cache-optimizer

Optimize prompt-cache hits and strip redundant context from AI harness configurations.

Updated May 11, 2026

Search Tags:#performance #caching #cost-optimization #prompt-engineering #harness #token-reduction

Ready to run

npx skills add https://github.com/AesopScott/mojo --skill cache-optimizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cache-optimizer
Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/cache-optimizer
Command: npx skills add https://github.com/AesopScott/mojo --skill cache-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses excessive AI operational costs and token waste by optimizing prompt-cache utilization and removing redundant context from instruction payloads.

Core Features & Use Cases

Cache Hit Optimization: Refines templates and injection order to ensure maximum reuse of cached prompt segments.
Redundant Context Removal: Strips unnecessary data from the instruction payload to reduce input token consumption.
Use Case: When managing a high-volume Prompt harness, use this Skill to audit and prune redundant system instructions, significantly lowering the cost per API call.

Quick Start

Run the cache-optimizer skill to analyze the current Prompt harness and identify redundant context that can be removed to improve cache efficiency.

Frequently Asked Questions about cache-optimizer

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

FAQPage Schema

How do I reduce input token consumption in my AI prompt harness?▼

Reducing input token consumption requires maximizing prompt-cache hits and stripping redundant context from system instruction payloads. Auditing injection sequences and removing unnecessary data lowers the token count per API call.

What is prompt-cache optimization and how does it lower AI operational costs?▼

Prompt-cache optimization refines prompt templates and injection order to ensure maximum reuse of cached prompt segments. Systematically inspecting harness configurations and removing redundant context directly lowers the cost per API call.

How do I audit a prompt harness for redundant system instructions?▼

Auditing a prompt harness for redundant system instructions involves systematically inspecting current configurations and verifying token usage metrics. This process identifies and prunes unnecessary data in injection sequences to maximize prompt-cache hits.

Does cache-optimizer work with high-volume prompt harnesses?▼

Yes, cache-optimizer is designed for high-volume prompt harnesses. It audits and prunes redundant system instructions from the instruction payload, significantly lowering the input token consumption and cost per API call.

What is the best way to maximize prompt-cache hits for system instruction payloads?▼

The best way to maximize prompt-cache hits is to refine templates and adjust the injection sequence order to ensure maximum reuse of cached prompt segments. This requires systematic inspection of the current harness configurations.

When should I not use prompt-cache optimization on my injection sequences?▼

Prompt-cache optimization is not ideal when injection sequences contain highly dynamic, non-repeating context that cannot be cached. The process relies on identifying and stripping redundant data from stable templates to reduce input token consumption.