Argument Crystallization
SkillDev toolsDistill the strongest arguments from each perspective through Argument
Available today. Use it from your connected AI after setup.
No other account needed.
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
| 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
- Collect initial positions with supporting arguments
- Cross-distribute: each perspective critiques and steel-mans others
- Authors refine arguments incorporating strongest critiques
- Identify points of genuine agreement vs. irreducible tensions
- 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.
| Tactic | When to use |
|---|---|
| disagreement-mapping | Map disagreement structure by collecting judgments, clustering opinions, extracting arguments per cluster, and visualizing fault lines. |
| iterative-convergence-round | Execute 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.
| SOP | When to use |
|---|---|
| consensus-synthesis | Synthesize 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