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feat: add 5 new specialized agents with 20 skills
Add domain expert agents with comprehensive skill sets: - service-mesh-expert (cloud-infrastructure): Istio/Linkerd patterns, mTLS, observability - event-sourcing-architect (backend-development): CQRS, event stores, projections, sagas - vector-database-engineer (llm-application-dev): embeddings, similarity search, hybrid search - monorepo-architect (developer-essentials): Nx, Turborepo, Bazel, pnpm workspaces - threat-modeling-expert (security-scanning): STRIDE, attack trees, security requirements Update all documentation to reflect correct counts: - 67 plugins, 99 agents, 107 skills, 71 commands
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Seth Hobson
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plugins/business-analytics/skills/data-storytelling/SKILL.md
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plugins/business-analytics/skills/data-storytelling/SKILL.md
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---
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name: data-storytelling
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description: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
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---
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# Data Storytelling
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Transform raw data into compelling narratives that drive decisions and inspire action.
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## When to Use This Skill
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- Presenting analytics to executives
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- Creating quarterly business reviews
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- Building investor presentations
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- Writing data-driven reports
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- Communicating insights to non-technical audiences
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- Making recommendations based on data
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## Core Concepts
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### 1. Story Structure
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```
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Setup → Conflict → Resolution
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Setup: Context and baseline
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Conflict: The problem or opportunity
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Resolution: Insights and recommendations
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```
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### 2. Narrative Arc
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```
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1. Hook: Grab attention with surprising insight
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2. Context: Establish the baseline
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3. Rising Action: Build through data points
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4. Climax: The key insight
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5. Resolution: Recommendations
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6. Call to Action: Next steps
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```
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### 3. Three Pillars
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| Pillar | Purpose | Components |
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|--------|---------|------------|
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| **Data** | Evidence | Numbers, trends, comparisons |
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| **Narrative** | Meaning | Context, causation, implications |
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| **Visuals** | Clarity | Charts, diagrams, highlights |
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## Story Frameworks
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### Framework 1: The Problem-Solution Story
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```markdown
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# Customer Churn Analysis
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## The Hook
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"We're losing $2.4M annually to preventable churn."
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## The Context
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- Current churn rate: 8.5% (industry average: 5%)
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- Average customer lifetime value: $4,800
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- 500 customers churned last quarter
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## The Problem
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Analysis of churned customers reveals a pattern:
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- 73% churned within first 90 days
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- Common factor: < 3 support interactions
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- Low feature adoption in first month
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## The Insight
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[Show engagement curve visualization]
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Customers who don't engage in the first 14 days
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are 4x more likely to churn.
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## The Solution
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1. Implement 14-day onboarding sequence
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2. Proactive outreach at day 7
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3. Feature adoption tracking
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## Expected Impact
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- Reduce early churn by 40%
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- Save $960K annually
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- Payback period: 3 months
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## Call to Action
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Approve $50K budget for onboarding automation.
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```
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### Framework 2: The Trend Story
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```markdown
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# Q4 Performance Analysis
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## Where We Started
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Q3 ended with $1.2M MRR, 15% below target.
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Team morale was low after missed goals.
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## What Changed
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[Timeline visualization]
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- Oct: Launched self-serve pricing
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- Nov: Reduced friction in signup
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- Dec: Added customer success calls
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## The Transformation
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[Before/after comparison chart]
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| Metric | Q3 | Q4 | Change |
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|----------------|--------|--------|--------|
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| Trial → Paid | 8% | 15% | +87% |
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| Time to Value | 14 days| 5 days | -64% |
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| Expansion Rate | 2% | 8% | +300% |
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## Key Insight
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Self-serve + high-touch creates compound growth.
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Customers who self-serve AND get a success call
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have 3x higher expansion rate.
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## Going Forward
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Double down on hybrid model.
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Target: $1.8M MRR by Q2.
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```
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### Framework 3: The Comparison Story
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```markdown
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# Market Opportunity Analysis
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## The Question
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Should we expand into EMEA or APAC first?
