opensearch-dashboard

name	opensearch-dashboard
description	Generate OpenSearch Dashboards (Kibana) saved objects — index patterns, visualizations, and dashboards as JSON. Use when the user asks to create dashboards, charts, or visualizations for OpenSearch/Elasticsearch/Kibana.

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" 
       
    
  
   
         
  

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