argument-crystallization

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Distill the strongest arguments from each perspective through Argument Delphi or Dialectical Delphi methods.

yogsoth-ai By yogsoth-ai schedule Updated 5/19/2026

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

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>
Install via CLI
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill argument-crystallization
Repository Details
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