skills.rest
qiuhaomem
qiuhaomemOFFICIAL
Explore all skills by this author →
GitHub

prompt-cache-strategy

Maximize prefix cache hits by enforcing stable prompts and batch requests.

1|Updated May 18, 2026
Search Tags:#llm#cost-saving#prompt-engineering#api-optimization#prefix-caching#cache-warming#batch-requests
One-click install
npx skills add https://github.com/qiuhaomem/HawkEye-Mem --skill prompt-cache-strategy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-cache-strategy
Source: https://github.com/qiuhaomem/HawkEye-Mem/tree/main/docs/skills/prompt-cache-strategy
Command: npx skills add https://github.com/qiuhaomem/HawkEye-Mem --skill prompt-cache-strategy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prefix caching dramatically reduces token spend by reusing computation for identical prefixes across requests. This guide offers a structured approach to implement and tune such caching for models that support prefix caching.

Core Features & Use Cases

  • Iron Laws: Enforce a fixed system prompt, stable message order, and consistent tool definitions to maximize cache hits.
  • Operational Guidance: Warm-up strategies, batch request merging, and cache alignment to improve efficiency.
  • Use Case: Long-running chat agents and tool orchestration that repeatedly reuse the same prompt prefixes.

Quick Start

Run a simple warm-up prompt to establish the cache, then start executing batched requests with a fixed system prompt and stable tool order.

Frequently Asked Questions about prompt-cache-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce LLM API costs with prefix caching?▼

Prefix caching reduces LLM API costs by reusing computation for identical prefixes across requests. You achieve this by enforcing a fixed system prompt, stable message order, and consistent tool definitions to maximize cache hits.

What is the best way to warm up a prompt cache for batch API requests?▼

The best way to warm up a prompt cache is to run a simple warm-up prompt to establish the cache before executing batched requests. This ensures the prefix is cached and ready for subsequent repeated model calls.

Does prefix caching work for long-running chat agents and tool orchestration?▼

Yes, prefix caching works effectively for long-running chat agents and tool orchestration. These use cases repeatedly reuse the same prompt prefixes, making them ideal for maximizing cache hits and reducing token spend.

Why are my LLM prefix cache hits not working for repeated model calls?▼

Prefix cache hits fail when the stable prefix is broken. To fix this, ensure you enforce a fixed system prompt, maintain a consistent conversation structure, and keep tool definitions in a stable message order across all requests.

How do I merge batch requests to improve API optimization and cache alignment?▼

You improve API optimization and cache alignment by merging batch requests under a fixed system prompt. This operational guidance ensures identical prefixes are reused across calls, maximizing cache efficiency and lowering token spend.

Related Skills

Explore More Software Engineering Skills→
saranskumarsaranskumar

api-contract-designer

Define API contracts specifying endpoints, payloads, error handling, and pagination.

Community
Advanced
rocklambrosrocklambros
2

profiling-llm-cost

Profile LLM application spend from trace logs with token counts and model pricing.

Community
Advanced
linkerdoglinkerdog
15

context-cache-management

Manage RARA context compression while preserving prompt prefixes and tool evidence.

Official
Advanced
NotysotyNotysoty
9

LLM Cost Optimizer

Audit LLM prompts and call patterns to identify token waste and cost-saving opportunities.

Community
Advanced