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Add comprehensive Conductor plugin implementing Context-Driven Development methodology with tracks, specs, and phased implementation plans. Components: - 5 commands: setup, new-track, implement, status, revert - 1 agent: conductor-validator - 3 skills: context-driven-development, track-management, workflow-patterns - 18 templates for project artifacts Documentation updates: - README.md: Updated counts (68 plugins, 100 agents, 110 skills, 76 tools) - docs/plugins.md: Added Conductor to Workflows section - docs/agents.md: Added conductor-validator agent - docs/agent-skills.md: Added Conductor skills section Also includes Prettier formatting across all project files.
429 lines
17 KiB
Markdown
429 lines
17 KiB
Markdown
---
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name: kpi-dashboard-design
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description: Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
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---
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# KPI Dashboard Design
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Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.
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## When to Use This Skill
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- Designing executive dashboards
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- Selecting meaningful KPIs
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- Building real-time monitoring displays
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- Creating department-specific metrics views
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- Improving existing dashboard layouts
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- Establishing metric governance
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## Core Concepts
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### 1. KPI Framework
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| Level | Focus | Update Frequency | Audience |
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| --------------- | ---------------- | ----------------- | ---------- |
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| **Strategic** | Long-term goals | Monthly/Quarterly | Executives |
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| **Tactical** | Department goals | Weekly/Monthly | Managers |
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| **Operational** | Day-to-day | Real-time/Daily | Teams |
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### 2. SMART KPIs
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```
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Specific: Clear definition
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Measurable: Quantifiable
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Achievable: Realistic targets
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Relevant: Aligned to goals
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Time-bound: Defined period
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```
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### 3. Dashboard Hierarchy
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```
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├── Executive Summary (1 page)
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│ ├── 4-6 headline KPIs
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│ ├── Trend indicators
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│ └── Key alerts
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├── Department Views
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│ ├── Sales Dashboard
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│ ├── Marketing Dashboard
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│ ├── Operations Dashboard
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│ └── Finance Dashboard
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└── Detailed Drilldowns
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├── Individual metrics
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└── Root cause analysis
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```
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## Common KPIs by Department
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### Sales KPIs
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```yaml
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Revenue Metrics:
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- Monthly Recurring Revenue (MRR)
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- Annual Recurring Revenue (ARR)
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- Average Revenue Per User (ARPU)
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- Revenue Growth Rate
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Pipeline Metrics:
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- Sales Pipeline Value
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- Win Rate
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- Average Deal Size
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- Sales Cycle Length
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Activity Metrics:
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- Calls/Emails per Rep
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- Demos Scheduled
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- Proposals Sent
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- Close Rate
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```
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### Marketing KPIs
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```yaml
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Acquisition:
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- Cost Per Acquisition (CPA)
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- Customer Acquisition Cost (CAC)
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- Lead Volume
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- Marketing Qualified Leads (MQL)
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Engagement:
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- Website Traffic
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- Conversion Rate
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- Email Open/Click Rate
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- Social Engagement
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ROI:
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- Marketing ROI
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- Campaign Performance
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- Channel Attribution
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- CAC Payback Period
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```
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### Product KPIs
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```yaml
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Usage:
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- Daily/Monthly Active Users (DAU/MAU)
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- Session Duration
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- Feature Adoption Rate
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- Stickiness (DAU/MAU)
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Quality:
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- Net Promoter Score (NPS)
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- Customer Satisfaction (CSAT)
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- Bug/Issue Count
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- Time to Resolution
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Growth:
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- User Growth Rate
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- Activation Rate
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- Retention Rate
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- Churn Rate
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```
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### Finance KPIs
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```yaml
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Profitability:
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- Gross Margin
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- Net Profit Margin
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- EBITDA
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- Operating Margin
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Liquidity:
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- Current Ratio
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- Quick Ratio
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- Cash Flow
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- Working Capital
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Efficiency:
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- Revenue per Employee
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- Operating Expense Ratio
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- Days Sales Outstanding
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- Inventory Turnover
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```
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## Dashboard Layout Patterns
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### Pattern 1: Executive Summary
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```
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┌─────────────────────────────────────────────────────────────┐
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│ EXECUTIVE DASHBOARD [Date Range ▼] │
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├─────────────┬─────────────┬─────────────┬─────────────────┤
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│ REVENUE │ PROFIT │ CUSTOMERS │ NPS SCORE │
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│ $2.4M │ $450K │ 12,450 │ 72 │
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│ ▲ 12% │ ▲ 8% │ ▲ 15% │ ▲ 5pts │
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├─────────────┴─────────────┴─────────────┴─────────────────┤
