column: 'column_name' }` to reference columns from the query result. The aggregation\n> happens in the ES|QL query itself. Use `data_source` for all data source configuration.\n>\n> **Data source types:** Use `data_view_reference` (with `ref_id`) for saved data views, `data_view_spec` (with\n> `index_pattern`) for ad-hoc index patterns, and `esql` for ES|QL queries.\n\n### ES|QL: Time Bucketing\n\nUse `BUCKET(@timestamp, n, ?_tstart, ?_tend)` for time series charts. The numeric argument is the target number of\nbuckets. Kibana injects `?_tstart`\u002F`?_tend` automatically. Do **not** reassign the result — use the full expression\n`BUCKET(@timestamp, 75, ?_tstart, ?_tend)` as both the `BY` clause and the `column` reference. Set `\"label\"` to provide\na friendly display name:\n\n```json\n\"x\": { \"column\": \"BUCKET(@timestamp, 75, ?_tstart, ?_tend)\", \"label\": \"@timestamp\" }\n```\n\n**Important:** To get a proper multilevel time axis (e.g., \"9th \u002F April 2026 \u002F 10th\") instead of raw timestamp labels,\nyou must set `\"scale\": \"temporal\"` on the x-axis:\n\n```json\n\"axis\": {\n  \"x\": { \"scale\": \"temporal\", \"domain\": { \"type\": \"fit\", \"rounding\": false } }\n}\n```\n\nWithout `\"scale\": \"temporal\"`, Kibana treats the bucket column as categorical text and renders unsorted, verbose\ntimestamp strings.\n\n```esql\nFROM logs | WHERE @timestamp \u003C= ?_tend AND @timestamp > ?_tstart | STATS count = COUNT(*) BY BUCKET(@timestamp, 75, ?_tstart, ?_tend)\n```\n\n> **Note:** `BUCKET(@timestamp, n, ?_tstart, ?_tend)` requires a `WHERE` clause with `?_tstart`\u002F`?_tend` bounds (Kibana\n> injects these). Alternatively, use `BUCKET(@timestamp, 1 hour)` with a fixed duration — this does not require\n> parameters but won't auto-scale.\n\n### ES|QL: Extracting Date Parts\n\nUse `DATE_EXTRACT(part, date)` with ES|QL part names (not SQL keywords). The part string must be double-quoted. Common\nparts: `\"hour_of_day\"`, `\"day_of_week\"`, `\"day_of_month\"`, `\"month_of_year\"`, `\"year\"`, `\"day_of_year\"`.\n\n```esql\nFROM logs | STATS count = COUNT() BY hour = DATE_EXTRACT(\"hour_of_day\", @timestamp), day = DATE_EXTRACT(\"day_of_week\", @timestamp)\n```\n\n### ES|QL: Creating Static\u002FConstant Values\n\nES|QL does not support `static_value` operations. Instead, create constant columns using `EVAL`:\n\n```esql\nFROM logs | STATS count = COUNT() | EVAL max_value = 20000, goal = 15000\n```\n\nThen reference with `{ \"column\": \"max_value\" }`. 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Use to list past races, get a single race's overall (per-task) results, chart a metric's trend across multiple runs, compare two races, or check whether a run converged — e.g. \"show me recent geonames races\", \"what's the service_time trend for nyc_taxis over the last 30 days?\", \"compare these two race-ids\". Applies when datastore.type = elasticsearch is set in ~\u002F.rally\u002Frally.ini.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8191,8194,8197,8198],{"name":8192,"slug":8193,"type":15},"Analytics","analytics",{"name":8195,"slug":8196,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":8199,"slug":8200,"type":15},"Performance","performance",2027,"https:\u002F\u002Fgithub.com\u002Felastic\u002Frally","2026-07-12T07:46:38.54144",{"slug":8205,"name":8205,"fn":8206,"description":8207,"org":8208,"tags":8209,"stars":8201,"repoUrl":8202,"updatedAt":8220},"developing-rally","develop and debug Rally source code","Work on Rally's own codebase, not running benchmarks with it. Use when setting up the dev environment, running Rally's tests or linters, navigating its source, debugging Rally's own code, or making changes to Rally itself.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8210,8213,8214,8217],{"name":8211,"slug":8212,"type":15},"Debugging","debugging",{"name":9,"slug":8,"type":15},{"name":8215,"slug":8216,"type":15},"Engineering","engineering",{"name":8218,"slug":8219,"type":15},"Local Development","local-development","2026-07-12T07:46:35.976807",{"slug":8222,"name":8222,"fn":8223,"description":8224,"org":8225,"tags":8226,"stars":8201,"repoUrl":8202,"updatedAt":8235},"running-benchmarks","run Rally benchmarks against Elasticsearch","Run Rally benchmarks (races) against Elasticsearch — an existing\u002Fexternal cluster or a Rally-provisioned distribution — and read the summary report. Use when running a race (any pipeline, track, challenge, target-hosts, or auth) or when interpreting throughput, latency, and service_time results.