argument-extraction

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/argument-extraction

Extract and steel-man core arguments for a given opinion cluster

  • Identifies key points within an opinion cluster to form robust arguments.
  • Deploys specialized tools to analyze and synthesize multiple perspectives.
  • Uses reasoning and judgment inputs to craft the strongest possible versions of these arguments.
  • Delivers synthesized, steel-manned arguments directly for further analysis or use.

SKILL.md

.github/skills/argument-extractionView on GitHub ↗
---
name: argument-extraction
description: Extract and steel-man the core arguments supporting a given opinion cluster.
execution: subagent
prompt: ./prompt.md
input: cluster, judgments[]
used-by: structured-consensus
---

# Argument Extraction

Extract the core arguments supporting a given opinion cluster and present them in their strongest possible form (steel-manned). Synthesizes reasoning from multiple perspectives within the cluster into coherent, well-structured arguments.

## Execution

Spawn a subagent that takes a cluster characterization and the relevant judgments, then produces a set of steel-manned arguments representing the cluster's position.

## Why Subagent

- Argument extraction requires careful synthesis across multiple inputs
- Steel-manning requires dedicated analytical attention
- Output feeds directly into disagreement-visualization

## HARD-GATE

Output MUST contain: at least 1 argument per cluster, each with `claim`, `evidence`, `reasoning`, and `strength` fields. Arguments must be steel-manned (strongest possible version).

More from yogsoth-ai/de-anthropocentric-research-engine

SkillDescription
abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
ablation-component-mappingMap system architecture to ablatable units for ablation studies
ablation-designDesign ablation studies to isolate component contributions in ML systems
ablation-executionRemove components one by one from a system, record the response/impact of each removal.
abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
abstraction-ladderPerform bisociation at multiple abstraction levels
abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.