Can it handle very large datasets?

By employing sublinear algorithms and sparse matrix representations, this skill is optimized for massive graph computations that standard iterative methods may find prohibitive.

How does it help with distributed system design?

It analyzes and optimizes communication topologies to identify bottlenecks and improve the fault tolerance and load distribution of distributed architectures.

Does it support personalized PageRank for recommendations?

Yes, the skill can calculate personalized PageRank scores by using preference vectors, making it an ideal tool for building tailored recommendation systems.

What algorithms does this skill use for graph analysis?

The skill utilizes advanced sublinear-time solvers and PageRank algorithms specifically designed to process large-scale matrices and graph data with high computational efficiency.

Can I use it for social network analysis?

Yes, it is highly effective for identifying influential nodes, detecting community clusters, and modeling influence propagation within social graphs.

Agent PageRank Analyzer

Name: Agent PageRank Analyzer
Author: nbossn

bynbossn

0•

Data Science & ML

Analyzes complex networks and calculates PageRank scores using sublinear algorithms to optimize graph structures and influence mapping.

The Agent PageRank Analyzer is a specialized expert designed for high-performance graph analysis and network optimization within the Claude Code environment. By leveraging advanced sublinear-time solvers, it enables developers to compute PageRank scores at scale, identify community structures, and optimize communication topologies for agent swarms or distributed systems. This skill is particularly useful for building recommendation engines, analyzing social influence propagation, and designing resilient network architectures where efficiency and computational precision are paramount.

Key Features

01Personalized PageRank support for custom recommendation systems

02Network topology optimization for agent swarms and distributed systems

03Matrix analysis for network resilience and load distribution

040 GitHub stars

05Advanced community detection and influence propagation analysis

06Large-scale PageRank computation using sublinear-time algorithms

Use Cases

01Optimizing communication efficiency and identifying bottlenecks in distributed system topologies

02Developing personalized recommendation engines based on user-item interaction graphs

03Identifying key influencers and viral marketing opportunities in social network datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agent-pagerank-analyzer

For use in Claude.ai and ChatGPT

Key Features

01Personalized PageRank support for custom recommendation systems

02Network topology optimization for agent swarms and distributed systems

03Matrix analysis for network resilience and load distribution

040 GitHub stars

05Advanced community detection and influence propagation analysis

06Large-scale PageRank computation using sublinear-time algorithms

Use Cases

01Optimizing communication efficiency and identifying bottlenecks in distributed system topologies

02Developing personalized recommendation engines based on user-item interaction graphs

03Identifying key influencers and viral marketing opportunities in social network datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agent-pagerank-analyzer

For use in Claude.ai and ChatGPT