argument-crystallization
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/argument-crystallization**Purpose:** Rather than converging on a single answer, crystallize the strongest possible arguments for each position. Uses Argument Delphi (focus on argument quality over agreement) and Dialectical Delphi (thesis-antithesis-synthesis) to produce the most rigorous version of each stance.
SKILL.md
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---
name: argument-crystallization
description: Distill the strongest arguments from each perspective through Argument Delphi or Dialectical Delphi methods.
used-by: structured-consensus
---
# Argument Crystallization
**Purpose:** Rather than converging on a single answer, crystallize the strongest possible arguments for each position. Uses Argument Delphi (focus on argument quality over agreement) and Dialectical Delphi (thesis-antithesis-synthesis) to produce the most rigorous version of each stance.
**When to use:**
- Policy deliberation requiring clear pro/con articulation
- Interdisciplinary disputes where each field has valid concerns
- Pre-decision analysis where decision-makers need best arguments
- Situations where the goal is argument quality, not agreement
## Budget
| Parameter | Constraint |
|-----------|-----------|
| Rounds | 2–3 (refine arguments, not opinions) |
| Perspectives | ≥4 independent |
| Argument quality gate | Each argument must be steel-manned |
## State Ledger
| Key | Type | Description |
|-----|------|-------------|
| question | string | The deliberation question |
| perspectives | array | Contributing perspectives |
| initial_arguments | array | First-round arguments |
| critiques | array | Cross-perspective critiques |
| refined_arguments | array | Steel-manned final arguments |
| synthesis | object | Points of agreement and irreducible tensions |
## Available Tactics
- **disagreement-mapping** — Identify argument clusters
- **iterative-convergence-round** — Refine arguments across rounds
## Available SOPs
- judgment-collection
- cluster-analysis
- argument-extraction
- feedback-distribution
- consensus-measurement
- consensus-synthesis
## Execution Guidance
1. Collect initial positions with supporting arguments
2. Cross-distribute: each perspective critiques and steel-mans others
3. Authors refine arguments incorporating strongest critiques
4. Identify points of genuine agreement vs. irreducible tensions
5. Produce crystallized argument map with quality ratings
## Output Format
```yaml
positions:
- label: <position name>
strongest_arguments: [...]
acknowledged_weaknesses: [...]
steel_man_version: <best possible formulation>
agreements:
- point: <shared conclusion>
strength: <how robust>
irreducible_tensions:
- between: [position_a, position_b]
nature: <empirical/value/priority>
why_irreducible: <explanation>
```
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