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│ │
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│ Revenue Trend │ Revenue by Product │
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│ ┌───────────────────────┐ │ ┌──────────────────┐ │
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│ │ /\ /\ │ │ │ ████████ 45% │ │
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│ │ / \ / \ /\ │ │ │ ██████ 32% │ │
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│ │ / \/ \ / \ │ │ │ ████ 18% │ │
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│ │ / \/ \ │ │ │ ██ 5% │ │
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│ └───────────────────────┘ │ └──────────────────┘ │
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│ │
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├─────────────────────────────────────────────────────────────┤
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│ 🔴 Alert: Churn rate exceeded threshold (>5%) │
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│ 🟡 Warning: Support ticket volume 20% above average │
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└─────────────────────────────────────────────────────────────┘
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```
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### Pattern 2: SaaS Metrics Dashboard
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```
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┌─────────────────────────────────────────────────────────────┐
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│ SAAS METRICS Jan 2024 [Monthly ▼] │
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├──────────────────────┬──────────────────────────────────────┤
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│ ┌────────────────┐ │ MRR GROWTH │
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│ │ MRR │ │ ┌────────────────────────────────┐ │
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│ │ $125,000 │ │ │ /── │ │
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│ │ ▲ 8% │ │ │ /────/ │ │
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│ └────────────────┘ │ │ /────/ │ │
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│ ┌────────────────┐ │ │ /────/ │ │
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│ │ ARR │ │ │ /────/ │ │
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│ │ $1,500,000 │ │ └────────────────────────────────┘ │
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│ │ ▲ 15% │ │ J F M A M J J A S O N D │
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│ └────────────────┘ │ │
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├──────────────────────┼──────────────────────────────────────┤
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│ UNIT ECONOMICS │ COHORT RETENTION │
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│ │ │
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│ CAC: $450 │ Month 1: ████████████████████ 100% │
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│ LTV: $2,700 │ Month 3: █████████████████ 85% │
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│ LTV/CAC: 6.0x │ Month 6: ████████████████ 80% │
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│ │ Month 12: ██████████████ 72% │
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│ Payback: 4 months │ │
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├──────────────────────┴──────────────────────────────────────┤
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│ CHURN ANALYSIS │
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│ ┌──────────┬──────────┬──────────┬──────────────────────┐ │
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│ │ Gross │ Net │ Logo │ Expansion │ │
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│ │ 4.2% │ 1.8% │ 3.1% │ 2.4% │ │
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│ └──────────┴──────────┴──────────┴──────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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### Pattern 3: Real-time Operations
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```
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┌─────────────────────────────────────────────────────────────┐
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│ OPERATIONS CENTER Live ● Last: 10:42:15 │
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├────────────────────────────┬────────────────────────────────┤
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│ SYSTEM HEALTH │ SERVICE STATUS │
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│ ┌──────────────────────┐ │ │
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│ │ CPU MEM DISK │ │ ● API Gateway Healthy │
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│ │ 45% 72% 58% │ │ ● User Service Healthy │
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│ │ ███ ████ ███ │ │ ● Payment Service Degraded │
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│ │ ███ ████ ███ │ │ ● Database Healthy │
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│ │ ███ ████ ███ │ │ ● Cache Healthy │
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│ └──────────────────────┘ │ │
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├────────────────────────────┼────────────────────────────────┤
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│ REQUEST THROUGHPUT │ ERROR RATE │
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│ ┌──────────────────────┐ │ ┌──────────────────────────┐ │
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│ │ ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅ │ │ │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ │ │
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│ └──────────────────────┘ │ └──────────────────────────┘ │
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│ Current: 12,450 req/s │ Current: 0.02% │
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│ Peak: 18,200 req/s │ Threshold: 1.0% │
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├────────────────────────────┴────────────────────────────────┤
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│ RECENT ALERTS │
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│ 10:40 🟡 High latency on payment-service (p99 > 500ms) │
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│ 10:35 🟢 Resolved: Database connection pool recovered │
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│ 10:22 🔴 Payment service circuit breaker tripped │
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└─────────────────────────────────────────────────────────────┘
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```
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## Implementation Patterns
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### SQL for KPI Calculations
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```sql
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-- Monthly Recurring Revenue (MRR)
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WITH mrr_calculation AS (
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SELECT
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DATE_TRUNC('month', billing_date) AS month,
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SUM(
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CASE subscription_interval
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WHEN 'monthly' THEN amount
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WHEN 'yearly' THEN amount / 12
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WHEN 'quarterly' THEN amount / 3
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END
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) AS mrr
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FROM subscriptions
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WHERE status = 'active'
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GROUP BY DATE_TRUNC('month', billing_date)
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)
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SELECT
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month,
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mrr,
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LAG(mrr) OVER (ORDER BY month) AS prev_mrr,
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(mrr - LAG(mrr) OVER (ORDER BY month)) / LAG(mrr) OVER (ORDER BY month) * 100 AS growth_pct
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FROM mrr_calculation;
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-- Cohort Retention
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WITH cohorts AS (
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SELECT
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user_id,
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DATE_TRUNC('month', created_at) AS cohort_month
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FROM users
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),
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activity AS (
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SELECT
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user_id,
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DATE_TRUNC('month', event_date) AS activity_month
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FROM user_events
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WHERE event_type = 'active_session'
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)
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SELECT
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c.cohort_month,
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EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) AS months_since_signup,
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COUNT(DISTINCT a.user_id) AS active_users,
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COUNT(DISTINCT a.user_id)::FLOAT / COUNT(DISTINCT c.user_id) * 100 AS retention_rate
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FROM cohorts c
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LEFT JOIN activity a ON c.user_id = a.user_id
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AND a.activity_month >= c.cohort_month
