Is this skill suitable for offline-first applications?

Yes, it is specifically designed for local-first computing, allowing apps to work offline and sync bidirectional state transfers once a connection is re-established.

Which data types are included in this skill?

It includes Last-Write-Wins (LWW) Registers, Grow-only (G) Counters, Positive-Negative (PN) Counters, Observed-Remove (OR) Sets, and Character-based Text CRDTs.

What are CRDTs used for?

Conflict-free Replicated Data Types are data structures that allow multiple users or systems to update state independently and concurrently, ensuring all replicas eventually converge to the same state without manual conflict resolution.

How does this skill handle data consistency?

The skill implements strong eventual consistency by ensuring all merge operations are idempotent, commutative, and associative, meaning the order and frequency of updates do not affect the final outcome.

CRDT Distributed State Engine

Name: CRDT Distributed State Engine
Author: plurigrid

byplurigrid

Database Management

Implements conflict-free replicated data types and bidirectional lens optics for seamless, distributed state synchronization.

About

The CRDT skill enables developers to build local-first, collaborative applications using Conflict-free Replicated Data Types integrated with compositional game theory. It provides a robust set of data structures—including counters, sets, and text registers—that guarantee eventual consistency through mathematical properties like idempotence and commutativity. By utilizing bidirectional lens optics and Play/Coplay semantics, this skill allows Claude to manage complex distributed states across multiple replicas without manual conflict resolution, making it ideal for real-time collaborative editing, decentralized systems, and offline-capable applications.

Key Features

Play/Coplay semantics for structured state transfer and acknowledgments
Comprehensive CRDT suite including LWW-Register, PN-Counters, and OR-Sets
Real-time performance metrics and merge success rate tracking
Automated mathematical verification for idempotence and commutativity
Bidirectional lens optics for compositional game theory integration
0 GitHub stars

Use Cases

Managing distributed inventory tracking in decentralized environments
Synchronizing agent preferences in distributed machine learning models
Building real-time collaborative text editors and shared workspaces

About

Key Features

Play/Coplay semantics for structured state transfer and acknowledgments
Comprehensive CRDT suite including LWW-Register, PN-Counters, and OR-Sets
Real-time performance metrics and merge success rate tracking
Automated mathematical verification for idempotence and commutativity
Bidirectional lens optics for compositional game theory integration
0 GitHub stars

Use Cases

Managing distributed inventory tracking in decentralized environments
Synchronizing agent preferences in distributed machine learning models
Building real-time collaborative text editors and shared workspaces