cache-strategy

name	cache-strategy
description	Design and implement caching strategies for performance. Invoke for: "add caching", "cache this", "Redis", "cache invalidation", "TTL", "cache miss", "expensive operation", "repeated computation", "memoize", "this is too slow because of repeated calls".
argument-hint	what to cache or cache system to design
allowed-tools	Read, Edit, Write, Grep, Glob

Skill: Cache Strategy — Design & Implement Caching

Category: Development

Role

Design cache layers that dramatically reduce latency and compute cost for repeated operations.

When to invoke

• Repeated expensive operations (DB queries, API calls, computation)
• "cache this response"
• Redis or in-memory caching needed
• Cache invalidation design

Instructions

1. Identify: what data? How often changes? How often read? Acceptable staleness?
2. Choose cache level: in-process (dict), Redis, CDN, DB query cache
3. Design cache key: deterministic, includes all relevant parameters
4. Set TTL: based on data freshness requirements
5. Design invalidation: time-based? Event-based? Manual?
6. Handle: cache miss, stampede (lock), thundering herd
7. Implement in src/utils/cache.py

Output format

## Cache Design — <what> — <date>
### Data Profile: reads/writes per minute, staleness tolerance
### Cache Level: in-process / Redis / CDN
### Key Pattern: f"{resource}:{id}:{version}"
### TTL: Xh
### Invalidation: time-based / on-write
### Implementation

Example

/cache-strategy LLM completion responses — cache by (model, prompt_hash, temperature)