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grafana-dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=wshobson-agents-plugins-observability-monitoring-skills-grafana-dashboards-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name grafana-dashboards description Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. Purpose Design effective Grafana dashboards for monitoring applications, infrastructure, and business metrics. When to Use 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 { "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) { "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 { "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 { "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 { "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 { "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=~"$service"}[5m])) Alerts in Dashboards { "alert" : { "name" : "High Error Rate" , "conditions" : [ { "evaluator" : { "params" : [ 5 ] , "type" : "gt" } , "operator" : { "type" : "and" } , "query" : { "params" : [ "A" , "5m" , "now" ] } , "reducer" : { "type" : "avg" } , "type" : "query" } ] , "executionErrorState" : "alerting" , "for" : "5m" , "frequency" : "1m" , "message" : "Error rate is above 5%" , "noDataState" : "no_data" , "notifications" : [ { "uid" : "slack-channel" } ] } } Dashboard Provisioning dashboards.yml: apiVersion: 1 providers: - name: "default" orgId: 1 folder: "General" type: file disableDeletion: false updateIntervalSeconds: 10 allowUiUpdates: true options: path: /etc/grafana/dashboards Common Dashboard Patterns Infrastructure Dashboard Key Panels: CPU utilization per node Memory usage per node Disk I/O Network traffic Pod count by namespace Node status Reference: See assets/infrastructure-dashboard.json Database Dashboard Key Panels: Queries per second Connection pool usage Query latency (P50, P95, P99) Active connections Database size Replication lag Slow queries Reference: See assets/database-dashboard.json Application Dashboard Key Panels: Request rate Error rate Response time (percentiles) Active users/sessions Cache hit rate Queue length Best Practices Start with templates (Grafana community dashboards) Use consistent naming for panels and variables Group related metrics in rows Set appropriate time ranges (default: Last 6 hours) Use variables for flexibility Add panel descriptions for context Configure units correctly Set meaningful thresholds for colors Use consistent colors across dashboards Test with different time ranges Dashboard as Code Terraform Provisioning resource "grafana_dashboard" "api_monitoring" { config_json = file("${path.module}/dashboards/api-monitoring.json") folder = grafana_folder.monitoring.id } resource "grafana_folder" "monitoring" { title = "Production Monitoring" } Ansible Provisioning - name: Deploy Grafana dashboards copy: src: " {{ item }} " dest: /etc/grafana/dashboards/ with_fileglob: - "dashboards/*.json" notify: restart grafana Related Skills prometheus-configuration - For metric collection slo-implementation - For SLO dashboards
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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