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argument-mapping

Extract and visualize argument structures from textual passages using Toulmin reconstruction.

33|6|Updated Mar 17, 2026
Search Tags:#argument mapping#discourse analysis#toulmin reconstruction#argumentation graphs#circular reasoning detection#networkx visualization#nlp premise extraction
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill argument-mapping-xjtulyc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: argument-mapping
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/14-philosophy/argument-mapping
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill argument-mapping-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, pandas, matplotlib, nltk.

What problem does it solve?

This Skill helps you transform philosophical or policy passages into explicit argument structures so you can evaluate what is being claimed, what supports it, and where reasoning may break down.

Core Features & Use Cases

  • Toulmin reconstruction: break arguments into claim, data, warrant, backing, rebuttal, and qualifier.
  • Support/attack graph analysis: build directed graphs and visualize them with color-coded edges.
  • Reasoning diagnostics: detect circular reasoning in support relations and check inconsistency via mapped negations.
  • Argument scheme classification & premise extraction: classify dominant schemes using heuristics and extract propositions/relation candidates from text.

Quick Start

Use the argument-mapping Skill to reconstruct a Toulmin-style argument from a passage and return a visual support/attack argument map with detected circularity.

Frequently Asked Questions about argument-mapping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an argument map from a philosophical text passage?▼

To build an argument map, this Skill extracts propositions and support/attack relations from text using NLP, models them as a directed graph with NetworkX, and renders a publication-ready visualization of the argument structure.

Can I detect circular reasoning in a support graph using NetworkX?▼

Yes, you can detect circular reasoning by running cycle checks on directed support edges within the NetworkX argumentation graph. The Skill identifies circular dependencies and checks inconsistency via mapped negations in the extracted argument structure.

Does this Skill require NLTK and Python libraries to extract argument premises?▼

Yes, this Skill requires NLTK for NLP premise extraction, NetworkX for directed graph modeling, pandas for data handling, and matplotlib to render publication-ready visualizations of the argument maps.

What is the best way to visualize argumentation graphs for academic discourse analysis?▼

The best way to visualize argumentation graphs is by generating color-coded directed graphs with NetworkX and matplotlib. This Skill renders publication-ready support/attack visualizations suitable for academic analysis and teaching materials.

How do I classify argument schemes and extract premises from policy passages?▼

This Skill classifies dominant argument schemes using heuristics and extracts propositions alongside relation candidates from policy text. It structures the extracted premises into an argumentation graph for diagnostic evaluation.

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