morphological-scenario
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/morphological-scenarioSystematically generate and filter plausible scenarios using morphological analysis and consistency checks
- Enumerates all possible combinations of key uncertainty parameters
- Uses Zwicky Box and CCA matrix for structured scenario development
- Filters combinations for internal consistency and plausibility
- Produces a set of robust scenarios for narrative construction and impact assessment
SKILL.md
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--- name: morphological-scenario description: "What are all possible combinations? — Zwicky Box construction with CCA consistency filtering for systematic scenario enumeration" version: 1.0.0 category: experiment-execution type: strategy used-by: scenario-planning sops: - scenario-driver-identification - parameter-enumeration - consistency-pair-evaluation - scenario-narrative-construction - scenario-impact-assessment - robustness-scoring - scenario-synthesis tactics: - parameter-space-construction - cross-consistency-filtering - strategy-robustness-testing --- # Strategy: Morphological Scenario ## Methodology General Morphological Analysis (Zwicky) combined with Cross-Consistency Assessment (Ritchey). Systematically enumerate all possible combinations of key uncertainty parameters, filter for internal consistency, and assess surviving configurations as plausible scenarios. Key principles: - **Completeness**: Every relevant parameter dimension is included - **MECE values**: Each parameter has mutually exclusive, collectively exhaustive values - **Pairwise consistency**: Filter via CCA matrix before narrative construction - **Combinatorial discipline**: Let the morphological field drive discovery, not intuition ## Execution Flow 1. **Identify drivers** → spawn `scenario-driver-identification` - Input: research context, planning horizon - Output: 5-8 key uncertainty drivers 2. **Enumerate parameters** → spawn `parameter-enumeration` - Input: driver list - Output: Zwicky Box (parameter × value matrix) 3. **Consistency filtering** → spawn `consistency-pair-evaluation` - Input: Zwicky Box - Output: CCA matrix, surviving configurations 4. **Narrative construction** → spawn `scenario-narrative-construction` (per surviving config) - Input: parameter configuration - Output: scenario narrative 5. **Impact assessment** → spawn `scenario-impact-assessment` (per scenario) - Input: scenario narrative, research approach - Output: impact analysis 6. **Robustness scoring** → spawn `robustness-scoring` - Input: all impact assessments - Output: robustness index 7. **Synthesis** → spawn `scenario-synthesis` - Input: all scenarios, robustness scores - Output: final scenario portfolio report ## Budget Gate | Step | Token Budget | Notes | |------|-------------|-------| | Driver identification | 8K | Single pass | | Parameter enumeration | 10K | May iterate once | | Consistency filtering | 15K | O(n²) pairwise | | Narrative construction | 12K × N | N = surviving configs (typically 4-8) | | Impact assessment | 10K × N | Per scenario | | Robustness scoring | 8K | Aggregation | | Synthesis | 12K | Final compilation |