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Construtor de Dashboards

Construa dashboards de monitoramento que respondam perguntas reais de operadores para Grafana, SigNoz e plataformas similares. Use quando precisar transformar métricas em um dashboard funcional, em vez de um painel de vaidade.

O que é Construtor de Dashboards?

O Construtor de Dashboards é uma habilidade projetada para ajudar desenvolvedores e operadores a criar dashboards de monitoramento verdadeiramente úteis. Diferente de abordagens que simplesmente exibem todas as métricas disponíveis, esta skill foca em responder perguntas operacionais concretas, como: o sistema está saudável, onde está o gargalo, o que mudou e qual ação deve ser tomada. A habilidade suporta plataformas populares como Grafana e SigNoz, e orienta o usuário através de um fluxo de trabalho estruturado: definir perguntas operacionais, estudar o esquema da plataforma alvo, construir o dashboard mínimo útil e, por fim, remover painéis de vaidade. Ela inclui exemplos práticos para Elasticsearch, Kafka e gateways de API, além de uma lista de verificação de qualidade. É ideal para quem deseja transformar listas de métricas brutas em ferramentas de operação reais e acionáveis.

Funciona com~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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Documentação

O que Construtor de Dashboards faz?

Use esta habilidade quando a tarefa for construir um dashboard que os operadores possam usar no dia a dia.

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

  • está saudável?
  • onde está o gargalo?
  • o que mudou?
  • qual ação alguém deve tomar?

Quando Usar

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

Diretrizes

  • não comece pelo layout visual; comece pelas perguntas do operador
  • não inclua todas as métricas disponíveis apenas 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 esquema da plataforma alvo

Inspecione dashboards existentes primeiro:

  • estrutura JSON
  • linguagem de consulta
  • variáveis
  • estilo de limites
  • layout das seções

3. Construa o dashboard 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

  • contagem de brokers
  • partições sub-replicadas
  • mensagens recebidas / enviadas
  • atraso do consumidor
  • pressão de disco e rede

Gateway de API / Ingress

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

Lista de Verificação de Qualidade

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

Habilidades 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/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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