intelligent-cache

Implement a multi-layer caching system with type-specific TTLs and get-or-generate semantics.

Updated Jan 23, 2026

Search Tags:#performance #caching #optimization #data management #reliability #get-or-generate #stampede prevention

Ready to run

npx skills add https://github.com/TDMorales/skill_set_library --skill intelligent-cache-tdmorales

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: intelligent-cache
Source: https://github.com/TDMorales/skill_set_library/tree/main/skills/.experimental/intelligent-cache
Command: npx skills add https://github.com/TDMorales/skill_set_library --skill intelligent-cache-tdmorales

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and examples (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently managing cached data by implementing a robust, multi-layer caching system that optimizes performance and ensures data freshness.

Core Features & Use Cases

Type-Specific TTLs: Define unique expiration times for different types of cached data.
Get-or-Generate Semantics: Ensures data is either retrieved from cache or generated once and then cached, preventing redundant computations.
Stampede Prevention: Protects against cache stampedes by ensuring concurrent requests for the same data share a single generation process.
Observability: Provides signals to track cache hits, misses, and generation times for performance monitoring.
Use Case: In an e-commerce application, cache product details with a short TTL (e.g., 5 minutes) and category listings with a longer TTL (e.g., 1 hour) to balance real-time accuracy with reduced database load.

Quick Start

Implement the intelligent cache skill with type-specific TTLs and get-or-generate semantics.

Frequently Asked Questions about intelligent-cache

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

FAQPage Schema

How do I prevent cache stampedes when concurrent requests try to generate the same data?▼

Cache stampede prevention ensures concurrent requests for the same data share a single generation process. This avoids redundant computations and database load by forcing parallel requests to wait for one initial generation to complete before retrieving the cached result.

How do I set different expiration times for different types of cached data?▼

Type-specific Time-To-Live (TTL) policies allow you to define unique expiration times for different data types. You can cache product details with a short TTL and category listings with a longer TTL to balance real-time accuracy with reduced database load.

What is the get-or-generate pattern for data caching?▼

The get-or-generate caching pattern ensures data is either retrieved from an existing cache or generated once and then stored. This single entry point prevents redundant computations by guaranteeing missing data is calculated only once before being saved.

Can I track cache hits and misses to monitor performance?▼

Observability signals allow you to track cache hits, misses, and generation times for performance monitoring. These signals provide the necessary metrics to evaluate retrieval efficiency and identify when data generation is impacting application performance.

Does this caching approach support graceful degradation when persistence fails?▼

The multi-layer caching system supports graceful degradation and optional persistence. If a persistent storage layer fails, it relies on memory-first retrieval to maintain operational continuity while preserving cached data availability.

What's the best way to implement a production-safe multi-layer cache?▼

A production-safe multi-layer cache combines memory-first retrieval, optional persistence, and stampede prevention. This approach ensures high availability and performance by prioritizing fast memory access while maintaining data freshness through type-specific TTLs.