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8227,8228,8231,8232],{"name":9,"slug":8,"type":15},{"name":8229,"slug":8230,"type":15},"Elasticsearch","elasticsearch",{"name":8199,"slug":8200,"type":15},{"name":8233,"slug":8234,"type":15},"Testing","testing","2026-07-12T07:46:37.277964",{"slug":8237,"name":8237,"fn":8238,"description":8239,"org":8240,"tags":8241,"stars":23,"repoUrl":24,"updatedAt":8252},"cloud-access-management","manage Elastic Cloud organization access","Manage Elastic Cloud organization access: invite users, assign roles to Serverless projects, and create or revoke Cloud API keys. Use when granting, modifying, or auditing user access.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8242,8245,8246,8249],{"name":8243,"slug":8244,"type":15},"Cloud","cloud",{"name":9,"slug":8,"type":15},{"name":8247,"slug":8248,"type":15},"Operations","operations",{"name":8250,"slug":8251,"type":15},"Permissions","permissions","2026-07-12T07:46:44.946285",{"slug":8254,"name":8254,"fn":8255,"description":8256,"org":8257,"tags":8258,"stars":23,"repoUrl":24,"updatedAt":8264},"cloud-create-project","create Elastic Cloud Serverless projects","Creates Elastic Cloud Serverless projects (Elasticsearch, Observability, or Security) via the REST API, saves credentials to file, and bootstraps a scoped Elasticsearch API key. Use when creating a new serverless project, provisioning a search or observability environment, or spinning up a new Elastic Cloud project.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8259,8260,8263],{"name":8243,"slug":8244,"type":15},{"name":8261,"slug":8262,"type":15},"Deployment","deployment",{"name":8229,"slug":8230,"type":15},"2026-07-12T07:46:42.353362",{"slug":8266,"name":8266,"fn":8267,"description":8268,"org":8269,"tags":8270,"stars":23,"repoUrl":24,"updatedAt":8274},"cloud-manage-project","manage Elastic Cloud Serverless projects","Manages existing Elastic Cloud Serverless projects: list, get, update, delete, reset credentials, resume, and load saved credentials. Connects to existing projects by resolving endpoints and acquiring scoped Elasticsearch API keys. Use when performing day-2 operations on serverless projects, connecting to an existing project, loading or resetting project credentials, or looking up project details.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8271,8272,8273],{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8247,"slug":8248,"type":15},"2026-07-12T07:46:41.097412",{"slug":8276,"name":8276,"fn":8277,"description":8278,"org":8279,"tags":8280,"stars":23,"repoUrl":24,"updatedAt":8289},"cloud-network-security","manage Elastic Cloud network security","Manage Serverless network security (traffic filters): create, update, and delete IP filters and AWS PrivateLink VPC filters. Use when restricting network access or configuring private connectivity.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8281,8282,8283,8286],{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8284,"slug":8285,"type":15},"Networking","networking",{"name":8287,"slug":8288,"type":15},"Security","security","2026-07-12T07:46:43.675992",{"slug":8291,"name":8291,"fn":8292,"description":8293,"org":8294,"tags":8295,"stars":23,"repoUrl":24,"updatedAt":8301},"cloud-setup","configure Elastic Cloud authentication","Configures Elastic Cloud authentication and environment defaults. Use when setting up EC_API_KEY, configuring Cloud API access, or when another cloud skill requires credentials.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8296,8299,8300],{"name":8297,"slug":8298,"type":15},"Authentication","authentication",{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},"2026-07-12T07:46:39.783105",{"slug":8303,"name":8303,"fn":8304,"description":8305,"org":8306,"tags":8307,"stars":23,"repoUrl":24,"updatedAt":8316},"elasticsearch-audit","configure Elasticsearch security audit logs","Enable, configure, and query Elasticsearch security audit logs. Use when the task involves audit logging setup, event filtering, or investigating security incidents like failed logins.