What is the primary purpose of the Pun Decomposition skill?

It enables Claude to identify and formalize the multiple meanings hidden within phonetic strings (puns) using topological and algebraic frameworks like ACSet schemas.

How does this skill use GF(3) triads?

It treats linguistic elements as trits (-1, 0, +1) to ensure compositional coherence and balanced semantic resonance across related analytical skills.

Does this skill support programmatic disambiguation?

Yes, it includes logic for 2-Poisson disambiguation to handle instances where multiple valid parses conflict during comprehension tasks.

Is this skill only for humor analysis?

No, while it handles puns, it is a robust tool for any linguistic task involving semantic ambiguity, etymological roots, or complex morphemic structures.

What are 'Gestalt attacks' in this context?

They refer to exploiting perceptual grouping principles like Closure and FigureGround to shift an interpretation from a dominant meaning to a secondary one.

Pun Decomposition Logic

Name: Pun Decomposition Logic
Author: plurigrid

byplurigrid

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Ciencia de Datos y ML

Analyzes and deconstructs linguistic puns into multiple semantic and phonetic parses using algebraic and topological structures.

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This skill provides Claude with advanced capabilities to identify and analyze puns by decomposing surface-level phonetic strings into multiple valid semantic interpretations. Built on a foundation of category theory, braided monoidal structures, and GF(3) triads, it allows for the formal modeling of context switching, gestalt perceptual grouping, and etymological resonance. It is an essential tool for developers and researchers working at the intersection of information theory, cognitive modeling, and deep linguistic analysis where semantic ambiguity must be structurally managed.

Características Principales

2 GitHub stars
Multi-parse validation for phonetic, morphemic, and etymological surfaces
Integration with ACSet schemas for structured decomposition and storage
Stochastic disambiguation using 2-Poisson sampling for comprehension defense
Algebraic modeling of semantic context switching using GF(3) triads
Gestalt principle exploitation for analyzing perceptual grouping and linguistic shifts

Casos de Uso

Modeling cognitive load and inductive bias in natural language processing
Developing AI systems capable of formalizing wordplay and semantic ambiguity
Advanced linguistic research and morphemic decomposition workflows

Acerca de

Características Principales

2 GitHub stars
Multi-parse validation for phonetic, morphemic, and etymological surfaces
Integration with ACSet schemas for structured decomposition and storage
Stochastic disambiguation using 2-Poisson sampling for comprehension defense
Algebraic modeling of semantic context switching using GF(3) triads
Gestalt principle exploitation for analyzing perceptual grouping and linguistic shifts

Casos de Uso

Modeling cognitive load and inductive bias in natural language processing
Developing AI systems capable of formalizing wordplay and semantic ambiguity
Advanced linguistic research and morphemic decomposition workflows