Opensearch Dashboard

Skill: OpenSearch Dashboard & Visualization Generator

Generate exportable OpenSearch Dashboards / Kibana saved objects (index patterns, visualizations, dashboards) as NDJSON for import.

Trigger

When the user asks to create, generate, or design OpenSearch/Kibana dashboards, visualizations, or index patterns.

Prerequisites

• [ ] Index name or pattern known (e.g., logs-*, orders-*)
• [ ] Field names and types known (or mapping available)
• [ ] Visualization requirements identified (chart type, metrics, dimensions)

Steps

Step 1: Identify Index Pattern

• [ ] Get the index name or pattern from the user
• [ ] List key fields and their types (keyword, text, date, long, float, boolean, geo_point)
• [ ] Identify the time field (usually @timestamp or created_at)
• [ ] Note any nested or object fields

{
  "type": "index-pattern",
  "attributes": {
    "title": "logs-*",
    "timeFieldName": "@timestamp",
    "fields": "[]"
  }
}

Step 2: Determine Visualization Types

| Chart Type | Best For | OpenSearch Vis Type | |-----------|----------|-------------------| | Line chart | Trends over time | line | | Bar chart | Comparisons, distributions | histogram | | Pie chart | Proportions | pie | | Area chart | Cumulative trends | area | | Data table | Detailed breakdowns | table | | Metric | Single KPI value | metric | | Gauge | Value against threshold | gauge | | Heat map | Density / correlation | heatmap | | Markdown | Text panels, notes | markdown | | TSVB | Advanced time series | metrics | | Vega | Custom visualizations | vega |

Step 3: Design Each Visualization

For each visualization:

• [ ] Choose chart type from the table above
• [ ] Define metric aggregation (count, sum, avg, min, max, cardinality, percentiles)
• [ ] Define bucket aggregation (date_histogram, terms, range, histogram, filters)
• [ ] Set appropriate time intervals
• [ ] Define split series or sub-aggregations if needed
• [ ] Choose colors and labels

Metric Aggregations

{
  "id": "1",
  "enabled": true,
  "type": "count",
  "params": {},
  "schema": "metric"
}

Common metrics:

• count — number of documents
• avg / sum / min / max — field statistics
• cardinality — unique count
• percentiles — p50, p95, p99
• top_hits — sample documents

Bucket Aggregations

{
  "id": "2",
  "enabled": true,
  "type": "date_histogram",
  "params": {
    "field": "@timestamp",
    "interval": "auto",
    "min_doc_count": 1
  },
  "schema": "segment"
}

Common buckets:

• date_histogram — time buckets (interval: 1m, 5m, 1h, 1d, auto)
• terms — top N values of a field
• range — custom numeric ranges
• filters — named query filters
• histogram — fixed-width numeric buckets

Step 4: Generate Visualization JSON

Template for a visualization saved object:

{
  "type": "visualization",
  "id": "[unique-id]",
  "attributes": {
    "title": "[Visualization Title]",
    "visState": "{\"title\":\"[title]\",\"type\":\"[vis-type]\",\"aggs\":[...],\"params\":{...}}",
    "uiStateJSON": "{}",
    "description": "[what this shows]",
    "kibanaSavedObjectMeta": {
      "searchSourceJSON": "{\"index\":\"[index-pattern-id]\",\"query\":{\"query\":\"\",\"language\":\"kuery\"},\"filter\":[]}"
    }
  }
}

Step 5: Compose Dashboard Layout

• [ ] Arrange visualizations in a grid layout
• [ ] Group related metrics together
• [ ] Place summary/KPI panels at the top
• [ ] Place detailed breakdowns below
• [ ] Add markdown panels for section headers or notes

Dashboard saved object:

{
  "type": "dashboard",
  "id": "[dashboard-id]",
  "attributes": {
    "title": "[Dashboard Title]",
    "description": "[what this dashboard monitors]",
    "panelsJSON": "[{\"gridData\":{\"x\":0,\"y\":0,\"w\":24,\"h\":15,\"i\":\"1\"},\"panelIndex\":\"1\",\"embeddableConfig\":{},\"panelRefName\":\"panel_0\"}]",
    "optionsJSON": "{\"hidePanelTitles\":false,\"useMargins\":true}",
    "timeRestore": true,
    "timeTo": "now",
    "timeFrom": "now-24h",
    "refreshInterval": {
      "pause": false,
      "value": 30000
    },
    "kibanaSavedObjectMeta": {
      "searchSourceJSON": "{\"query\":{\"query\":\"\",\"language\":\"kuery\"},\"filter\":[]}"
    }
  },
  "references": [
    {"name": "panel_0", "type": "visualization", "id": "[vis-id]"}
  ]
}

Step 6: Generate NDJSON Export

• [ ] Combine all saved objects (index pattern + visualizations + dashboard)
• [ ] Output as NDJSON (one JSON object per line)
• [ ] Include references between objects

{"type":"index-pattern","id":"...","attributes":{...}}
{"type":"visualization","id":"...","attributes":{...},"references":[...]}
{"type":"dashboard","id":"...","attributes":{...},"references":[...]}

Step 7: Provide Import Instructions

• [ ] Instructions for importing via Dashboards UI: Stack Management → Saved Objects → Import
• [ ] Instructions for importing via API:

curl -X POST "[OPENSEARCH_DASHBOARDS_URL]/api/saved_objects/_import" \
  -H "osd-xsrf: true" \
  --form file=@dashboard-export.ndjson

Common Dashboard Recipes

Application Monitoring Dashboard

• Row 1: Request rate (metric), Error rate (metric), P99 latency (metric)
• Row 2: Request rate over time (line), Error rate over time (line)
• Row 3: Top endpoints by latency (bar), Status code distribution (pie)
• Row 4: Recent errors table (data table)

Business Metrics Dashboard

• Row 1: Total orders (metric), Revenue (metric), Conversion rate (metric)
• Row 2: Orders over time (area), Revenue over time (line)
• Row 3: Top products (bar), Order status distribution (pie)
• Row 4: Orders by region (heat map or data table)

Infrastructure Dashboard

• Row 1: CPU avg (gauge), Memory avg (gauge), Disk usage (gauge)
• Row 2: CPU over time (area), Memory over time (area)
• Row 3: Network in/out (line), Container restarts (bar)
• Row 4: Top processes by CPU (data table)

Rules

• NEVER include real cluster URLs, credentials, or internal hostnames
• ALWAYS use [OPENSEARCH_DASHBOARDS_URL] as placeholder
• ALWAYS generate valid NDJSON format
• ALWAYS include unique IDs for saved objects (use UUIDs or descriptive slugs)
• Use meaningful visualization titles and descriptions
• Default time range to now-24h unless user specifies otherwise
• Set reasonable refresh intervals (30s for ops, 5m for business)

Completion

Exportable NDJSON file with index pattern, visualizations, and dashboard. Ready to import via UI or API.

If a Step Fails

• Unknown fields: Ask the user for the index mapping or run GET [index]/_mapping
• Complex aggregation: Start with a simple version, iterate
• Too many visualizations: Group into multiple dashboards by concern (ops vs business)
• Import fails: Verify NDJSON format (one object per line, valid JSON), check index pattern exists

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: girijashankarj
• Source: girijashankarj/cursor-handbook
• License: MIT

Install and usage instructions live in the source repository linked above.