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dashboard-builder

Crie painéis de monitoramento que respondam a perguntas reais de operadores para Grafana, SigNoz e plataformas similares. Use quando for transformar métricas em um painel funcional em vez de um quadro de vaidade.

O que é dashboard-builder?

Esta skill auxilia na criação de dashboards operacionais para plataformas como Grafana, SigNoz e similares. O foco não é exibir todas as métricas disponíveis, mas sim responder perguntas concretas de operadores: o serviço está saudável? Onde está o gargalo? O que mudou? Qual ação deve ser tomada? O fluxo de trabalho começa com a definição das perguntas operacionais (saúde, latência, throughput, saturação e riscos específicos), seguido pelo estudo do schema da plataforma alvo. A estrutura recomendada inclui seções de visão geral, desempenho, recursos e seções específicas do serviço. A skill também inclui exemplos de conjuntos de painéis para Elasticsearch, Kafka e API gateways, além de uma checklist de qualidade para garantir que cada painel tenha valor operacional, evitando painéis de vaidade.

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

Dashboard Builder

Use esta skill quando a tarefa for construir um painel que as pessoas possam usar para operar.

O objetivo não é "mostrar todas as métricas". O objetivo é responder:

  • está saudável?
  • onde está o gargalo?
  • o que mudou?
  • qual ação deve ser tomada?

Quando Usar

  • "Construir um painel de monitoramento para Kafka"
  • "Criar um dashboard Grafana para Elasticsearch"
  • "Fazer um dashboard SigNoz para este serviço"
  • "Transformar esta lista de métricas em um painel operacional real"

Diretrizes

  • não comece pelo layout visual; comece pelas perguntas do operador
  • não inclua todas as métricas disponíveis só porque existem
  • não misture painéis de saúde, throughput e recursos sem estrutura
  • não publique painéis sem títulos, unidades e limites sensatos

Fluxo de Trabalho

1. Defina as perguntas operacionais

Organize em torno de:

  • saúde / disponibilidade
  • latência / desempenho
  • throughput / volume
  • saturação / recursos
  • risco específico do serviço

2. Estude o schema da plataforma alvo

Inspecione dashboards existentes primeiro:

  • estrutura JSON
  • linguagem de consulta
  • variáveis
  • estilização de limites
  • layout de seções

3. Construa o painel mínimo útil

Estrutura recomendada:

  1. visão geral
  2. desempenho
  3. recursos
  4. seção específica do serviço

4. Corte painéis de vaidade

Cada painel deve responder a uma pergunta real. Se não responder, remova-o.

Exemplos de Conjuntos de Painéis

Elasticsearch

  • saúde do cluster
  • alocação de shards
  • latência de busca
  • taxa de indexação
  • heap JVM / GC

Kafka

  • número de brokers
  • partições sub-replicadas
  • mensagens de entrada / saída
  • atraso do consumidor
  • pressão de disco e rede

API gateway / ingress

  • taxa de requisições
  • latência p50 / p95 / p99
  • taxa de erro
  • saúde do upstream
  • conexões ativas

Checklist de Qualidade

  • JSON do dashboard válido
  • agrupamento claro de seções
  • títulos e unidades presentes
  • limites/cores de status são significativos
  • variáveis existem para filtros comuns
  • intervalo de tempo padrão e atualização são sensatos
  • nenhum painel de vaidade sem valor operacional

Skills Relacionadas

  • 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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