prompt-caching
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Best use case
prompt-caching is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "prompt-caching" skill to help with this workflow task. Context: Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-caching/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-caching Compares
| Feature / Agent | prompt-caching | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Prompt Caching 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: 1. Cache at the right level—prefix, response, or both 2. 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`
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