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Dashboard-Builder

Erstellen von operativen Dashboards für Grafana, SigNoz und ähnliche Plattformen, die echte Betreiberfragen beantworten. Verwende dies, wenn Metriken in ein funktionierendes Dashboard umgewandelt werden sollen, statt in ein Eitelkeits-Dashboard.

Was ist Dashboard-Builder?

Das Dashboard-Builder-Skill ist dafür konzipiert, operative Dashboards zu erstellen, die echte Betreiberfragen beantworten, anstatt einfach alle verfügbaren Metriken anzuzeigen. Es unterstützt Plattformen wie Grafana, SigNoz und ähnliche und folgt einem fragengetriebenen Ansatz: Statt mit dem visuellen Layout zu beginnen, werden zunächst die Betriebsfragen definiert (Gesundheit, Engpässe, Änderungen, Handlungsbedarf). Der Workflow umfasst fünf Schritte: Definition der Betriebsfragen, Studium des Zielplattform-Schemas, Aufbau des minimal nützlichen Dashboards, Entfernen von Eitelkeits-Panels und Qualitätssicherung. Das Skill bietet vordefinierte Beispiel-Panel-Sets für Elasticsearch, Kafka und API-Gateways sowie eine Qualitätscheckliste, um sicherzustellen, dass jedes Dashboard klare Titel, Einheiten und sinnvolle Schwellwerte enthält. Es ist eng verwandt mit den Skills `research-ops`, `backend-patterns` und `terminal-ops`.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/dashboard-builder
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Dokumentation

Dashboard Builder

Verwende dies, wenn die Aufgabe darin besteht, ein Dashboard zu erstellen, von dem aus Menschen arbeiten können.

Das Ziel ist nicht "jede Metrik anzeigen". Das Ziel ist, zu beantworten:

  • ist es gesund?
  • wo ist der Engpass?
  • was hat sich geändert?
  • welche Maßnahme sollte jemand ergreifen?

Wann verwenden

  • "Erstelle ein Kafka-Überwachungs-Dashboard"
  • "Erstelle ein Grafana-Dashboard für Elasticsearch"
  • "Erstelle ein SigNoz-Dashboard für diesen Dienst"
  • "Wandle diese Metrikliste in ein echtes operatives Dashboard um"

Leitplanken

  • nicht mit dem visuellen Layout beginnen; mit Betreiberfragen beginnen
  • nicht jede verfügbare Metrik einbeziehen, nur weil sie existiert
  • Gesundheits-, Durchsatz- und Ressourcen-Panels nicht ohne Struktur mischen
  • keine Panels ohne Titel, Einheiten und sinnvolle Schwellwerte ausliefern

Arbeitsablauf

1. Die Betriebsfragen definieren

Organisieren um:

  • Gesundheit / Verfügbarkeit
  • Latenz / Leistung
  • Durchsatz / Volumen
  • Sättigung / Ressourcen
  • dienstspezifisches Risiko

2. Das Schema der Zielplattform studieren

Zuerst bestehende Dashboards inspizieren:

  • JSON-Struktur
  • Abfragesprache
  • Variablen
  • Schwellwert-Styling
  • Abschnittslayout

3. Das minimal nützliche Dashboard bauen

Empfohlene Struktur:

  1. Übersicht
  2. Leistung
  3. Ressourcen
  4. dienstspezifischer Abschnitt

4. Eitelkeits-Panels entfernen

Jedes Panel sollte eine echte Frage beantworten. Wenn nicht, entferne es.

Beispiel-Panel-Sets

Elasticsearch

  • Cluster-Gesundheit
  • Shard-Verteilung
  • Suchlatenz
  • Indexierungsrate
  • JVM-Heap / GC

Kafka

  • Broker-Anzahl
  • unter-replizierte Partitionen
  • Nachrichten rein / raus
  • Consumer-Lag
  • Festplatten- und Netzwerkdruck

API-Gateway / Ingress

  • Anfragenrate
  • p50 / p95 / p99 Latenz
  • Fehlerrate
  • Upstream-Gesundheit
  • aktive Verbindungen

Qualitäts-Checkliste

  • gültiges Dashboard-JSON
  • klare Abschnittsgruppierung
  • Titel und Einheiten vorhanden
  • Schwellwerte/Statusfarben sind sinnvoll
  • Variablen für gängige Filter vorhanden
  • Standardzeitbereich und Aktualisierung sind sinnvoll
  • keine Eitelkeits-Panels ohne operativen Wert

Verwandte Skills

  • 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.

Verwandte Skills

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