What formats does the causality graph output support?

The skill can generate structured JSON data and Graphviz DOT files for visual representation of causal relationships.

What is token novelty in L-Space?

Token novelty measures the 'surprise' or information density of specific tokens, which helps in positioning text within a conceptual manifold for better analysis.

How does it handle code analysis?

It uses a causality graph generator to map out how different parts of a codebase interact, treating the repository as a narrative to identify logical dependencies.

Can I use L-Space for non-technical text?

Yes, L-Space is highly effective for prose, helping users visualize narrative arcs and the thematic tension within stories or long-form content.

What is the primary purpose of the L-Space skill?

L-Space is designed to extract and analyze the deep narrative and causal structures within text and code, providing structured data like causality graphs and novelty curves.

L-Space Narrative & Causality Analyzer

Name: L-Space Narrative & Causality Analyzer
Author: plurigrid

byplurigrid

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Data Science & ML

Extracts narrative arcs, measures token novelty, and generates causality graphs from text and code repositories.

L-Space is a sophisticated skill that treats information as a topological construct, allowing users to analyze the underlying narrative structure and causal relationships within any text or codebase. Inspired by the concept of informational physics, it provides powerful tools for measuring token surprise to determine manifold positioning, extracting complex narrative arcs, and building causality graphs to understand how elements within a system influence one another. Whether you are auditing a legacy software repository or analyzing the coherence of technical documentation, L-Space offers a unique, multidimensional perspective on data evolution and structural consistency.

Key Features

01Causality graph generation with support for Python and other languages

02Bidirectional causation modeling for complex informational systems

03Automated narrative arc and causality extraction from prose and code

04Token novelty measurement for manifold positioning and surprise analysis

05Topological data analysis using sheaves on posets of intervals

068 GitHub stars

Use Cases

01Analyzing technical documentation to ensure narrative coherence and information density

02Identifying structural anomalies or 'narrative collapse' in complex datasets and knowledge bases

03Mapping the logical flow and dependency causality within large-scale software repositories

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi l-space

For use in Claude.ai and ChatGPT

Download Skill