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dashboard-builder

构建可回答运维人员实际问题的监控仪表板，适用于 Grafana、SigNoz 等平台。在将指标转化为可工作的仪表板而非虚荣面板时使用。

dashboard-builder 是什么？

该技能指导你构建真正可操作的监控仪表板，而非仅展示所有指标的虚荣面板。其核心理念是从运维问题出发，确保每个面板都能回答诸如“系统是否健康？”、“瓶颈在哪里？”、“发生了什么变化？”等问题。它支持 Grafana、SigNoz 等常见平台，并提供了针对 Elasticsearch、Kafka、API 网关等服务的示例面板集。工作流程包括定义操作问题、研究目标平台架构、构建最小可用面板，并剔除无用面板。最后还附带质量检查清单，确保仪表板结构清晰、阈值有意义、变量配置合理。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/dashboard-builder
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Dashboard Builder

当任务是构建一个可供运维人员操作的仪表板时使用此技能。

目标不是“展示所有指标”。目标是回答以下问题：

  • 系统是否健康？
  • 瓶颈在哪里？
  • 发生了什么变化？
  • 应该采取什么操作？

使用时机

  • “构建一个 Kafka 监控仪表板”
  • “为 Elasticsearch 创建一个 Grafana 仪表板”
  • “为此服务制作一个 SigNoz 仪表板”
  • “将这个指标列表转化为一个真正的运维仪表板”

限制条件

  • 不要从视觉布局开始；要从运维问题开始
  • 不要仅仅因为存在就包含所有可用指标
  • 不要在没有结构的情况下混合健康、吞吐量和资源面板
  • 不要交付没有标题、单位和合理阈值的面板

工作流程

1. 定义操作问题

围绕以下方面组织：

  • 健康 / 可用性
  • 延迟 / 性能
  • 吞吐量 / 容量
  • 饱和度 / 资源
  • 服务特定风险

2. 研究目标平台架构

首先检查现有仪表板：

  • JSON 结构
  • 查询语言
  • 变量
  • 阈值样式
  • 布局分区

3. 构建最小可用面板

推荐结构：

  1. 概览
  2. 性能
  3. 资源
  4. 服务特定部分

4. 剔除虚荣面板

每个面板都应回答一个真实的问题。如果不能，就移除它。

示例面板集

Elasticsearch

  • 集群健康
  • 分片分配
  • 搜索延迟
  • 索引速率
  • JVM 堆 / GC

Kafka

  • 代理数
  • 副本不足的分区
  • 消息流入/流出
  • 消费者滞后
  • 磁盘和网络压力

API 网关 / 入口

  • 请求速率
  • p50 / p95 / p99 延迟
  • 错误率
  • 上游健康
  • 活跃连接

质量检查清单

  • 有效的仪表板 JSON
  • 清晰的分组
  • 标题和单位已存在
  • 阈值/状态颜色有意义
  • 存在用于常见过滤器的变量
  • 默认时间范围和刷新设置合理
  • 没有对操作无价值的虚荣面板

相关技能

  • research-ops
  • backend-patterns
  • terminal-ops

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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