Individual skill

caching-strategy

Designs multi-layer caching - CDN, application, and database - choosing the pattern, TTL, and invalidation plan per data type, and produces a cache-decision table plus stampede protection. Use when someone asks "should I cache this", "Redis or CDN for this endpoint", "how do I invalidate this cache", "why is my cache hit rate so low", or is bolting Redis onto a slow service. Do NOT use for sizing or tuning database connection pools - use connection-pool-tuner instead - and do NOT use for frontend bundle, image, or Core Web Vitals work - use web-performance instead.

skills/caching-strategy/SKILL.md

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Repository last updated: Oct 13, 2026
License: MIT

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claude Repository topic
claude-skills Repository topic
skill-me Repository topic
data Derived category
data-databases Derived category

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# Caching Strategy

A cache is a correctness liability you accept in exchange for latency, so every cache needs an explicit answer to "how stale can this be?" and "how does it get invalidated?" before it ships. The costly mistake this skill prevents is the write-only cache: a layer that adds a network hop and an invalidation bug surface while its hit rate quietly sits below 50 percent, making the system slower and wronger at the same time.

## Operating procedure

Work per data type, not per system - "cache the API" is not a decision, "cache the product listing for 60s at the CDN" is.

### Step 1: Gather inputs

Collect these for each candidate data type. If a number is an estimate, label it a guess and refine from production metrics later.

1. Read:write ratio. How many reads per write to the same key.
2. Recompute cost. Latency of a cache miss (DB query, computation, upstream call).