Prompt caching
Skill AravindS-Wick/aravindhan-skills/skills/library/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.From its SKILL.md
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SKILL.md
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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:
- 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
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most prompt engineering skills give in 346 tokens
Counted across 542 of the 575 authors here whose files we hold, read 2026-09-06
- Provide few-shot examples for complex tasksin 17 of 542, across 16 files
- Ask clarifying questions if information is ambiguousin 16 of 542, across 14 files
- Output a complete optimized prompt for the userin 15 of 542, across 9 files
- Validate structured outputs against schemasin 15 of 542, across 13 files
- Analyze the draft prompt for intent and gapsin 14 of 542, across 8 files
- Detect project tech stack from local filesin 14 of 542, across 8 files
- Recommend a model based on task scopein 13 of 542, across 7 files
- Present results in the specified output formatin 13 of 542, across 7 files
- Match intent and scope to ECC componentsin 13 of 542, across 7 files
- Ask one question at a timein 13 of 542, across 12 files
- Respond in the same language as the user inputin 12 of 542, across 6 files
- Ask up to three clarification questions if context is missingin 11 of 542, across 5 files
Said here and by no other author read
- Cache at prefix or response level
- Use native prompt caching for repeated prefixes
- Cache full responses for similar queries
- Pre-cache documents instead of using RAG
- Optimize for cache misses
- Structure prompts for optimal caching
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.