How does it use Cellular Sheaves?

The skill converts network stacks into cellular sheaves to perform diffusion analysis via Laplacians, allowing for a high-fidelity trace of how ideas move between nodes.

What database does this skill use for network storage?

It utilizes DuckDB to manage network nodes, influence edges, and idea adoption data with high-performance SQL queries.

What is a 'reach multiplier' in this context?

A reach multiplier is an amplification factor calculated by comparing the size of a user's second-order network to their direct connections.

Does this skill work with other plugins?

Yes, it is part of a triad that includes bisimulation-game for equivalence checking and pulse-mcp-stream for live data sourcing.

What is the primary purpose of the Influence Propagation skill?

It is designed to analyze how information and influence flow through networks, providing metrics like reach multipliers and adoption rates using topological mathematics.

Network Influence & Propagation

Name: Network Influence & Propagation
Author: plurigrid

byplurigrid

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Data Science & ML

Analyzes social network dynamics and traces the diffusion of ideas using advanced topological math and cellular sheaf structures.

Influence Propagation is a specialized Layer 7 skill for Claude Code designed for sophisticated network analysis and social graph modeling. It leverages advanced mathematical concepts like condensed stacks and cellular sheaves to trace how ideas, topics, and behaviors spread through complex interperspectival networks. By integrating Ruby-based diffusion logic with DuckDB for data persistence and Hy for perspective mapping, it allows users to measure reach multipliers, idea adoption rates, and find key network amplifiers. This skill is essential for developers and data scientists building systems to understand information virality, community influence, and structural connectivity.

Key Features

01Provides a structured DuckDB schema for high-performance network edge tracking

02Calculates second-order reach multipliers to measure amplification factors

037 GitHub stars

04Integrates with the GF(3) Triad to validate network flow patterns

05Traces idea adoption timelines using cellular sheaf diffusion and Laplacians

06Maps concept hubs and influence flow using interperspectival Hy analysis

Use Cases

01Analyzing the structural importance of 'bridge' nodes using betweenness metrics

02Identifying key influencers and amplifiers within decentralized social networks

03Tracking the adoption rate and persistence of specific concepts or behaviors over time

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi influence-propagation

For use in Claude.ai and ChatGPT

Download Skill