What is the Distributed Systems discovery skill?

It is a gateway skill for Claude Code that automatically identifies when you are working on distributed architectures and provides access to 17 specialized sub-skills.

How does progressive loading work with this skill?

Instead of loading all data at once, this skill activates a gateway that allows Claude to 'cat' specific skill files only when they become relevant to your current coding task.

Which consensus algorithms are supported?

The skill provides detailed implementation and theoretical guidance for both RAFT and Paxos (including Basic and Multi-Paxos) consensus protocols.

Can this skill help with database sharding?

Yes, it includes specific modules for partitioning, sharding, consistent hashing, and rebalancing strategies for distributed data stores.

Distributed Systems Architect

Name: Distributed Systems Architect
Author: rand

byrand

•

Cloud Infrastructure

Provides specialized architectural patterns and implementation guidance for consensus, replication, and distributed algorithms.

About

This skill acts as an intelligent gateway for building high-availability and resilient distributed architectures within Claude Code. It automatically activates when detecting work related to the CAP theorem, consensus protocols like RAFT and Paxos, or eventual consistency models like CRDTs. By enabling progressive loading of 17 domain-specific skills, it provides Claude with the exact theoretical foundations and implementation patterns needed for complex tasks—ranging from causality tracking with vector clocks to data sharding and gossip protocols—without overloading the initial context window.

Key Features

Implementation guides for CRDTs and conflict resolution strategies
Comprehensive patterns for RAFT and Paxos consensus algorithms
Automatic discovery of 17 specialized distributed systems skills
Optimization strategies for partitioning, sharding, and replication
57 GitHub stars
Advanced causality tracking using vector and interval tree clocks

Use Cases

Designing conflict-free replicated data types (CRDTs) for collaborative real-time applications
Architecting scalable data layers using consistent hashing and gossip-based failure detection
Implementing leader election and log replication for distributed databases or services

About

Key Features

Implementation guides for CRDTs and conflict resolution strategies
Comprehensive patterns for RAFT and Paxos consensus algorithms
Automatic discovery of 17 specialized distributed systems skills
Optimization strategies for partitioning, sharding, and replication
57 GitHub stars
Advanced causality tracking using vector and interval tree clocks

Use Cases

Designing conflict-free replicated data types (CRDTs) for collaborative real-time applications
Architecting scalable data layers using consistent hashing and gossip-based failure detection
Implementing leader election and log replication for distributed databases or services