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GROUP BY c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month))
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ORDER BY c.cohort_month, months_since_signup;
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-- Customer Acquisition Cost (CAC)
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SELECT
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DATE_TRUNC('month', acquired_date) AS month,
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SUM(marketing_spend) / NULLIF(COUNT(new_customers), 0) AS cac,
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SUM(marketing_spend) AS total_spend,
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COUNT(new_customers) AS customers_acquired
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FROM (
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SELECT
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DATE_TRUNC('month', u.created_at) AS acquired_date,
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u.id AS new_customers,
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m.spend AS marketing_spend
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FROM users u
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JOIN marketing_spend m ON DATE_TRUNC('month', u.created_at) = m.month
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WHERE u.source = 'marketing'
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) acquisition
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GROUP BY DATE_TRUNC('month', acquired_date);
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```
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### Python Dashboard Code (Streamlit)
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```python
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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st.set_page_config(page_title="KPI Dashboard", layout="wide")
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# Header with date filter
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col1, col2 = st.columns([3, 1])
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with col1:
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st.title("Executive Dashboard")
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with col2:
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date_range = st.selectbox(
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"Period",
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["Last 7 Days", "Last 30 Days", "Last Quarter", "YTD"]
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)
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# KPI Cards
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def metric_card(label, value, delta, prefix="", suffix=""):
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delta_color = "green" if delta >= 0 else "red"
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delta_arrow = "▲" if delta >= 0 else "▼"
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st.metric(
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label=label,
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value=f"{prefix}{value:,.0f}{suffix}",
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delta=f"{delta_arrow} {abs(delta):.1f}%"
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)
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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metric_card("Revenue", 2400000, 12.5, prefix="$")
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with col2:
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metric_card("Customers", 12450, 15.2)
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with col3:
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metric_card("NPS Score", 72, 5.0)
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with col4:
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metric_card("Churn Rate", 4.2, -0.8, suffix="%")
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# Charts
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("Revenue Trend")
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revenue_data = pd.DataFrame({
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'Month': pd.date_range('2024-01-01', periods=12, freq='M'),
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'Revenue': [180000, 195000, 210000, 225000, 240000, 255000,
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270000, 285000, 300000, 315000, 330000, 345000]
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})
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fig = px.line(revenue_data, x='Month', y='Revenue',
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line_shape='spline', markers=True)
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fig.update_layout(height=300)
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st.plotly_chart(fig, use_container_width=True)
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with col2:
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st.subheader("Revenue by Product")
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product_data = pd.DataFrame({
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'Product': ['Enterprise', 'Professional', 'Starter', 'Other'],
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'Revenue': [45, 32, 18, 5]
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})
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fig = px.pie(product_data, values='Revenue', names='Product',
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hole=0.4)
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fig.update_layout(height=300)
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st.plotly_chart(fig, use_container_width=True)
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# Cohort Heatmap
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st.subheader("Cohort Retention")
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cohort_data = pd.DataFrame({
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'Cohort': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
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'M0': [100, 100, 100, 100, 100],
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'M1': [85, 87, 84, 86, 88],
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'M2': [78, 80, 76, 79, None],
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'M3': [72, 74, 70, None, None],
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'M4': [68, 70, None, None, None],
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})
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fig = go.Figure(data=go.Heatmap(
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z=cohort_data.iloc[:, 1:].values,
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x=['M0', 'M1', 'M2', 'M3', 'M4'],
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y=cohort_data['Cohort'],
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colorscale='Blues',
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text=cohort_data.iloc[:, 1:].values,
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texttemplate='%{text}%',
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textfont={"size": 12},
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))
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fig.update_layout(height=250)
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st.plotly_chart(fig, use_container_width=True)
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# Alerts Section
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st.subheader("Alerts")
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alerts = [
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{"level": "error", "message": "Churn rate exceeded threshold (>5%)"},
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{"level": "warning", "message": "Support ticket volume 20% above average"},
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]
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for alert in alerts:
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if alert["level"] == "error":
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st.error(f"🔴 {alert['message']}")
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elif alert["level"] == "warning":
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st.warning(f"🟡 {alert['message']}")
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```
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## Best Practices
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### Do's
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- **Limit to 5-7 KPIs** - Focus on what matters
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- **Show context** - Comparisons, trends, targets
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- **Use consistent colors** - Red=bad, green=good
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- **Enable drilldown** - From summary to detail
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- **Update appropriately** - Match metric frequency
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### Don'ts
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- **Don't show vanity metrics** - Focus on actionable data
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- **Don't overcrowd** - White space aids comprehension
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- **Don't use 3D charts** - They distort perception
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- **Don't hide methodology** - Document calculations
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- **Don't ignore mobile** - Ensure responsive design
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## Resources
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- [Stephen Few's Dashboard Design](https://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf)
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- [Edward Tufte's Principles](https://www.edwardtufte.com/tufte/)
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- [Google Data Studio Gallery](https://datastudio.google.com/gallery)
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