Prompt Caching

This covers three caching layers for LLM applications: Anthropic's native prompt caching for stable prefixes (90% token cost reduction), response caching with Redis for repeated queries, and Cache Augmented Generation where you pre-load entire document sets into cached prompts instead of doing RAG retrieval. The CAG approach is smart when your corpus is under 100K tokens and rarely changes, since you skip the retrieval step entirely. The sharp edges section is honest about when caching hurts more than helps, like when your cache hit rate is below 50% and the overhead of cache checks actually slows things down. Includes a decision matrix comparing CAG versus RAG based on corpus size and update frequency.

skills.sh installs: 760
GitHub stars: 43.1K
last updated: Jul 14, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill prompt-caching --agent claude-code

Installs into .claude/skills of the current project.

Files

Prompt Caching

Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)

Capabilities

prompt-cache
response-cache
kv-cache
cag-patterns
cache-invalidation

Prerequisites

Knowledge: Caching fundamentals, LLM API usage, Hash functions
Skills_recommended: context-window-management

Scope

Does_not_cover: CDN caching, Database query caching, Static asset caching
Boundaries: Focus is LLM-specific caching, Covers prompt and response caching

Ecosystem

Primary_tools

Anthropic Prompt Caching - Native prompt caching in Claude API
Redis - In-memory cache for responses (ioredis on servers; @upstash/redis over HTTP on serverless, see upstash-redis)
OpenAI Caching - Automatic caching in OpenAI API