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## The Comparison
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[Side-by-side market analysis]
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### EMEA
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- Market size: $4.2B
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- Growth rate: 8%
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- Competition: High
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- Regulatory: Complex (GDPR)
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- Language: Multiple
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### APAC
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- Market size: $3.8B
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- Growth rate: 15%
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- Competition: Moderate
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- Regulatory: Varied
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- Language: Multiple
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## The Analysis
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[Weighted scoring matrix visualization]
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| Factor | Weight | EMEA Score | APAC Score |
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|-------------|--------|------------|------------|
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| Market Size | 25% | 5 | 4 |
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| Growth | 30% | 3 | 5 |
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| Competition | 20% | 2 | 4 |
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| Ease | 25% | 2 | 3 |
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| **Total** | | **2.9** | **4.1** |
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## The Recommendation
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APAC first. Higher growth, less competition.
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Start with Singapore hub (English, business-friendly).
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Enter EMEA in Year 2 with localization ready.
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## Risk Mitigation
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- Timezone coverage: Hire 24/7 support
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- Cultural fit: Local partnerships
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- Payment: Multi-currency from day 1
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```
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## Visualization Techniques
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### Technique 1: Progressive Reveal
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```markdown
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Start simple, add layers:
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Slide 1: "Revenue is growing" [single line chart]
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Slide 2: "But growth is slowing" [add growth rate overlay]
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Slide 3: "Driven by one segment" [add segment breakdown]
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Slide 4: "Which is saturating" [add market share]
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Slide 5: "We need new segments" [add opportunity zones]
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```
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### Technique 2: Contrast and Compare
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```markdown
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Before/After:
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┌─────────────────┬─────────────────┐
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│ BEFORE │ AFTER │
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│ │ │
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│ Process: 5 days│ Process: 1 day │
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│ Errors: 15% │ Errors: 2% │
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│ Cost: $50/unit │ Cost: $20/unit │
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└─────────────────┴─────────────────┘
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This/That (emphasize difference):
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┌─────────────────────────────────────┐
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│ CUSTOMER A vs B │
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│ ┌──────────┐ ┌──────────┐ │
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│ │ ████████ │ │ ██ │ │
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│ │ $45,000 │ │ $8,000 │ │
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│ │ LTV │ │ LTV │ │
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│ └──────────┘ └──────────┘ │
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│ Onboarded No onboarding │
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└─────────────────────────────────────┘
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```
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### Technique 3: Annotation and Highlight
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```python
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import matplotlib.pyplot as plt
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import pandas as pd
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fig, ax = plt.subplots(figsize=(12, 6))
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# Plot the main data
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ax.plot(dates, revenue, linewidth=2, color='#2E86AB')
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# Add annotation for key events
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ax.annotate(
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'Product Launch\n+32% spike',
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xy=(launch_date, launch_revenue),
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xytext=(launch_date, launch_revenue * 1.2),
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fontsize=10,
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arrowprops=dict(arrowstyle='->', color='#E63946'),
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color='#E63946'
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)
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# Highlight a region
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ax.axvspan(growth_start, growth_end, alpha=0.2, color='green',
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label='Growth Period')
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# Add threshold line
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ax.axhline(y=target, color='gray', linestyle='--',
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label=f'Target: ${target:,.0f}')
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ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')
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ax.legend()
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```
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## Presentation Templates
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### Template 1: Executive Summary Slide
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```
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┌─────────────────────────────────────────────────────────────┐
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│ KEY INSIGHT │
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│ ══════════════════════════════════════════════════════════│
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│ │
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│ "Customers who complete onboarding in week 1 │
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│ have 3x higher lifetime value" │
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│ │
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├──────────────────────┬──────────────────────────────────────┤
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│ │ │
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│ THE DATA │ THE IMPLICATION │
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│ │ │
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│ Week 1 completers: │ ✓ Prioritize onboarding UX │
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│ • LTV: $4,500 │ ✓ Add day-1 success milestones │
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│ • Retention: 85% │ ✓ Proactive week-1 outreach │
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│ • NPS: 72 │ │
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│ │ Investment: $75K │
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│ Others: │ Expected ROI: 8x │
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│ • LTV: $1,500 │ │
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│ • Retention: 45% │ │
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│ • NPS: 34 │ │
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│ │ │
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└──────────────────────┴──────────────────────────────────────┘
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```
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### Template 2: Data Story Flow
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```
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Slide 1: THE HEADLINE
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"We can grow 40% faster by fixing onboarding"
