PaperclipOrg
Claude Agent Skill · by Wshobson

Data Storytelling

Transforms spreadsheets and analytics into executive-ready narratives using proven storytelling frameworks like problem-solution and trend analysis. Structures

Install
Terminal · npx
$npx skills add https://github.com/wshobson/agents --skill data-storytelling
Works with Paperclip

How Data Storytelling fits into a Paperclip company.

Data Storytelling drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
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Source file
SKILL.md447 lines
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---name: data-storytellingdescription: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.--- # Data Storytelling Transform raw data into compelling narratives that drive decisions and inspire action. ## When to Use This Skill - Presenting analytics to executives- Creating quarterly business reviews- Building investor presentations- Writing data-driven reports- Communicating insights to non-technical audiences- Making recommendations based on data ## Core Concepts ### 1. Story Structure ```Setup → Conflict → Resolution Setup: Context and baselineConflict: The problem or opportunityResolution: Insights and recommendations``` ### 2. Narrative Arc ```1. Hook: Grab attention with surprising insight2. Context: Establish the baseline3. Rising Action: Build through data points4. Climax: The key insight5. Resolution: Recommendations6. Call to Action: Next steps``` ### 3. Three Pillars | Pillar        | Purpose  | Components                       || ------------- | -------- | -------------------------------- || **Data**      | Evidence | Numbers, trends, comparisons     || **Narrative** | Meaning  | Context, causation, implications || **Visuals**   | Clarity  | Charts, diagrams, highlights     | ## Story Frameworks ### Framework 1: The Problem-Solution Story ```markdown# Customer Churn Analysis ## The Hook "We're losing $2.4M annually to preventable churn." ## The Context - Current churn rate: 8.5% (industry average: 5%)- Average customer lifetime value: $4,800- 500 customers churned last quarter ## The Problem Analysis of churned customers reveals a pattern: - 73% churned within first 90 days- Common factor: < 3 support interactions- Low feature adoption in first month ## The Insight [Show engagement curve visualization]Customers who don't engage in the first 14 daysare 4x more likely to churn. ## The Solution 1. Implement 14-day onboarding sequence2. Proactive outreach at day 73. Feature adoption tracking ## Expected Impact - Reduce early churn by 40%- Save $960K annually- Payback period: 3 months ## Call to Action Approve $50K budget for onboarding automation.``` ### Framework 2: The Trend Story ```markdown# Q4 Performance Analysis ## Where We Started Q3 ended with $1.2M MRR, 15% below target.Team morale was low after missed goals. ## What Changed [Timeline visualization] - Oct: Launched self-serve pricing- Nov: Reduced friction in signup- Dec: Added customer success calls ## The Transformation [Before/after comparison chart]| Metric | Q3 | Q4 | Change ||----------------|--------|--------|--------|| Trial → Paid | 8% | 15% | +87% || Time to Value | 14 days| 5 days | -64% || Expansion Rate | 2% | 8% | +300% | ## Key Insight Self-serve + high-touch creates compound growth.Customers who self-serve AND get a success callhave 3x higher expansion rate. ## Going Forward Double down on hybrid model.Target: $1.8M MRR by Q2.``` ### Framework 3: The Comparison Story ```markdown# Market Opportunity Analysis ## The Question Should we expand into EMEA or APAC first? ## The Comparison [Side-by-side market analysis] ### EMEA - Market size: $4.2B- Growth rate: 8%- Competition: High- Regulatory: Complex (GDPR)- Language: Multiple ### APAC - Market size: $3.8B- Growth rate: 15%- Competition: Moderate- Regulatory: Varied- Language: Multiple ## The Analysis [Weighted scoring matrix visualization] | Factor      | Weight | EMEA Score | APAC Score || ----------- | ------ | ---------- | ---------- || Market Size | 25%    | 5          | 4          || Growth      | 30%    | 3          | 5          || Competition | 20%    | 2          | 4          || Ease        | 25%    | 2          | 3          || **Total**   |        | **2.9**    | **4.1**    | ## The Recommendation APAC first. Higher growth, less competition.Start with Singapore hub (English, business-friendly).Enter EMEA in Year 2 with localization ready. ## Risk Mitigation - Timezone coverage: Hire 24/7 support- Cultural fit: Local partnerships- Payment: Multi-currency from day 1``` ## Visualization Techniques ### Technique 1: Progressive Reveal ```markdownStart simple, add layers: Slide 1: "Revenue is growing" [single line chart]Slide 2: "But growth is slowing" [add growth rate overlay]Slide 3: "Driven by one segment" [add segment breakdown]Slide 4: "Which is saturating" [add market share]Slide 5: "We need new segments" [add opportunity zones]``` ### Technique 2: Contrast and Compare ```markdownBefore/After:┌─────────────────┬─────────────────┐│ BEFORE │ AFTER ││ │ ││ Process: 5 days│ Process: 1 day ││ Errors: 15% │ Errors: 2% ││ Cost: $50/unit │ Cost: $20/unit │└─────────────────┴─────────────────┘ This/That (emphasize difference):┌─────────────────────────────────────┐│ CUSTOMER A vs B ││ ┌──────────┐ ┌──────────┐ ││ │ ████████ │ │ ██ │ ││ │ $45,000 │ │ $8,000 │ ││ │ LTV │ │ LTV │ ││ └──────────┘ └──────────┘ ││ Onboarded No onboarding │└─────────────────────────────────────┘``` ### Technique 3: Annotation and Highlight ```pythonimport matplotlib.pyplot as pltimport pandas as pd fig, ax = plt.subplots(figsize=(12, 6)) # Plot the main dataax.plot(dates, revenue, linewidth=2, color='#2E86AB') # Add annotation for key eventsax.annotate(    'Product Launch\n+32% spike',    xy=(launch_date, launch_revenue),    xytext=(launch_date, launch_revenue * 1.2),    fontsize=10,    arrowprops=dict(arrowstyle='->', color='#E63946'),    color='#E63946') # Highlight a regionax.axvspan(growth_start, growth_end, alpha=0.2, color='green',           label='Growth Period') # Add threshold lineax.axhline(y=target, color='gray', linestyle='--',           label=f'Target: ${target:,.0f}') ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')ax.legend()``` ## Presentation Templates ### Template 1: Executive Summary Slide ```┌─────────────────────────────────────────────────────────────┐│  KEY INSIGHT                                                ││  ══════════════════════════════════════════════════════════││                                                             ││  "Customers who complete onboarding in week 1              ││   have 3x higher lifetime value"                           ││                                                             │├──────────────────────┬──────────────────────────────────────┤│                      │                                      ││  THE DATA            │  THE IMPLICATION                     ││                      │                                      ││  Week 1 completers:  │  ✓ Prioritize onboarding UX         ││  • LTV: $4,500       │  ✓ Add day-1 success milestones     ││  • Retention: 85%    │  ✓ Proactive week-1 outreach        ││  • NPS: 72           │                                      ││                      │  Investment: $75K                    ││  Others:             │  Expected ROI: 8x                    ││  • LTV: $1,500       │                                      ││  • Retention: 45%    │                                      ││  • NPS: 34           │                                      ││                      │                                      │└──────────────────────┴──────────────────────────────────────┘``` ### Template 2: Data Story Flow ```Slide 1: THE HEADLINE"We can grow 40% faster by fixing onboarding" Slide 2: THE CONTEXTCurrent state metricsIndustry benchmarksGap analysis Slide 3: THE DISCOVERYWhat the data revealedSurprising findingPattern identification Slide 4: THE DEEP DIVERoot cause analysisSegment breakdownsStatistical significance Slide 5: THE RECOMMENDATIONProposed actionsResource requirementsTimeline Slide 6: THE IMPACTExpected outcomesROI calculationRisk assessment Slide 7: THE ASKSpecific requestDecision neededNext steps``` ### Template 3: One-Page Dashboard Story ```markdown# Monthly Business Review: January 2024 ## THE HEADLINE Revenue up 15% but CAC increasing faster than LTV ## KEY METRICS AT A GLANCE ┌────────┬────────┬────────┬────────┐│ MRR │ NRR │ CAC │ LTV ││ $125K │ 108% │ $450 │ $2,200 ││ ▲15% │ ▲3% │ ▲22% │ ▲8% │└────────┴────────┴────────┴────────┘ ## WHAT'S WORKING ✓ Enterprise segment growing 25% MoM✓ Referral program driving 30% of new logos✓ Support satisfaction at all-time high (94%) ## WHAT NEEDS ATTENTION ✗ SMB acquisition cost up 40%✗ Trial conversion down 5 points✗ Time-to-value increased by 3 days ## ROOT CAUSE [Mini chart showing SMB vs Enterprise CAC trend]SMB paid ads becoming less efficient.CPC up 35% while conversion flat. ## RECOMMENDATION 1. Shift $20K/mo from paid to content2. Launch SMB self-serve trial3. A/B test shorter onboarding ## NEXT MONTH'S FOCUS - Launch content marketing pilot- Complete self-serve MVP- Reduce time-to-value to < 7 days``` ## Writing Techniques ### Headlines That Work ```markdownBAD: "Q4 Sales Analysis"GOOD: "Q4 Sales Beat Target by 23% - Here's Why" BAD: "Customer Churn Report"GOOD: "We're Losing $2.4M to Preventable Churn" BAD: "Marketing Performance"GOOD: "Content Marketing Delivers 4x ROI vs. Paid" Formula:[Specific Number] + [Business Impact] + [Actionable Context]``` ### Transition Phrases ```markdownBuilding the narrative:• "This leads us to ask..."• "When we dig deeper..."• "The pattern becomes clear when..."• "Contrast this with..." Introducing insights:• "The data reveals..."• "What surprised us was..."• "The inflection point came when..."• "The key finding is..." Moving to action:• "This insight suggests..."• "Based on this analysis..."• "The implication is clear..."• "Our recommendation is..."``` ### Handling Uncertainty ```markdownAcknowledge limitations:• "With 95% confidence, we can say..."• "The sample size of 500 shows..."• "While correlation is strong, causation requires..."• "This trend holds for [segment], though [caveat]..." Present ranges:• "Impact estimate: $400K-$600K"• "Confidence interval: 15-20% improvement"• "Best case: X, Conservative: Y"``` ## Best Practices ### Do's - **Start with the "so what"** - Lead with insight- **Use the rule of three** - Three points, three comparisons- **Show, don't tell** - Let data speak- **Make it personal** - Connect to audience goals- **End with action** - Clear next steps ### Don'ts - **Don't data dump** - Curate ruthlessly- **Don't bury the insight** - Front-load key findings- **Don't use jargon** - Match audience vocabulary- **Don't show methodology first** - Context, then method- **Don't forget the narrative** - Numbers need meaning