name	metrics-dashboard
description	KPI and metrics dashboard workflow covering metric definition, data sourcing, visualization design, and anomaly detection. Delivers actionable dashboards.
trigger	- Defining KPIs and metrics - Designing executive dashboards - Implementing performance tracking - Setting up anomaly detection
skip_when	- Detailed financial analysis → use financial-analysis - Building models → use financial-modeling - Preparing reports → use financial-reporting
related	[object Object]

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

metrics-dashboard

Install Skill

SKILL.md