--- name: grafana-dashboards-0c5de629ba9c description: "Create and manage production-ready Grafana dashboards for comprehensive system observability." risk: unknown source: community date_added: "2026-02-27" --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated to grafana dashboards - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Purpose Design effective Grafana dashboards for monitoring applications, infrastructure, and business metrics. ## Use this skill when - Visualize Prometheus metrics - Create custom dashboards - Implement SLO dashboards - Monitor infrastructure - Track business KPIs ## Dashboard Design Principles ### 1. Hierarchy of Information ``` ┌─────────────────────────────────────┐ │ Critical Metrics (Big Numbers) │ ├─────────────────────────────────────┤ │ Key Trends (Time Series) │ ├─────────────────────────────────────┤ │ Detailed Metrics (Tables/Heatmaps) │ └─────────────────────────────────────┘ ``` ### 2. RED Method (Services) - **Rate** - Requests per second - **Errors** - Error rate - **Duration** - Latency/response time ### 3. USE Method (Resources) - **Utilization** - % time resource is busy - **Saturation** - Queue length/wait time - **Errors** - Error count ## Dashboard Structure ### API Monitoring Dashboard ```json { "dashboard": { "title": "API Monitoring", "tags": ["api", "production"], "timezone": "browser", "refresh": "30s", "panels": [ { "title": "Request Rate", "type": "graph", "targets": [ { "expr": "sum(rate(http_requests_total[5m])) by (service)", "legendFormat": "{{service}}" } ], "gridPos": {"x": 0, "y": 0, "w": 12, "h": 8} }, { "title": "Error Rate %", "type": "graph", "targets": [ { "expr": "(sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m]))) * 100", "legendFormat": "Error Rate" } ], "alert": { "conditions": [ { "evaluator": {"params": [5], "type": "gt"}, "operator": {"type": "and"}, "query": {"params": ["A", "5m", "now"]}, "type": "query" } ] }, "gridPos": {"x": 12, "y": 0, "w": 12, "h": 8} }, { "title": "P95 Latency", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))", "legendFormat": "{{service}}" } ], "gridPos": {"x": 0, "y": 8, "w": 24, "h": 8} } ] } } ``` **Reference:** See `assets/api-dashboard.json` ## Panel Types ### 1. Stat Panel (Single Value) ```json { "type": "stat", "title": "Total Requests", "targets": [{ "expr": "sum(http_requests_total)" }], "options": { "reduceOptions": { "values": false, "calcs": ["lastNotNull"] }, "orientation": "auto", "textMode": "auto", "colorMode": "value" }, "fieldConfig": { "defaults": { "thresholds": { "mode": "absolute", "steps": [ {"value": 0, "color": "green"}, {"value": 80, "color": "yellow"}, {"value": 90, "color": "red"} ] } } } } ``` ### 2. Time Series Graph ```json { "type": "graph", "title": "CPU Usage", "targets": [{ "expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)" }], "yaxes": [ {"format": "percent", "max": 100, "min": 0}, {"format": "short"} ] } ``` ### 3. Table Panel ```json { "type": "table", "title": "Service Status", "targets": [{ "expr": "up", "format": "table", "instant": true }], "transformations": [ { "id": "organize", "options": { "excludeByName": {"Time": true}, "indexByName": {}, "renameByName": { "instance": "Instance", "job": "Service", "Value": "Status" } } } ] } ``` ### 4. Heatmap ```json { "type": "heatmap", "title": "Latency Heatmap", "targets": [{ "expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)", "format": "heatmap" }], "dataFormat": "tsbuckets", "yAxis": { "format": "s" } } ``` ## Variables ### Query Variables ```json { "templating": { "list": [ { "name": "namespace", "type": "query", "datasource": "Prometheus", "query": "label_values(kube_pod_info, namespace)", "refresh": 1, "multi": false }, { "name": "service", "type": "query", "datasource": "Prometheus", "query": "label_values(kube_service_info{namespace=\"$namespace\"}, service)", "refresh": 1, "multi": true } ] } } ``` ### Use Variables in Queries ``` sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m])) ``` ## Alerts in Dashboards ```json { "alert": { "name": "High Error Rate", "conditions": [ { "evaluator": { "params": [5], "type": "gt" }, "operator": {"type": "and"}, "query": { "params": ["A", "5m", "now"] }, "reducer": {"type": "avg"}, "type": "query" } ], "executionErrorState": "alerting", "for": "5m", "frequency": "1m", "message": "Error rate is above 5%", "noDataState": "no_data", "notifications": [ {"uid": "slack-channel"} ] } } ``` ## Dashboard Provisioning **dashboards.yml:** ```yaml apiVersion: 1 providers: - name: 'default' orgId: 1 folder: 'General' type: file disableDeletion: false updateIntervalSeconds: 10 allowUiUpdates: true options: path: /etc/grafana/dashboards ``` ## Common Dashboard Patterns ### Infrastructure Dashboard **Key Panels:** - CPU utilization per node - Memory usage per node - Disk I/O - Network traffic - Pod count by namespace - Node status **Reference:** See `assets/infrastructure-dashboard.json` ### Database Dashboard **Key Panels:** - Queries per second - Connection pool usage - Query latency (P50, P95, P99) - Active connections - Database size - Replication lag - Slow queries **Reference:** See `assets/database-dashboard.json` ### Application Dashboard **Key Panels:** - Request rate - Error rate - Response time (percentiles) - Active users/sessions - Cache hit rate - Queue length ## Best Practices 1. **Start with templates** (Grafana community dashboards) 2. **Use consistent naming** for panels and variables 3. **Group related metrics** in rows 4. **Set appropriate time ranges** (default: Last 6 hours) 5. **Use variables** for flexibility 6. **Add panel descriptions** for context 7. **Configure units** correctly 8. **Set meaningful thresholds** for colors 9. **Use consistent colors** across dashboards 10. **Test with different time ranges** ## Dashboard as Code ### Terraform Provisioning ```hcl resource "grafana_dashboard" "api_monitoring" { config_json = file("${path.module}/dashboards/api-monitoring.json") folder = grafana_folder.monitoring.id } resource "grafana_folder" "monitoring" { title = "Production Monitoring" } ``` ### Ansible Provisioning ```yaml - name: Deploy Grafana dashboards copy: src: "{{ item }}" dest: /etc/grafana/dashboards/ with_fileglob: - "dashboards/*.json" notify: restart grafana ``` ## Reference Files - `assets/api-dashboard.json` - API monitoring dashboard - `assets/infrastructure-dashboard.json` - Infrastructure dashboard - `assets/database-dashboard.json` - Database monitoring dashboard - `references/dashboard-design.md` - Dashboard design guide ## Related Skills - `prometheus-configuration` - For metric collection - `slo-implementation` - For SLO dashboards ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing. --- Source attribution: ASoudy Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills/skills/grafana-dashboards/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Antigravity User Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ASoudy Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills/skills/grafana-dashboards/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Backend Developer, Data Analyst, DevOps / SRE, Growth Product Manager, Product Manager Specialties: API, Backend Tools: Grafana Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: cc9a0b6e167dd2c078ef5f9f872fa7e50a5cabc5574b55329e1c31711e499e54