metrics-dashboard

Name: metrics-dashboard
Rating: 7.9 (16 reviews)
Author: LerianStudio

by LerianStudio1551GitHub

This skill provides a structured 5-phase workflow for designing KPI dashboards, from requirements gathering to implementation. It enforces documentation standards for metrics and data sources, includes pressure resistance patterns, and dispatches to a specialized metrics-analyst agent.

Output Preview

output_preview.md

Executive Revenue Dashboard - Implementation Package

Dashboard Overview

Purpose: Monitor monthly revenue performance and identify growth opportunities Primary Audience: C-Suite executives (CEO, CFO, CRO) Update Frequency: Daily (automated refresh at 6 AM UTC) Drill-down Level: Country → Region → Account

KPI Definitions

1. Monthly Recurring Revenue (MRR)

-- Calculation Methodology
SELECT 
  DATE_TRUNC('month', subscription_start_date) AS month,
  SUM(monthly_amount_usd) AS mrr_usd,
  COUNT(DISTINCT account_id) AS active_accounts
FROM subscriptions
WHERE status = 'active'
GROUP BY 1
ORDER BY 1 DESC;

Formula: Σ(Active Subscription Monthly Amount) Target: $2.5M MRR by Q4 Owner: VP of Sales Unit: USD

2. Customer Acquisition Cost (CAC)

-- Marketing + Sales Expenses / New Customers
SELECT 
  DATE_TRUNC('month', campaign_date) AS month,
  ROUND(
    (SUM(marketing_spend) + SUM(sales_commission)) /
    COUNT(DISTINCT new_customer_id),
    2
  ) AS cac_usd
FROM marketing_campaigns mc
JOIN sales_closed sc ON mc.campaign_id = sc.campaign_id
GROUP BY 1;

Target: < $1,200 Benchmark: Industry average $1,500

Data Source Documentation

| Source System | Extraction Method | Refresh | Quality Checks | |---------------|-------------------|---------|----------------| | Salesforce CRM | API sync (hourly) | Daily | Account ID validation, currency conversion | | Stripe Payments | Webhook events | Real-time | Transaction reconciliation, fraud flags | | Google Analytics | BigQuery export | Daily | Session deduplication, bot filtering |

Visualization Specifications

Primary Dashboard Layout

{
  "layout": "3x2 grid",
  "components": [
    {
      "type": "metric_card",
      "title": "Current MRR",
      "value": "$2,145,320",
      "trend": "+12.5% MoM",
      "color": "green",
      "sparkline": true
    },
    {
      "type": "time_series",
      "title": "MRR Growth Trend",
      "x_axis": "Month",
      "y_axis": "USD (thousands)",
      "forecast": true
    }
  ]
}

Anomaly Detection Thresholds

MRR Drop >15% MoM: Immediate alert to CRO
CAC Increase >20%: Weekly review with marketing
Churn Rate >5%: Daily monitoring until resolved

Implementation Checklist

✅ Data feeds connected and validated ✅ Calculations match source system reports ✅ Visuals render correctly on mobile/desktop ✅ Access controls: Executives + Finance team only ✅ Performance: <3s load time with 12 months history

Target Audience

Business analysts, data teams, and managers responsible for creating performance dashboards and tracking KPIs

8/10Security

Low security risk, safe to use

Clarity

Practicality

Quality

Maintainability

Innovation

Data

kpi-dashboarddata-visualizationmetrics-trackingbusiness-intelligence

Compatible Agents

Claude Code

~/.claude/skills/

Codex CLI

~/.codex/skills/

Gemini CLI

~/.gemini/skills/

OpenCode

~/.opencode/skills/

OpenClaw

~/.openclaw/skills/

GitHub Copilot

~/.copilot/skills/

Cursor

~/.cursor/skills/

Windsurf

~/.codeium/windsurf/skills/

Cline

~/.cline/skills/

Roo Code

~/.roo/skills/

Kiro

~/.kiro/skills/

Junie

~/.junie/skills/

Augment Code

~/.augment/skills/

Warp

~/.warp/skills/

Goose

~/.config/goose/skills/

SKILL.md

Metrics Dashboard Workflow

This skill provides a structured workflow for designing KPI dashboards using the metrics-analyst agent.