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8308,8311,8312,8315],{"name":8309,"slug":8310,"type":15},"Audit","audit",{"name":8229,"slug":8230,"type":15},{"name":8313,"slug":8314,"type":15},"Logs","logs",{"name":8287,"slug":8288,"type":15},"2026-07-12T07:47:35.092599",{"slug":8318,"name":8318,"fn":8319,"description":8320,"org":8321,"tags":8322,"stars":23,"repoUrl":24,"updatedAt":8327},"elasticsearch-authn","configure Elasticsearch authentication realms","Authenticate to Elasticsearch using native, file-based, LDAP\u002FAD, SAML, OIDC, Kerberos, JWT, or certificate realms. Use when connecting with credentials, choosing a realm, or managing API keys. Assumes the target realms are already configured.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8323,8324,8325,8326],{"name":8297,"slug":8298,"type":15},{"name":9,"slug":8,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8287,"slug":8288,"type":15},"2026-07-12T07:47:41.474547",{"slug":8329,"name":8329,"fn":8330,"description":8331,"org":8332,"tags":8333,"stars":23,"repoUrl":24,"updatedAt":8340},"elasticsearch-authz","manage Elasticsearch RBAC and security roles","Manage Elasticsearch RBAC: native users, roles, role mappings, document- and field-level security. Use when creating users or roles, assigning privileges, or mapping external realms like LDAP\u002FSAML.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8334,8335,8336,8339],{"name":9,"slug":8,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8337,"slug":8338,"type":15},"RBAC","rbac",{"name":8287,"slug":8288,"type":15},"2026-07-12T07:47:36.394177",{"slug":8342,"name":8342,"fn":8343,"description":8344,"org":8345,"tags":8346,"stars":23,"repoUrl":24,"updatedAt":8353},"elasticsearch-esql","query Elasticsearch data with ES|QL","Execute ES|QL (Elasticsearch Query Language) queries, use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ES|QL results.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8347,8348,8349,8350],{"name":8192,"slug":8193,"type":15},{"name":8195,"slug":8196,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8351,"slug":8352,"type":15},"SQL","sql","2026-07-12T07:47:40.249533",86,{"items":8356,"total":2970},[8357,8364,8370,8376,8383,8389,8396],{"slug":8237,"name":8237,"fn":8238,"description":8239,"org":8358,"tags":8359,"stars":23,"repoUrl":24,"updatedAt":8252},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8360,8361,8362,8363],{"name":8243,"slug":8244,"type":15},{"name":9,"slug":8,"type":15},{"name":8247,"slug":8248,"type":15},{"name":8250,"slug":8251,"type":15},{"slug":8254,"name":8254,"fn":8255,"description":8256,"org":8365,"tags":8366,"stars":23,"repoUrl":24,"updatedAt":8264},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8367,8368,8369],{"name":8243,"slug":8244,"type":15},{"name":8261,"slug":8262,"type":15},{"name":8229,"slug":8230,"type":15},{"slug":8266,"name":8266,"fn":8267,"description":8268,"org":8371,"tags":8372,"stars":23,"repoUrl":24,"updatedAt":8274},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8373,8374,8375],{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8247,"slug":8248,"type":15},{"slug":8276,"name":8276,"fn":8277,"description":8278,"org":8377,"tags":8378,"stars":23,"repoUrl":24,"updatedAt":8289},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8379,8380,8381,8382],{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8284,"slug":8285,"type":15},{"name":8287,"slug":8288,"type":15},{"slug":8291,"name":8291,"fn":8292,"description":8293,"org":8384,"tags":8385,"stars":23,"repoUrl":24,"updatedAt":8301},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8386,8387,8388],{"name":8297,"slug":8298,"type":15},{"name":8243,"slug":8244,"type":15},{"name":8229,"slug":8230,"type":15},{"slug":8303,"name":8303,"fn":8304,"description":8305,"org":8390,"tags":8391,"stars":23,"repoUrl":24,"updatedAt":8316},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8392,8393,8394,8395],{"name":8309,"slug":8310,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8313,"slug":8314,"type":15},{"name":8287,"slug":8288,"type":15},{"slug":8318,"name":8318,"fn":8319,"description":8320,"org":8397,"tags":8398,"stars":23,"repoUrl":24,"updatedAt":8327},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8399,8400,8401,8402],{"name":8297,"slug":8298,"type":15},{"name":9,"slug":8,"type":15},{"name":8229,"slug":8230,"type":15},{"name":8287,"slug":8288,"type":15}]