Data storytelling

Skill a5c-ai/babysitter/library/specializations/domains/business/decision-intelligence/skills/data-storytelling

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npx -y skills add a5c-ai/babysitter --skill data-storytelling

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Narrative generation skill for transforming analytical insights into compelling business stories

SKILL.md

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Data Storytelling

Overview

The Data Storytelling skill transforms analytical insights into compelling, actionable business narratives. It bridges the gap between complex analysis and executive decision-making by generating clear, contextual, and persuasive communications tailored to different audiences.

Capabilities

Insight prioritization and selection
Narrative structure generation
Chart annotation automation
Key takeaway extraction
Executive summary generation
Recommendation framing
Action item identification
Audience-appropriate language adaptation

Used By Processes

Insight-to-Action Process
Executive Dashboard Development
Decision Documentation and Learning

Usage

Insight Input

# Analytical insights to narrate
insights = {
    "context": {
        "analysis_type": "quarterly_performance",
        "period": "Q3 2024",
        "audience": "executive_leadership",
        "objective": "investment_decision"
    },
    "key_findings": [
        {
            "metric": "Revenue",
            "value": 12500000,
            "change": 0.15,
            "benchmark": "above_target",
            "significance": "high",
            "drivers": ["new_product_launch", "market_expansion"]
        },
        {
            "metric": "Customer Acquisition Cost",
            "value": 185,
            "change": 0.22,
            "benchmark": "above_target",
            "significance": "medium",
            "drivers": ["increased_competition", "channel_mix_shift"]
        }
    ],
    "supporting_data": {
        "visualizations": ["revenue_trend.png", "cac_breakdown.png"],
        "tables": ["segment_performance.csv"]
    }
}

Narrative Configuration

# Narrative structure configuration
narrative_config = {
    "structure": "situation_complication_resolution",
    "tone": "professional",
    "length": "executive_summary",  # or "detailed_report"
    "format": "markdown",
    "sections": [
        "headline",
        "key_takeaways",
        "context",
        "analysis",
        "recommendations",
        "next_steps"
    ],
    "emphasis": "actionable_recommendations"
}

Audience Adaptation

# Audience-specific settings
audience_profiles = {
    "executive_leadership": {
        "detail_level": "high_level",
        "jargon": "minimal",
        "focus": "strategic_implications",
        "format_preference": "bullet_points",
        "time_available": "2_minutes"
    },
    "technical_team": {
        "detail_level": "detailed",
        "jargon": "acceptable",
        "focus": "methodology_and_data",
        "format_preference": "full_narrative",
        "time_available": "15_minutes"
    },
    "board_of_directors": {
        "detail_level": "summary",
        "jargon": "none",
        "focus": "business_impact",
        "format_preference": "visual_heavy",
        "time_available": "5_minutes"
    }
}

Narrative Structures

Structure	Best For	Flow
SCR (Situation-Complication-Resolution)	Problem-solving	Context -> Challenge -> Solution
Pyramid	Executive updates	Conclusion -> Supporting points -> Details
Before-After-Bridge	Change proposals	Current state -> Future state -> How to get there
STAR	Case studies	Situation -> Task -> Action -> Result
What-So What-Now What	Quick insights	Finding -> Implication -> Action

Input Schema

{
  "insights": {
    "context": "object",
    "key_findings": ["object"],
    "supporting_data": "object"
  },
  "narrative_config": {
    "structure": "string",
    "tone": "string",
    "length": "string",
    "sections": ["string"]
  },
  "audience": {
    "profile": "string",
    "detail_level": "string",
    "time_available": "string"
  }
}

Output Schema

{
  "narrative": {
    "headline": "string",
    "executive_summary": "string",
    "sections": {
      "section_name": "string (markdown)"
    },
    "key_takeaways": ["string"],
    "recommendations": ["string"],
    "next_steps": [
      {
        "action": "string",
        "owner": "string",
        "timeline": "string"
      }
    ]
  },
  "annotations": {
    "visualization_id": "string annotation"
  },
  "metadata": {
    "word_count": "number",
    "reading_time": "string",
    "complexity_score": "number"
  }
}

Best Practices

Lead with the most important insight (inverted pyramid)
Use specific numbers, not vague descriptors
Connect data to business outcomes
Include clear calls to action
Acknowledge limitations and uncertainties
Use active voice and strong verbs
Test narrative with representative audience member

Annotation Guidelines

For chart annotations:

Highlight the key insight, not just describe the data
Use arrows and callouts sparingly
Provide context (comparisons, benchmarks)
Include "so what" implications

Integration Points

Receives insights from all analysis skills
Connects with Decision Visualization for annotated charts
Feeds into Decision Journal for documentation
Supports Insight Translator agent for communication