argument-crystallization
Argument Crystallization distills the strongest possible arguments from each perspective on a contested question using Argument Delphi and Dialectical Delphi methods, prioritizing argument rigor over consensus. Use this skill for policy deliberation, interdisciplinary disputes, or pre-decision analysis where decision-makers need steel-manned positions articulated at their best, identify genuine points of agreement, and understand where tensions between viewpoints are genuinely irreducible.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/argument-crystallization && cp -r /tmp/argument-crystallization/skills/argument-crystallization ~/.claude/skills/argument-crystallizationSKILL.md
# 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>
```Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems