Does this skill support asynchronous Python?

Yes, the skill includes specific code examples and best practices for both the standard redis-py library and the aioredis client for asynchronous operations.

Can I use this for rate limiting?

Absolutely. The skill provides a detailed sliding window rate limiter implementation utilizing Redis pipelines and expiration logic.

Which Redis data structures are covered?

The skill provides guidance and CLI commands for Strings, Hashes, Lists, Sets, and Sorted Sets (ZSets).

How does the distributed lock implementation work?

It uses a Lua script pattern to ensure atomic operations, preventing race conditions by verifying ownership of the lock before releasing it.

Redis Data & Caching

Name: Redis Data & Caching
Author: Lobbi-Docs

byLobbi-Docs

Gestión de Bases de Datos

Optimizes application performance through Redis caching, real-time pub/sub messaging, and distributed data structures.

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The Redis skill equips Claude with the specialized knowledge required to implement high-performance data strategies within the Golden Armada AI Agent Fleet Platform. It provides a comprehensive framework for managing low-latency operations, ranging from simple key-value caching and session management to complex distributed locks and atomic rate limiting. By integrating Redis CLI references and production-ready Python patterns for both synchronous and asynchronous workflows, this skill enables developers to build scalable, responsive systems that handle high-concurrency tasks with ease.

Características Principales

Real-time event handling through Pub/Sub implementation patterns
Production-ready Python snippets for sync and async Redis clients
Scalable rate limiting logic using Redis pipelines
Pre-built patterns for Cache-Aside and Distributed Locking
0 GitHub stars
Comprehensive Redis CLI quick reference for connection and data operations

Casos de Uso

Managing secure, short-lived user sessions for web applications
Implementing high-performance caching layers to reduce database load
Enforcing distributed rate limits across multiple microservices

Acerca de

Características Principales

Real-time event handling through Pub/Sub implementation patterns
Production-ready Python snippets for sync and async Redis clients
Scalable rate limiting logic using Redis pipelines
Pre-built patterns for Cache-Aside and Distributed Locking
0 GitHub stars
Comprehensive Redis CLI quick reference for connection and data operations

Casos de Uso

Managing secure, short-lived user sessions for web applications
Implementing high-performance caching layers to reduce database load
Enforcing distributed rate limits across multiple microservices