Prompt Caching
SkillAI & modelsThis is a skill that teaches an AI agent prompt claude skill techniques for caching large language model prompts and responses. It covers Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation), so chats can become faster and cheaper.
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Then ask your AI: use the Prompt Caching skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Have an AI agent that can load skills.
What your AI can do with it
- Explains Anthropic prompt caching strategies
- Covers response caching for model outputs
- Teaches CAG (Cache Augmented Generation)
- Guides when to apply each caching approach
- Helps make chats faster and cheaper through caching
Getting started
- Have an AI agent that can load skills.
- Add the prompt-caching skill to the agent's available skills.
- Ask the agent about prompt caching, response caching, or CAG to apply the skill.
What this skill tells your AI
The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/prompt-caching/SKILL.md and read by ahel’s review.
You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.
You understand that LLM caching is different from traditional caching—prompts have prefixes that can be cached, responses vary with temperature, and semantic similarity often matters more than exact match.
Your core principles:
- Cache at the right level—prefix, response, or both
- K
Capabilities
- prompt-cache
- response-cache
- kv-cache
- cag-patterns
- cache-invalidation
Patterns
Anthropic Prompt Caching
Use Claude's native prompt caching for repeated prefixes
Response Caching
Cache full LLM responses for identical or similar queries
Cache Augmented Generation (CAG)
Pre-cache documents in prompt instead of RAG retrieval
Anti-Patterns
❌ Caching with High Temperature
❌ No Cache Invalidation
❌ Caching Everything
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Cache miss causes latency spike with additional overhead | high | // Optimize for cache misses, not just hits |
| Cached responses become incorrect over time | high | // Implement proper cache invalidation |
| Prompt caching doesn't work due to prefix changes | medium | // Structure prompts for optimal caching |
Related Skills
Works well with: context-window-management, rag-implementation, conversation-memory
Signals
- GitHub stars
- 32k
- Forks
- 4k
- Last commit
- Oct 2026
Questions
- When should this skill be used?
- Use it when working with prompt caching, cache prompts, response caches, CAG, or cache augmented generation.
- What is CAG?
- CAG stands for Cache Augmented Generation, one of the caching strategies the skill covers alongside Anthropic prompt caching and response caching.
- Does caching make chats cheaper?
- The skill teaches caching techniques such as caching model responses that can make chats faster and cheaper.
Advanced
- Item type
- skill
- Key
prompt-caching-davila7- Source
- github.com/davila7/claude-code-templates
github.com/davila7/claude-code-templates
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