Argument Crystallization

SkillDev tools

Distill the strongest arguments from each perspective through Argument

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Argument Crystallization skill

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/argument-crystallization/SKILL.md and read by ahel’s review.

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

ParameterConstraint
Rounds2–3 (refine arguments, not opinions)
Perspectives≥4 independent
Argument quality gateEach argument must be steel-manned

State Ledger

KeyTypeDescription
questionstringThe deliberation question
perspectivesarrayContributing perspectives
initial_argumentsarrayFirst-round arguments
critiquesarrayCross-perspective critiques
refined_argumentsarraySteel-manned final arguments
synthesisobjectPoints 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>

Available Tactics

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

TacticWhen to use
disagreement-mappingMap disagreement structure by collecting judgments, clustering opinions, extracting arguments per cluster, and visualizing fault lines.
iterative-convergence-roundExecute one full Delphi round — collect judgments, distribute anonymous feedback, measure consensus, decide whether to continue.

Available SOPs

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

SOPWhen to use
consensus-synthesisSynthesize all rounds into a final consensus report documenting agreements, dissent, and process.

Signals

GitHub stars
469
Forks
37
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
argument-crystallization
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine