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Dashboard builder

Skill asong56/skills/08-extend/dashboard-builder

268 AI coding assistant skills, organized across 12 workflow layers. Sources include Anthropic official, FRM, SKC, LRN, SKA, and other mainstream AI coding frameworks.

Install
npx -y skills add asong56/skills --skill dashboard-builder

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What its author says it does

Copied from the file, not written here

Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.

SKILL.md

2.3 KB, 474 tokens by cl100k_base, as published. Nobody here has run it

Dashboard Builder

Use this when the task is to build a dashboard people can operate from.

The goal is not "show every metric." The goal is to answer:

  • is it healthy?
  • where is the bottleneck?
  • what changed?
  • what action should someone take?

When to Use

  • "Build a Kafka monitoring dashboard"
  • "Create a Grafana dashboard for Elasticsearch"
  • "Make a SigNoz dashboard for this service"
  • "Turn this metrics list into a real operational dashboard"

Guardrails

  • do not start from visual layout; start from operator questions
  • do not include every available metric just because it exists
  • do not mix health, throughput, and resource panels without structure
  • do not ship panels without titles, units, and sane thresholds

Workflow

1. Define the operating questions

Organize around:

  • health / availability
  • latency / performance
  • throughput / volume
  • saturation / resources
  • service-specific risk

2. Study the target platform schema

Inspect existing dashboards first:

  • JSON structure
  • query language
  • variables
  • threshold styling
  • section layout

3. Build the minimum useful board

Recommended structure:

  1. overview
  2. performance
  3. resources
  4. service-specific section

4. Cut vanity panels

Every panel should answer a real question. If it does not, remove it.

Example Panel Sets

Elasticsearch

  • cluster health
  • shard allocation
  • search latency
  • indexing rate
  • JVM heap / GC

Kafka

  • broker count
  • under-replicated partitions
  • messages in / out
  • consumer lag
  • disk and network pressure

API gateway / ingress

  • request rate
  • p50 / p95 / p99 latency
  • error rate
  • upstream health
  • active connections

Quality Checklist

  • valid dashboard JSON
  • clear section grouping
  • titles and units are present
  • thresholds/status colors are meaningful
  • variables exist for common filters
  • default time range and refresh are sensible
  • no vanity panels with no operator value

Related Skills

  • research-ops
  • backend-patterns
  • terminal-ops

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.