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What it is
Guide for designing effective analytical dashboards using Metabase, Grafana, and Looker including design principles, KPI selection, layout patterns, interactivity, and stakeholder-driven dashboard strategy.
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SKILL.md
427 lines · 1,709 words
You are an expert dashboard designer who creates clear, actionable analytical dashboards that drive decisions, selecting the right tools and applying information design principles across Metabase, Grafana, and Looker.
Use this skill when:
User asks about dashboard designer techniques or best practices
User needs guidance on dashboard designer concepts
Guide for designing effective analytical dashboards using Metabase, Grafana, and Looker including design principles, KPI selection, layout patterns, interactivity, and stakeholder-driven dashboard strategy.
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The principle
You are an expert dashboard designer who creates clear, actionable analytical dashboards that drive decisions, selecting the right tools and applying information design principles across Metabase, Grafana, and Looker.
What it covers
When to Use
Dashboard Design Principles
KPI Selection Framework
Metabase Setup and Patterns
Grafana for Operational Dashboards
Looker / LookML Patterns
Dashboard Anti-Patterns
Dashboard Review Checklist
Stakeholder Communication
Process
Output Format
Edge Cases
Example
User wants to implement or improve their approach to dashboard designer
Do NOT use when:
The request falls outside the scope of dashboard designer
User needs a different specialized skill for their specific situation
The topic requires professional consultation beyond general guidance
A dashboard should communicate its primary message within 5 seconds of viewing. If a stakeholder cannot identify the key takeaway immediately, the design needs revision.
┌─────────────────────────────────────────────────┐
│ LEVEL 1: KPI Summary Cards (top row) │
│ Answer: "How are we doing right now?" │
├─────────────────────────────────────────────────┤
│ LEVEL 2: Trend Charts (middle section) │
│ Answer: "How has it been changing?" │
├─────────────────────────────────────────────────┤
│ LEVEL 3: Breakdown Tables (bottom section) │
│ Answer: "What are the details?" │
└─────────────────────────────────────────────────┘Executive Dashboard (4-6 charts):
┌──────┬──────┬──────┬──────┐
│ KPI1 │ KPI2 │ KPI3 │ KPI4 │ <- Summary cards
├──────┴──────┼──────┴──────┤
│ Main Trend │ Secondary │ <- Key trends
├─────────────┼─────────────┤
│ Breakdown │ Breakdown │ <- Details
└─────────────┴─────────────┘
Operational Dashboard (8-12 charts):
┌──────┬──────┬──────┬──────┬──────┬──────┐
│ KPI1 │ KPI2 │ KPI3 │ KPI4 │ KPI5 │ KPI6 │
├──────┴──────┴──────┼──────┴──────┴──────┤
│ Primary Metric │ Secondary Metric │
├────────────────────┼────────────────────┤
│ Segment Detail │ Segment Detail │
├────────────────────┴────────────────────┤
│ Detailed Table │
└─────────────────────────────────────────┘| Function | Primary KPIs | Secondary KPIs |
|---|---|---|
| SaaS Revenue | MRR, ARR, Net Revenue Retention | ARPU, Expansion MRR, Contraction MRR |
| Growth | New Users, Activation Rate, WAU/MAU | Signup Rate, Time-to-Activation, Virality |
| Engagement | DAU/MAU, Session Duration, Feature Usage | Stickiness, Power Users %, Return Rate |
| Retention | D1/D7/D30 Retention, Churn Rate | Resurrection Rate, At-Risk Users |
| Sales | Pipeline Value, Win Rate, Sales Cycle | Quota Attainment, Lead Response Time |
| Support | CSAT, First Response Time, Resolution Time | Ticket Volume, Escalation Rate |
| Marketing | CAC, LTV:CAC, Conversion Rate | Click-Through Rate, Cost Per Lead |
| Engineering | Deployment Frequency, Lead Time, MTTR | Change Failure Rate, Uptime |
┌─────────────────────────────┐
│ Monthly Revenue │
│ $1,245,320 │ <- Current value (large, bold)
│ ▲ 12.3% vs last month │ <- Trend indicator with context
│ Target: $1,200,000 ✓ │ <- Target comparison
│ ████████████████░░░ 104% │ <- Progress bar
└─────────────────────────────┘| Comparison Type | When to Use | Example |
|---|---|---|
| Period over Period | Trend detection | This month vs. last month |
| Year over Year | Seasonal businesses | Jan 2025 vs. Jan 2024 |
| vs. Target | Goal tracking | Actual vs. budget |
| vs. Benchmark | Industry context | Your NPS vs. industry avg |
| Cohort-based | Product changes | Pre-launch vs. post-launch |
# Simple question (UI builder)
- Type: "Simple"
- Use when: Basic aggregations, filters, single-table queries
# Custom question (notebook editor)
- Type: "Custom"
- Use when: Joins, custom columns, multi-step aggregations
# Native query (SQL)
- Type: "Native"
- Use when: Complex logic, CTEs, window functions, performance-- Use Metabase variables for interactive filters
SELECT
DATE_TRUNC('week', created_at) AS week,
COUNT(*) AS signups,
COUNT(*) FILTER (WHERE activated) AS activated,
ROUND(100.0 * COUNT(*) FILTER (WHERE activated) / COUNT(*), 1) AS activation_rate
FROM users
WHERE created_at >= {{start_date}}
AND created_at < {{end_date}}
AND CASE WHEN {{channel}} = 'All' THEN TRUE
ELSE acquisition_channel = {{channel}} END
GROUP BY 1
ORDER BY 1;Collection structure:
├── Executive/
│ ├── Company Overview
│ └── Board Metrics
├── Product/
│ ├── User Engagement
│ ├── Feature Adoption
│ └── Retention
├── Revenue/
│ ├── MRR Dashboard
│ ├── Cohort LTV
│ └── Pricing Analysis
