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 augm...
Best use case
prompt-caching is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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 augm...
Teams using prompt-caching should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
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 augm...
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` ## When to Use This skill is applicable to execute the workflow or actions described in the overview.
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