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go-samber-hot

Configure samber/hot in-memory caching with eviction policies and TTL management for Go applications.

Updated May 2, 2026
Search Tags:#caching#backend#go#in-memory#ttl#eviction#samber
One-click install
npx skills add https://github.com/Qunnnn/agents --skill go-samber-hot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: go-samber-hot
Source: https://github.com/Qunnnn/agents/tree/main/skills/go/go-samber-hot
Command: npx skills add https://github.com/Qunnnn/agents --skill go-samber-hot

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps Go developers implement efficient in-memory caching strategies without manually building eviction, expiration, loading, and monitoring mechanisms.

Core Features & Use Cases

  • Cache Algorithms: Provides guidance for selecting and configuring LRU, LFU, W-TinyLFU, S3FIFO, and other eviction strategies for different workloads.
  • Production Caching Patterns: Covers TTL expiration, janitor cleanup, loaders, singleflight-style deduplication, sharding, and metrics integration.
  • Use Case: Apply this Skill when building a Go service that needs a high-performance cache for user sessions, database results, API responses, or frequently accessed application data.

Quick Start

Ask the AI to configure a samber/hot cache with an appropriate eviction policy, TTL, and loader pattern for your Go application.

Frequently Asked Questions about go-samber-hot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement in-memory caching in Go with TTL and eviction policies?▼

Implement in-memory caching in Go by configuring eviction policies like LRU or LFU alongside TTL expiration and janitor cleanup. This handles cache lifecycle management and ensures fast data access for backend services.

What is the best way to select a cache eviction algorithm for a Go backend service?▼

Choose a cache eviction algorithm based on your workload by selecting from LRU, LFU, W-TinyLFU, or S3FIFO strategies. This optimizes cache hit rates and manages memory effectively across different access patterns in Go.

How does singleflight deduplication work for cache loading in Go applications?▼

Singleflight deduplication for cache loading prevents duplicate data fetches by collapsing concurrent requests for the same key into a single underlying load operation, reducing database or API pressure while improving Go application performance.

Can I use generic type-safe caching with production monitoring in Go backend services?▼

Yes, generic type-safe caching supports Go backend services with integrated production monitoring. This approach includes metrics integration and sharding to handle high-concurrency access to frequently accessed application data.

When do I need an in-memory cache with janitor cleanup for my Go application?▼

You need an in-memory cache with janitor cleanup when your Go application requires automated TTL expiration control, preventing stale data accumulation and optimizing memory usage for user sessions, database results, or API responses.

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