---
name: yogsoth-ai/morphological-scenario
source: https://app.decimal.ai/s/yogsoth-ai-morphological-scenario@1/SKILL.md
source_sha256: 378014c2f181
---

# 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 |

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## Available Tactics

Optional, no fixed order; the final leaf is always a sop.

| Tactic | When to use |
| --- | --- |
| cross-consistency-filtering | Orchestrates pairwise consistency evaluation and narrative construction to filter the morphological field |
| parameter-space-construction | Orchestrates driver identification and parameter enumeration to build the complete morphological field |
| strategy-robustness-testing | Orchestrates impact assessment and robustness scoring to evaluate research approach resilience across scenarios |

## Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use |
| --- | --- |
| experiment-execution-consistency-pair-evaluation | Pairwise consistency assessment using Cross-Consistency Assessment (CCA) matrix |
| parameter-enumeration | Enumerate possible values for each uncertainty driver using MECE principles |
| robustness-scoring | Compute robustness index across scenarios with sensitivity analysis |
| scenario-driver-identification | Identify key uncertainty drivers using PESTEL framework scanning |
| scenario-impact-assessment | Assess each scenario's impact on the research approach across multiple dimensions |
| scenario-narrative-construction | Build rich narratives for surviving morphological configurations using Shell method |
| scenario-synthesis | Comprehensive scenario analysis report synthesizing all scenario work |

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