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:

overview
performance
resources
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

research-ops
backend-patterns
terminal-ops

Files1

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Overall Score

82/100

Grade

B

Good

Safety

95

Quality

78

Clarity

85

Completeness

72

Summary

A skill that guides AI agents to build operational monitoring dashboards for platforms like Grafana and SigNoz. Rather than creating visually comprehensive but operationally useless dashboards, it teaches agents to structure dashboards around real operator questions (health, performance, throughput, resources) and to include only panels that answer those questions.

Detected Capabilities

Dashboard design methodology (organizing by operational questions rather than available metrics)Platform-agnostic schema analysis (Grafana JSON, SigNoz UI, query language patterns)Panel design with thresholds, units, and meaningful status indicatorsVanity panel elimination (scope reduction based on operator value)Example panel patterns for common infrastructure services (Elasticsearch, Kafka, ingress controllers)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

build monitoring dashboardgrafana dashboard designoperational metrics visualizationkafka dashboardelasticsearch monitoringsigNoz setupturn metrics into dashboard

Use Cases

Build a Kafka cluster monitoring dashboard with broker health, replication status, and consumer lag panels
Create a Grafana dashboard for Elasticsearch cluster operations including shard allocation and JVM metrics
Design a SigNoz dashboard for API gateway monitoring with latency percentiles, error rates, and upstream health
Convert a raw metrics list into a structured, operator-focused dashboard with clear sections and actionable thresholds

Quality Notes

Positive: Clear philosophical guardrails that prioritize operator intent over metric coverage — establishes a principled boundary against scope creep.
Positive: Well-structured workflow with four concrete steps (define questions → study schema → build minimum board → cut vanity).
Positive: Domain-specific examples (Elasticsearch, Kafka, API gateway) with realistic panel sets, helping agents understand what 'operational value' looks like.
Positive: Quality checklist is actionable and specific (JSON validity, sections, units, thresholds, variables, time range, no vanity).
Positive: Scope is clearly bounded to dashboard design; does not claim to teach metrics collection, alerting rules, or platform administration.
Limitation: No guidance on querying — skill assumes the agent or user will know how to write queries for the target platform (PromQL, ES query DSL, etc.). Could benefit from a note like 'consult platform-specific query documentation' or examples of simple queries.
Limitation: Does not address multi-datasource dashboards or cross-platform correlation — assumes single-service or single-platform focus.
Limitation: No error handling guidance — what if the target system lacks certain metrics, or if thresholds cannot be determined from data? Skill could acknowledge these edge cases.
Limitation: Related skills are listed but not explained — agent does not know when to defer to `research-ops` or `backend-patterns`.

Model: claude-haiku-4-5-20251001Analyzed: Apr 20, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest

v1.0

No changelog

2026-04-12

dashboard-builder