ReddRadar
Agent skill
data-storytelling
Narrative generation skill for transforming analytical insights into compelling business stories
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/data-storytelling
Metadata
Additional technical details for this skill
- domain
- business
- category
- visualization
- priority
- medium
- specialization
- decision-intelligence
- tools libraries
-
[ "openai/anthropic APIs", "jinja2", "markdown" ] - shared candidate
- YES
SKILL.md
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
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