Individual skill
prompt-caching-strategy
Optimize LLM prompt caching hit rate to reduce API costs and improve latency. Analyzes prompt structure, identifies cacheable vs non-cacheable content, and recommends restructuring to maximize cache reuse. TRIGGER when: user says "prompt cache", "cache hit rate", "reduce LLM cost", "optimize token usage", "cheaper LLM calls", or asks about prompt caching strategies for OpenAI/Anthropic/other providers. DO NOT TRIGGER when: user just wants to optimize a single prompt's quality (use prompt-optimizer instead), or asks about general caching infrastructure.
skills/prompt-caching-strategy/SKILL.md
Source commit b38be6a7f6350964d59328b52f0624e1d1d552d1
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- Last checked
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- ai-agents Repository topic
- anthropic Repository topic
- claude Repository topic
- claude-code Repository topic
- developer-tools Repository topic
- llm Repository topic
- mcp Repository topic
- productivity Repository topic
- writing Derived category
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SKILL.mdcore · 10,109 bytes
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# Prompt Caching Strategy
Arrange reusable prompt content to improve cache reuse, then measure input cost
and latency. Savings depend on model, platform, cache eligibility, writes,
retention, reuse and output volume. Runtime cache hits and savings for this skill
are **unmeasured**; the examples below validate arithmetic without provider calls.
## When to Activate
Use for repeated API requests that share authorized instructions, tools or
reference material. First identify the exact provider endpoint, model, service
tier and caching mode. Consult its current documentation before selecting rates,
minimum prefix length or retention. Caching does not reduce context-window use.
