Communitygithub.com

儀表板建置器

為 Grafana、SigNoz 等平台建置能回答實際運維人員問題的監控儀表板。當你需要將指標轉化為可運作的儀表板，而非華而不實的展示板時使用。

儀表板建置器 是什麼？

此技能旨在協助開發者或 AI 代理建置以運維為導向的監控儀表板，支援 Grafana 與 SigNoz 等主流平台。其核心理念是避免盲目塞入所有可用指標，而是從運維人員的真實問題出發，例如「系統健康嗎？」、「瓶頸在哪裡？」、「發生了什麼變化？」以及「應該採取什麼行動？」。它定義了清晰的建置工作流程，包括定義運維問題、研究目標平台的 JSON 結構與查詢語言、建立最小可用儀表板，並透過「去除虛榮面板」的步驟確保每個面板都有實際價值。此技能還提供 Elasticsearch、Kafka、API 閘道等常見情境的範例面板組，並附有品質檢查清單，能顯著提升監控儀表板的實用性與可操作性。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/dashboard-builder
查看原文→瀏覽所有技能

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

說明文件

儀表板建置器 是做什麼的？

當任務是建置一個可供人員操作的儀表板時，請使用此技能。

目標不是「顯示所有指標」。目標是回答：

  • 它健康嗎？
  • 瓶頸在哪裡？
  • 什麼改變了？
  • 應該採取什麼行動？

使用時機

  • 「建置一個 Kafka 監控儀表板」
  • 「為 Elasticsearch 建立一個 Grafana 儀表板」
  • 「為此服務製作一個 SigNoz 儀表板」
  • 「將此指標列表轉變為真正的運維儀表板」

護欄

  • 不要從視覺佈局開始；從運維問題開始
  • 不要只因某指標存在就包含它
  • 不要在沒有結構的情況下混雜健康、吞吐量和資源面板
  • 不要發布沒有標題、單位和合理閾值的面板

工作流程

1. 定義運維問題

圍繞以下面向組織：

  • 健康 / 可用性
  • 延遲 / 效能
  • 吞吐量 / 流量
  • 飽和度 / 資源
  • 服務特定的風險

2. 研究目標平台綱要

先檢查現有儀表板：

  • JSON 結構
  • 查詢語言
  • 變數
  • 閾值樣式
  • 區段佈局

3. 建置最小可用儀表板

建議的結構：

  1. 概覽
  2. 效能
  3. 資源
  4. 服務特定區段

4. 刪除虛榮面板

每個面板都應回答一個真實問題。如果不能，就移除它。

範例面板組

Elasticsearch

  • 叢集健康
  • 分片分配
  • 搜尋延遲
  • 索引速率
  • JVM 堆積 / GC

Kafka

  • 代理程式數量
  • 複製不足的分區
  • 訊息流入 / 流出
  • 消費者延遲
  • 磁碟與網路壓力

API 閘道 / 入口

  • 請求速率
  • p50 / p95 / p99 延遲
  • 錯誤率
  • 上游健康狀態
  • 活躍連線數

品質檢查清單

  • 有效的儀表板 JSON
  • 清晰的區段分組
  • 標題和單位都存在
  • 閾值/狀態顏色具有意義
  • 存在常用篩選條件的變數
  • 預設時間範圍和重新整理間隔合理
  • 沒有對運維無價值的虛榮面板

相關技能

  • 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.

相關技能

steipete/video-frames

Extract frames or short clips from videos using ffmpeg.

community

steipete/summarize

Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

community

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.

community

obra/superpowers

Obra superpowers agent skills.

community

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...

community

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.

community