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dashboard-builder

Construye paneles de monitoreo que respondan preguntas reales de operadores para Grafana, SigNoz y plataformas similares. Úsalo cuando necesites convertir métricas en un tablero funcional, no en uno decorativo.

¿Qué es dashboard-builder?

Dashboard Builder es una habilidad diseñada para crear paneles de monitoreo operativos, enfocados en responder preguntas clave de operadores en lugar de mostrar todas las métricas disponibles. Es compatible con plataformas como Grafana y SigNoz, y evita paneles 'de adorno' que no proporcionan valor real. La habilidad guía al usuario a través de un flujo de trabajo estructurado: primero definir preguntas operativas (salud, latencia, rendimiento, saturación y riesgos específicos), luego estudiar el esquema de la plataforma objetivo, construir un tablero mínimo útil con secciones ordenadas (vista general, rendimiento, recursos y sección específica del servicio), y finalmente eliminar paneles que no respondan a preguntas reales. Incluye ejemplos prácticos para Elasticsearch, Kafka y puertas de enlace API, y una lista de verificación de calidad para asegurar que el resultado sea funcional y operativo. No se debe empezar por el diseño visual, sino por las preguntas del operador, y se deben incluir títulos, unidades y umbrales significativos en cada panel.

Compatible con~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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Documentación

Dashboard Builder

Úsalo cuando la tarea sea construir un tablero desde el cual las personas puedan operar.

El objetivo no es "mostrar cada métrica". El objetivo es responder:

  • ¿está saludable?
  • ¿dónde está el cuello de botella?
  • ¿qué cambió?
  • ¿qué acción debería tomar alguien?

Cuándo Usarlo

  • "Construir un tablero de monitoreo para Kafka"
  • "Crear un tablero de Grafana para Elasticsearch"
  • "Hacer un tablero de SigNoz para este servicio"
  • "Convertir esta lista de métricas en un tablero operativo real"

Barreras de Seguridad

  • no empezar por el diseño visual; empezar por las preguntas del operador
  • no incluir cada métrica disponible solo porque existe
  • no mezclar paneles de salud, rendimiento y recursos sin estructura
  • no enviar paneles sin títulos, unidades y umbrales sensatos

Flujo de Trabajo

1. Definir las preguntas operativas

Organizar en torno a:

  • salud / disponibilidad
  • latencia / rendimiento
  • throughput / volumen
  • saturación / recursos
  • riesgo específico del servicio

2. Estudiar el esquema de la plataforma objetivo

Inspeccionar primero los tableros existentes:

  • estructura JSON
  • lenguaje de consulta
  • variables
  • estilo de umbrales
  • diseño de secciones

3. Construir el tablero mínimo útil

Estructura recomendada:

  1. vista general
  2. rendimiento
  3. recursos
  4. sección específica del servicio

4. Eliminar paneles decorativos

Cada panel debe responder una pregunta real. Si no lo hace, elimínalo.

Ejemplos de Conjuntos de Paneles

Elasticsearch

  • salud del clúster
  • asignación de fragmentos
  • latencia de búsqueda
  • tasa de indexación
  • heap JVM / GC

Kafka

  • cantidad de brokers
  • particiones subreplicadas
  • mensajes entrantes / salientes
  • retraso del consumidor
  • presión de disco y red

Puerta de enlace API / ingreso

  • tasa de solicitudes
  • latencia p50 / p95 / p99
  • tasa de errores
  • salud del upstream
  • conexiones activas

Lista de Verificación de Calidad

  • JSON válido del tablero
  • agrupación clara de secciones
  • títulos y unidades presentes
  • umbrales/colores de estado significativos
  • variables para filtros comunes
  • rango de tiempo y actualización predeterminados sensatos
  • sin paneles decorativos sin valor operativo

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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