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dashboard-builder

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

¿Qué es dashboard-builder?

Dashboard Builder es una habilidad diseñada para ingenieros y operadores que necesitan construir paneles de monitoreo accionables en plataformas como Grafana, SigNoz y similares. En lugar de simplemente mostrar todas las métricas disponibles, esta habilidad se centra en responder preguntas clave del operador: ¿está saludable el sistema?, ¿dónde está el cuello de botella?, ¿qué cambió? y ¿qué acción debe tomar alguien? El proceso comienza definiendo preguntas operativas en cinco categorías (salud, latencia, rendimiento, saturación y riesgos específicos del servicio), luego estudia el esquema de la plataforma objetivo y construye el panel mínimo útil con secciones claras como resumen, rendimiento, recursos y secciones específicas del servicio. Además, aplica un riguroso control de calidad que elimina paneles decorativos o sin valor operativo, asegurando que cada panel tenga títulos, unidades y umbrales significativos. Es compatible con sistemas de monitoreo populares y se integra bien con habilidades relacionadas como investigación de operaciones y patrones de backend.

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

Dashboard Builder

Utiliza esto cuando la tarea sea construir un panel 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 debe tomar alguien?

Cuándo Usarlo

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

Barreras de Seguridad

  • no empezar desde el diseño visual; empezar desde 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
  • caudal / volumen
  • saturación / recursos
  • riesgo específico del servicio

2. Estudiar el esquema de la plataforma objetivo

Inspeccionar paneles existentes primero:

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

3. Construir el panel mínimo útil

Estructura recomendada:

  1. resumen
  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.

Conjuntos de Paneles de Ejemplo

Elasticsearch

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

Kafka

  • número de brokers
  • particiones sub-replicadas
  • mensajes entrantes / salientes
  • retraso del consumidor
  • presión de disco y red

Puerta de enlace API / entrada

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

Lista de Verificación de Calidad

  • JSON del panel válido
  • agrupación de secciones clara
  • títulos y unidades presentes
  • umbrales/colores de estado significativos
  • variables existen para filtros comunes
  • rango de tiempo y actualización por defecto 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/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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