context-optimization

Optimize LLM context windows via compaction, masking, and partitioning.

Updated Jun 17, 2025

Search Tags:#prompt engineering #llm #context optimization #kv-cache #agent systems #token cost reduction

Ready to run

npx skills add https://github.com/jax2730/workcode --skill context-optimization-jax2730

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-optimization
Source: https://github.com/jax2730/workcode/tree/main/LLM%26Dialog/Agent-Skills-for-Context-Engineering-main/skills/context-optimization
Command: npx skills add https://github.com/jax2730/workcode --skill context-optimization-jax2730

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the limitations of fixed context windows in LLMs by employing advanced techniques to optimize how information is stored and accessed, thereby reducing token costs and improving performance.

Core Features & Use Cases

Context Compaction: Summarizes older conversation turns and tool outputs to free up space.
Observation Masking: Replaces verbose tool outputs with compact references, saving significant tokens.
KV-Cache Optimization: Designs prompts and context ordering to maximize the reuse of cached computations, reducing latency and cost.
Context Partitioning: Divides complex tasks into sub-agents with isolated contexts for better management.
Use Case: When dealing with long-running agents or large documents, this skill can prevent context windows from filling up, ensuring continued performance and cost-effectiveness.

Quick Start

Optimize the current context by summarizing older messages and eliding verbose tool outputs.

Frequently Asked Questions about context-optimization

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

FAQPage Schema

How do I reduce token costs when an LLM agent exceeds the context window limit?▼

To reduce token costs and resolve context window limits, you can apply context compaction to summarize older conversation turns and use observation masking to replace verbose tool outputs with compact references.

What is KV-cache optimization and how does it lower LLM latency?▼

KV-cache optimization lowers LLM latency by structuring prompt ordering to maximize the reuse of cached computations, which prevents redundant processing and reduces overall token computation costs during long conversations.

How do I manage context for long-running agent systems handling large documents?▼

You can manage context for long-running agent systems by partitioning complex tasks into sub-agents with isolated contexts, and by summarizing retrieved documents and conversation history to prevent the context window from filling up.

What is the best way to elide verbose tool outputs in LLM conversation history?▼

The best way to elide verbose tool outputs is through observation masking, a technique that replaces large tool execution results with compact references to free up context space and save significant tokens.

Does context partitioning work for isolating sub-agent environments in complex tasks?▼

Yes, context partitioning works by dividing complex tasks into sub-agents with isolated contexts, enabling better management of system resources and ensuring continued performance without filling up the main context window.