CF Plugin Hyperbolic Reasoning

Run CLI commands for hyperbolic taxonomic queries and entailment checks.

1|Updated Feb 8, 2026

Search Tags:#knowledge graph #embeddings #taxonomy #hierarchical data #hyperbolic reasoning #entailment

Ready to run

npx skills add https://github.com/ricable/cli-skills-builder --skill cf-plugin-hyperbolic-reasoning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: CF Plugin Hyperbolic Reasoning
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/claude-flow-plugin-hyperbolic-reasoning
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill cf-plugin-hyperbolic-reasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles the challenge of understanding and querying complex hierarchical data, which is often inefficiently handled by traditional methods.

Core Features & Use Cases

Poincare Ball Embeddings: Represents hierarchical data in hyperbolic space for efficient processing.
Taxonomic Reasoning: Performs "is-a" queries and finds least common ancestors in taxonomies.
Hierarchical Search: Enables fast searching within tree-structured data.
Entailment Graphs: Manages and queries concept entailment relationships.
Use Case: Analyze a product taxonomy to find the most specific common category for two different products, or to classify a new product into the existing hierarchy.

Quick Start

Enable the hyperbolic reasoning plugin for your CLI.

Frequently Asked Questions about CF Plugin Hyperbolic Reasoning

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

FAQPage Schema

How do I query hierarchical data to find the least common ancestor in a taxonomy?▼

You can query hierarchical data by applying hyperbolic reasoning with Poincare ball embeddings. This method represents tree-structured data in hyperbolic space, allowing you to perform taxonomic classification and find the least common ancestor efficiently.

What is the best way to classify new items into an existing knowledge graph hierarchy?▼

Using hyperbolic geometry and Poincare ball embeddings is an effective way to classify new items into an existing knowledge graph. It facilitates taxonomic classification by mapping hierarchical data into a space that naturally represents tree structures.

How do I analyze entailment graphs for concept relationships in a knowledge graph?▼

You can analyze entailment graphs by using hyperbolic reasoning capabilities designed to manage and query concept entailment relationships. This allows your agent workflows to process hierarchical data and determine logical entailment within the graph.

Does hyperbolic reasoning work better than traditional methods for navigating complex taxonomies?▼

Hyperbolic reasoning works better for navigating complex taxonomies because traditional methods often handle hierarchical data inefficiently. By using Poincare ball embeddings, it processes tree-structured data and hierarchical searches more effectively.

Can I use this approach for hierarchical search within tree-structured product data?▼

Yes, you can use hyperbolic reasoning for hierarchical search within tree-structured product data. It enables fast searching and classification by representing your product taxonomy in hyperbolic space using Poincare ball embeddings.