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Slide 2: THE CONTEXT
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Current state metrics
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Industry benchmarks
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Gap analysis
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Slide 3: THE DISCOVERY
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What the data revealed
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Surprising finding
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Pattern identification
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Slide 4: THE DEEP DIVE
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Root cause analysis
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Segment breakdowns
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Statistical significance
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Slide 5: THE RECOMMENDATION
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Proposed actions
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Resource requirements
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Timeline
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Slide 6: THE IMPACT
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Expected outcomes
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ROI calculation
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Risk assessment
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Slide 7: THE ASK
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Specific request
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Decision needed
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Next steps
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```
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### Template 3: One-Page Dashboard Story
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```markdown
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# Monthly Business Review: January 2024
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## THE HEADLINE
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Revenue up 15% but CAC increasing faster than LTV
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## KEY METRICS AT A GLANCE
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┌────────┬────────┬────────┬────────┐
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│ MRR │ NRR │ CAC │ LTV │
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│ $125K │ 108% │ $450 │ $2,200 │
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│ ▲15% │ ▲3% │ ▲22% │ ▲8% │
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└────────┴────────┴────────┴────────┘
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## WHAT'S WORKING
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✓ Enterprise segment growing 25% MoM
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✓ Referral program driving 30% of new logos
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✓ Support satisfaction at all-time high (94%)
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## WHAT NEEDS ATTENTION
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✗ SMB acquisition cost up 40%
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✗ Trial conversion down 5 points
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✗ Time-to-value increased by 3 days
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## ROOT CAUSE
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[Mini chart showing SMB vs Enterprise CAC trend]
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SMB paid ads becoming less efficient.
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CPC up 35% while conversion flat.
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## RECOMMENDATION
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1. Shift $20K/mo from paid to content
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2. Launch SMB self-serve trial
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3. A/B test shorter onboarding
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## NEXT MONTH'S FOCUS
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- Launch content marketing pilot
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- Complete self-serve MVP
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- Reduce time-to-value to < 7 days
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```
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## Writing Techniques
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### Headlines That Work
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```markdown
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BAD: "Q4 Sales Analysis"
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GOOD: "Q4 Sales Beat Target by 23% - Here's Why"
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BAD: "Customer Churn Report"
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GOOD: "We're Losing $2.4M to Preventable Churn"
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BAD: "Marketing Performance"
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GOOD: "Content Marketing Delivers 4x ROI vs. Paid"
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Formula:
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[Specific Number] + [Business Impact] + [Actionable Context]
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```
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### Transition Phrases
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```markdown
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Building the narrative:
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• "This leads us to ask..."
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• "When we dig deeper..."
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• "The pattern becomes clear when..."
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• "Contrast this with..."
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Introducing insights:
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• "The data reveals..."
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• "What surprised us was..."
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• "The inflection point came when..."
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• "The key finding is..."
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Moving to action:
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• "This insight suggests..."
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• "Based on this analysis..."
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• "The implication is clear..."
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• "Our recommendation is..."
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```
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### Handling Uncertainty
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```markdown
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Acknowledge limitations:
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• "With 95% confidence, we can say..."
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• "The sample size of 500 shows..."
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• "While correlation is strong, causation requires..."
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• "This trend holds for [segment], though [caveat]..."
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Present ranges:
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• "Impact estimate: $400K-$600K"
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• "Confidence interval: 15-20% improvement"
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• "Best case: X, Conservative: Y"
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```
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## Best Practices
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### Do's
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- **Start with the "so what"** - Lead with insight
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- **Use the rule of three** - Three points, three comparisons
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- **Show, don't tell** - Let data speak
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- **Make it personal** - Connect to audience goals
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- **End with action** - Clear next steps
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### Don'ts
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- **Don't data dump** - Curate ruthlessly
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- **Don't bury the insight** - Front-load key findings
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- **Don't use jargon** - Match audience vocabulary
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- **Don't show methodology first** - Context, then method
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- **Don't forget the narrative** - Numbers need meaning
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## Resources
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- [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/)
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- [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516)
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- [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)
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