├── Marketing/
│ ├── Campaign Performance
│ └── Channel Attribution
└── Operational/
├── System Health
└── Support Metrics{
"panels": [
{
"title": "Request Rate",
"type": "timeseries",
"targets": [
{
"expr": "rate(http_requests_total{job='api'}[5m])",
"legendFormat": "{{method}} {{path}}"
}
],
"fieldConfig": {
"defaults": {
"unit": "reqps",
"thresholds": {
"steps": [
{ "value": 0, "color": "green" },
{ "value": 80, "color": "yellow" },
{ "value": 95, "color": "red" }
]
}
}
}
}
]
}# Grafana alerting for dashboard metrics
groups:
- name: SLA Alerts
rules:
- alert: HighErrorRate
expr: |
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) > 0.01
for: 5m
labels:
severity: critical
annotations:
summary: "Error rate above 1% for 5 minutes"
- alert: HighLatency
expr: |
histogram_quantile(0.95,
rate(http_request_duration_seconds_bucket[5m])
) > 0.5
for: 10m
labels:
severity: warning
annotations:
summary: "P95 latency above 500ms"# Template variables for interactive filtering
Variable: environment
Type: Query
Query: label_values(up, environment)
Multi-value: true
Variable: service
Type: Query
Query: label_values(up{environment=~"$environment"}, service)
Multi-value: true
Variable: interval
Type: Interval
Values: 1m, 5m, 15m, 1h
Default: 5m# views/users.view.lkml
view: users {
sql_table_name: analytics.users ;;
dimension: user_id {
primary_key: yes
type: number
}
dimension_group: created {
type: time
timeframes: [raw, date, week, month, quarter, year]
sql: ${TABLE}.created_at ;;
}
dimension: acquisition_channel {
type: string
sql: ${TABLE}.channel ;;
}
measure: total_users {
type: count_distinct
sql: ${user_id} ;;
}
measure: new_users_last_30d {
type: count_distinct
sql: ${user_id} ;;
filters: [created_date: "last 30 days"]
}
}explore: orders {
join: users {
type: left_outer
sql_on: ${orders.user_id} = ${users.user_id} ;;
relationship: many_to_one
}
join: products {
type: left_outer
sql_on: ${orders.product_id} = ${products.product_id} ;;
relationship: many_to_one
}
always_filter: {
filters: [orders.created_date: "last 90 days"]
}
}| Anti-Pattern | Problem | Fix |
|---|---|---|
| Wall of numbers | Cognitive overload | Limit to 6-8 key metrics per view |
| Rainbow charts | No visual hierarchy | Use 2-3 colors with purpose |
| Pie chart overuse | Hard to compare slices | Use horizontal bar charts |
| No time context | Cannot assess trend | Always include period-over-period |
| Too many filters | Analysis paralysis | Default to most common view |
| Vanity metrics | No actionable insight | Connect metrics to decisions |
| Stale dashboards | Erodes trust | Set up refresh schedules and alerts |
| No documentation | Tribal knowledge | Add descriptions to every metric |
| One dashboard for all | Serves nobody well | Tailor to specific audience |
| Real-time everything | Unnecessary load | Match refresh to decision cadence |
Each chart answers a specific question
Primary KPIs are visible without scrolling
Consistent date ranges across all panels
Color usage is purposeful and accessible
Labels and units are clear on all axes
Comparison context provided (target, trend, benchmark)
Filters default to the most common use case
Mobile/tablet view is usable (if needed)
Data source documentation is linked
Refresh schedule matches decision cadence
Stakeholders can find answers without asking questions
Dashboard load time is under 5 seconds
At least one person reviews the dashboard weekly
Unused charts are removed quarterly
Metric definitions are reviewed for accuracy
Access permissions are appropriate
Dashboard: [Name]
Audience: [Who uses this]
Cadence: [How often they check it]
Primary Questions:
1. [Question this dashboard answers]
2. [Second question]
3. [Third question]
Key Decisions Supported:
- [Decision 1 based on metrics]
- [Decision 2 based on metrics]
Data Sources: [List]
Refresh Frequency: [Real-time / hourly / daily]
Owner: [Team/person responsible]Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
Analyze context. Review the information provided and identify key factors relevant to dashboard designer
Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
Present structured output. Deliver findings in the output format below with clear next steps
Address follow-ups. Answer additional questions and refine recommendations based on feedback
## Dashboard Designer Analysis
### Assessment
[Key findings and observations]
### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]
### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]Incomplete information: Ask clarifying questions before proceeding with recommendations
Conflicting requirements: Prioritize the most critical constraint and note trade-offs
Out of scope requests: Redirect to appropriate specialized skill or professional resource
Beginner vs advanced: Adjust depth and terminology based on user's experience level
Input: "Help me with dashboard designer for my current situation"
Output:
Based on your situation, here is a structured approach to dashboard designer:
Assessment: Evaluate your current state and identify key areas for improvement
Strategy: Develop a targeted plan based on best practices
Implementation: Execute the plan with specific, measurable steps
Review: Monitor progress and adjust as needed
SKILL.md
427 lines · 1,709 words
The text of SKILL.md is shown as it downloads, under the Apache-2.0 license, by Ferrox Labs. The license is the one in the file's own frontmatter.
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