How do I generate a causality graph from my code?

You can use the causality_graph.py tool included in the skill to analyze your source files and generate a structured graph of logical flow.

Can L-Space be used for creative writing analysis?

Yes, while powerful for code, it is specifically designed to handle prose through its narrative_extract.bb tool, which maps story arcs and causal consistency.

What is 'token novelty' in this context?

Token novelty is a metric that measures the 'surprise' or unique information density of text, helping you identify which parts of a document contain the most original content.

What technologies does the L-Space skill use?

The skill utilizes a mix of Python for data processing, Babashka (Clojure) for extraction, and mathematical frameworks like sheaf-cohomology for consistency checking.

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

L-Space extracts and analyzes the narrative structure, causal links, and informational density of text or code, treating information as a non-Euclidean topological space.

L-Space Narrative Analysis

Name: L-Space Narrative Analysis
Author: plurigrid

byplurigrid

Data Science & ML

Extracts and analyzes narrative arcs, causality, and token novelty from complex text and codebases.

About

L-Space is a sophisticated analysis skill designed for deep informational mapping, drawing inspiration from informational physics and narrative theory. It allows developers and researchers to treat text and code as a topological space, extracting structural 'narratives' and causal relationships that are often hidden in large datasets. By measuring token novelty and manifold positioning, L-Space identifies high-density information zones and traces the logical flow of systems, making it an essential tool for understanding the coherence of massive documentation libraries or complex software architectures.

Key Features

Extracts narrative arcs and causality graphs from prose or code
Generates Graphviz-ready causal maps for visual logic tracing
Implements sheaf-theoretical approaches for local-to-global consistency
Analyzes informational density and critical mass in text collections
Measures token novelty and 'surprise' curves for manifold positioning
0 GitHub stars

Use Cases

Analyzing the structural coherence and narrative flow of technical documentation
Tracing logical causality and dependency arcs in large-scale software repositories
Measuring the informational novelty of text segments to identify unique or high-value content

About

Key Features

Extracts narrative arcs and causality graphs from prose or code
Generates Graphviz-ready causal maps for visual logic tracing
Implements sheaf-theoretical approaches for local-to-global consistency
Analyzes informational density and critical mass in text collections
Measures token novelty and 'surprise' curves for manifold positioning
0 GitHub stars

Use Cases

Analyzing the structural coherence and narrative flow of technical documentation
Tracing logical causality and dependency arcs in large-scale software repositories
Measuring the informational novelty of text segments to identify unique or high-value content