scenario-narrative-generator
Scenario narrative generation skill for creating vivid, consistent future scenario descriptions
Best use case
scenario-narrative-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Scenario narrative generation skill for creating vivid, consistent future scenario descriptions
Teams using scenario-narrative-generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scenario-narrative-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scenario-narrative-generator Compares
| Feature / Agent | scenario-narrative-generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Scenario narrative generation skill for creating vivid, consistent future scenario descriptions
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 testingRelated Skills
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