Agent skill

scenario-narrative-generator

Scenario narrative generation skill for creating vivid, consistent future scenario descriptions

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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/scenario-narrative-generator

Metadata

Additional technical details for this skill

domain
business
category
collaboration
priority
medium
specialization
decision-intelligence
tools libraries
[
    "LLM APIs",
    "jinja2",
    "markdown"
]

SKILL.md

Scenario Narrative Generator

Overview

The Scenario Narrative Generator skill creates vivid, internally consistent narratives for strategic scenarios. It transforms driving forces and uncertainties into compelling stories that help stakeholders envision alternative futures and test strategic options.

Capabilities

  • Driving forces integration
  • Consistency checking across scenario elements
  • Narrative arc construction
  • Key event identification
  • Implication extraction
  • Headline generation
  • Persona-in-scenario development
  • Scenario comparison tables

Used By Processes

  • Strategic Scenario Development
  • War Gaming and Competitive Response Modeling
  • What-If Analysis Framework

Usage

Scenario Framework

python
# Define scenario framework
scenario_framework = {
    "focus_question": "What will the enterprise software market look like in 2030?",
    "time_horizon": "2030",
    "critical_uncertainties": [
        {
            "name": "AI Adoption Rate",
            "dimension": "Technology",
            "poles": ["Rapid AI Integration", "Gradual AI Adoption"]
        },
        {
            "name": "Regulatory Environment",
            "dimension": "Political",
            "poles": ["Tech-Friendly Regulation", "Restrictive Regulation"]
        }
    ],
    "scenario_matrix": {
        "scenarios": [
            {"name": "AI Explosion", "position": ["Rapid AI", "Tech-Friendly"]},
            {"name": "Regulated Innovation", "position": ["Rapid AI", "Restrictive"]},
            {"name": "Steady Progress", "position": ["Gradual AI", "Tech-Friendly"]},
            {"name": "Digital Caution", "position": ["Gradual AI", "Restrictive"]}
        ]
    }
}

Scenario Elements

python
# Define scenario elements
scenario_elements = {
    "scenario_name": "AI Explosion",
    "driving_forces": {
        "technology": "AI capabilities advance rapidly, with AGI breakthroughs",
        "economy": "Massive productivity gains fuel economic growth",
        "society": "Workforce disruption creates social tension",
        "regulation": "Governments adopt innovation-friendly policies"
    },
    "key_events": [
        {"year": 2025, "event": "First enterprise AGI deployment"},
        {"year": 2026, "event": "50% of software written by AI"},
        {"year": 2027, "event": "Major productivity leap in white-collar work"},
        {"year": 2028, "event": "Traditional software vendors consolidate"},
        {"year": 2029, "event": "New AI-native competitors dominate"},
        {"year": 2030, "event": "Enterprise software market unrecognizable"}
    ],
    "stakeholder_impacts": {
        "customers": "Expect AI-first solutions, willing to pay premium for automation",
        "competitors": "AI-native startups disrupt incumbents",
        "employees": "Massive reskilling required",
        "investors": "Flight to AI leaders, traditional valuations collapse"
    }
}

Narrative Generation

python
# Generate narrative
narrative_config = {
    "scenario_name": "AI Explosion",
    "style": "journalist_from_the_future",
    "length": "1500_words",
    "structure": {
        "headline": True,
        "opening_hook": True,
        "timeline_narrative": True,
        "stakeholder_vignettes": True,
        "implications_summary": True
    },
    "persona": {
        "include": True,
        "name": "Sarah Chen",
        "role": "CIO of a mid-size manufacturer",
        "journey": "How her company navigated this world"
    }
}

Consistency Check

python
# Check narrative consistency
consistency_check = {
    "checks": [
        {
            "type": "causal_logic",
            "elements": ["rapid_ai", "workforce_disruption"],
            "result": "consistent",
            "note": "AI adoption logically leads to job displacement"
        },
        {
            "type": "timeline",
            "elements": ["AGI_2025", "software_dominance_2026"],
            "result": "plausible",
            "note": "12-month gap is tight but possible given premise"
        },
        {
            "type": "contradiction",
            "elements": ["innovation_friendly_regulation", "strict_ai_oversight"],
            "result": "inconsistent",
            "note": "Resolve: clarify regulation is permissive on development, focused on safety"
        }
    ]
}

Comparison Table

python
# Generate comparison table
comparison_config = {
    "scenarios": ["AI Explosion", "Regulated Innovation", "Steady Progress", "Digital Caution"],
    "dimensions": [
        "Market Size 2030",
        "Number of Major Vendors",
        "AI Penetration Rate",
        "Regulatory Burden",
        "Workforce Impact",
        "Key Success Factors",
        "Strategic Implications"
    ]
}

Input Schema

json
{
  "scenario_framework": {
    "focus_question": "string",
    "time_horizon": "string",
    "critical_uncertainties": ["object"],
    "scenario_matrix": "object"
  },
  "scenario_elements": {
    "driving_forces": "object",
    "key_events": ["object"],
    "stakeholder_impacts": "object"
  },
  "narrative_config": {
    "style": "string",
    "length": "string",
    "structure": "object",
    "persona": "object"
  }
}

Output Schema

json
{
  "narrative": {
    "headline": "string",
    "body": "string (markdown)",
    "word_count": "number"
  },
  "persona_story": {
    "name": "string",
    "journey": "string"
  },
  "key_events_timeline": ["object"],
  "implications": {
    "strategic": ["string"],
    "operational": ["string"],
    "capability_gaps": ["string"]
  },
  "comparison_table": "object",
  "consistency_report": "object"
}

Narrative Styles

Style Characteristics Best For
Journalist News article from the future Vivid, accessible
Historian Looking back at changes Analytical, comprehensive
Day-in-the-Life Personal experience Emotional, relatable
Strategic Briefing Executive summary Time-efficient, action-oriented

Best Practices

  1. Make scenarios vivid and memorable with specific details
  2. Ensure internal consistency within each scenario
  3. Make scenarios sufficiently different from each other
  4. Balance plausibility with challenge to conventional thinking
  5. Include both opportunities and threats
  6. Use personas to make abstract futures tangible
  7. Connect scenarios to strategic decisions

Scenario Quality Criteria

Criterion Description
Plausibility Could this happen given current trends?
Consistency Do elements logically fit together?
Relevance Does it address the focus question?
Differentiation Is it distinct from other scenarios?
Usability Can stakeholders engage with it?
Challenge Does it stretch conventional thinking?

Integration Points

  • Feeds into War Game Orchestrator for competitive scenarios
  • Connects with System Dynamics Modeler for quantification
  • Supports Scenario Planner agent
  • Integrates with Strategic Options Analyst for strategy testing

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