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仪表盘构建器

为 Grafana、SigNoz 等平台构建能回答真实运维问题的监控仪表盘。适用于将指标转化为可用的仪表盘而非虚荣展板。

仪表盘构建器 是什么？

Dashboard Builder 是一个专注于构建可操作性监控仪表盘的 AI Agent 技能。其核心理念是仪表盘的目标不是“展示所有指标”，而是回答真实运维人员的问题：是否健康？瓶颈在哪里？发生了什么变化？需要采取什么行动？该技能支持 Grafana、SigNoz 等常见平台，并强调从运维问题出发而非视觉布局。工作流程包括定义运维问题、研究目标平台架构、构建最小有用仪表盘并移除虚荣面板。技能还提供了 Elasticsearch、Kafka、API 网关等常见服务的最佳实践面板示例，以及完整的质量检查清单，确保每个面板都有实际运维价值。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/dashboard-builder
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仪表盘构建器 是做什么的？

当任务需要构建一个人们可从中进行运维操作的仪表盘时使用此技能。

目标不是“展示每个指标”。目标是回答以下问题：

  • 它健康吗？
  • 瓶颈在哪里？
  • 发生了什么变化？
  • 应该采取什么行动？

使用时机

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

约束条件

  • 不要从视觉布局开始；要从运维问题开始
  • 不要仅仅因为指标存在就包含所有可用指标
  • 不要无结构地混合健康、吞吐量和资源面板
  • 不要发布没有标题、单位和合理阈值的面板

工作流程

1. 定义运维问题

围绕以下方面组织：

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

2. 研究目标平台架构

首先检查现有仪表盘：

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

3. 构建最小有用仪表盘

推荐结构：

  1. 概览
  2. 性能
  3. 资源
  4. 服务特定分区

4. 移除虚荣面板

每个面板都应回答一个真实问题。如果不能，则将其移除。

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

  • 代理数量
  • 未复制分区
  • 消息输入 / 输出
  • 消费者积压
  • 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
  • 活动连接

质量检查清单

  • 有效的仪表盘 JSON
  • 清晰的分区分组
  • 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间隔合理
  • 没有无运维价值的虚荣面板

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳：このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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openclaw/openclaw

Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

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obra/superpowers

Obra superpowers agent skills.

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affaan-m/ECC

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...

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