cache-strategy

Enforce TTL-based caching with cache-aside, write-through, and stampede protection.

Updated Mar 20, 2026
One-click install
npx skills add https://github.com/hendrax5/ironman --skill cache-strategy-hendrax5
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cache-strategy
Source: https://github.com/hendrax5/ironman/tree/main/skills/cache
Command: npx skills add https://github.com/hendrax5/ironman --skill cache-strategy-hendrax5

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Standardizes caching practices by enforcing TTLs and consistent invalidation to reduce stale data and lighten load on data stores.

Core Features & Use Cases

  • TTL-driven caching: apply per-type TTLs to balance data freshness and performance.
  • Cache patterns: supports Cache-Aside, Write-Through, and Stampede Protection strategies.
  • Invalidation rules: explicit delete and prefix-based invalidation to maintain data consistency.
  • Hard rules: ensure no eternal caches and no PII in cache.

Quick Start

Install the cache-strategy module and wire it into your data access layer to enforce TTL-based caching.

Frequently Asked Questions about cache-strategy

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

FAQPage Schema
How do I implement TTL-based caching in Python to prevent stale data?

TTL-based caching prevents stale data by enforcing per-type expiration limits on cached items. This approach applies explicit time-to-live rules to balance data freshness and performance for read-heavy backend workloads.

What is the best way to protect against cache stampedes in a read-heavy application?

Protecting against cache stampedes requires implementing stampede protection patterns alongside standard cache-aside strategies. This prevents simultaneous database overloads when expired cache entries trigger mass concurrent recomputation requests.

How do I set up explicit cache invalidation rules for sessions and master data?

Explicit cache invalidation for sessions and master data uses prefix-based deletion rules to maintain consistency. This ensures targeted cache clearing without risking widespread data unavailability or serving outdated information.

Can I use write-through caching with Redis for frequently changing counters?

Write-through caching with Redis supports frequently changing counters by synchronously writing data to both the cache and data store. This pattern ensures consistency while reducing read latency for backend data-access layers.

What are the limitations of using TTL caching for backend services?

TTL caching limitations include strict rules prohibiting eternal caches and storing PII in cache layers. These constraints prevent unbounded memory growth and security risks while maintaining data freshness.