Workflow Overview

The metrics dashboard workflow follows 5 phases:

Phase	Name	Description
1	Requirements	Define dashboard objectives and audience
2	KPI Design	Define metrics and methodology
3	Data Architecture	Map data sources and calculations
4	Visualization	Design visual presentation
5	Implementation	Build and validate

Phase 1: Requirements

MANDATORY: Define dashboard objectives before building

Questions to Answer

Question	Purpose
What decisions will this support?	Ensures relevance
Who is the primary audience?	Tailors complexity
What frequency of update?	Sets refresh requirements
What level of drill-down?	Scopes depth
What benchmark comparisons?	Defines targets

Dashboard Types

Type	Audience	Focus
Executive	C-Suite	High-level, strategic
Operational	Managers	Detailed, actionable
Departmental	Department heads	Function-specific
Board	Directors	Governance, strategic

Blocker Check

If ANY of these are unclear, STOP and ask:

Dashboard purpose
Primary audience
Key decisions supported
Update frequency required

Phase 2: KPI Design

MANDATORY: Define all metrics with methodology

KPI Definition Standard

Element	Requirement
Name	Clear, concise name
Definition	Precise description
Formula	Exact calculation
Unit	Measurement unit
Target	Performance target
Owner	Accountable person
Frequency	Update cadence

KPI Categories

Category	Example KPIs
Financial	Revenue, margin, EBITDA, cash flow
Operational	Throughput, cycle time, utilization
Customer	Retention, NPS, LTV, CAC
Growth	ARR growth, customer growth, expansion

KPI Selection Principles

Principle	Description
Relevance	Supports specific decisions
Measurable	Can be quantified objectively
Actionable	Drives specific actions
Timely	Available when needed
Owned	Clear accountability

Phase 3: Data Architecture

MANDATORY: Document data lineage completely

Data Source Mapping

Element	Documentation
Source system	Where data originates
Extraction method	How data is obtained
Transformation	Any calculations or adjustments
Refresh frequency	How often updated
Data quality	Validation checks

Data Quality Requirements

Check	Validation
Completeness	All required data present
Accuracy	Data matches source
Timeliness	Data is current
Consistency	Data consistent across sources

Phase 4: Agent Dispatch

Dispatch to specialist with full context

Agent Dispatch

Task tool:
  subagent_type: "metrics-analyst"
  model: "opus"
  prompt: |
    Design metrics dashboard per these specifications:

    **Purpose**: [from Phase 1]
    **Audience**: [from Phase 1]
    **Update Frequency**: [from Phase 1]

    **KPIs Required**:
    [From Phase 2 - list with definitions]

    **Data Sources**:
    [From Phase 3 - source mapping]

    **Required Output**:
    - KPI definitions with formulas
    - Data source documentation
    - Calculation methodology
    - Visualization specifications
    - Anomaly thresholds
    - Implementation guide

Required Output Elements

Element	Requirement
Metrics Summary	Dashboard overview
KPI Definitions	Complete definitions
Data Sources	Source documentation
Calculation Methodology	Formula details
Dashboard Design	Visual specifications
Anomaly Analysis	Threshold definitions
Recommendations	Enhancement suggestions

Phase 5: Implementation

MANDATORY: Validate before deployment

Implementation Checklist

Check	Validation
Data feeds working	All sources connected
Calculations verified	Outputs match expected
Visuals rendering	Display correctly
Refresh working	Updates as expected
Access controlled	Right users have access

Validation Tests

Test	Description
Data reconciliation	Dashboard ties to source
Historical comparison	Trends make sense
Edge cases	Handles nulls, zeros
Performance	Loads in acceptable time

Pressure Resistance

See shared-patterns/pressure-resistance.md for universal pressures.

Dashboard-Specific Pressures

Pressure Type	Request	Agent Response
"Just show the numbers"	"Numbers without methodology cannot be trusted. I'll include documentation."
"Pick the most important KPIs"	"KPI selection requires business input. Which decisions should these support?"
"Skip the data quality checks"	"Unreliable data undermines dashboard value. I'll validate all sources."
"Copy the existing dashboard"	"Each dashboard needs fresh design. I'll validate requirements."

Anti-Rationalization Table

See shared-patterns/anti-rationalization.md for universal anti-rationalizations.

Dashboard-Specific Anti-Rationalizations

Rationalization	Why It's WRONG	Required Action
"Everyone knows what revenue means"	Definitions vary	DEFINE specifically
"Data source is obvious"	Lineage needs documentation	DOCUMENT source
"Calculation is standard"	Standard still needs documentation	SHOW formula
"Refresh frequency doesn't matter"	Stale data causes bad decisions	SPECIFY frequency

Execution Report

Upon completion, report:

Metric	Value
Duration	Xm Ys
KPIs Defined	N
Data Sources Mapped	N
Visualizations Designed	N
Anomaly Thresholds	N
Result	COMPLETE/PARTIAL

Quality Indicators

Indicator	Status
All KPIs defined	YES/NO
All sources documented	YES/NO
All calculations shown	YES/NO
Data validated	YES/NO
Refresh tested	YES/NO

Source: https://github.com/LerianStudio/ring#finance-team~skills~metrics-dashboard

Content curated from original sources, copyright belongs to authors

Grade A

7.9AI Score

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WORKS WITH

Claude

Codex

Gemini