context-optimization

Optimizes token usage via KV-cache, masking, partitioning for limited AI contexts.

Updated Mar 13, 2026

Search Tags:#partitioning #compaction #masking #token-budget #context-optimization #kv-cache

Ready to run

npx skills add https://github.com/marinvch/ai-os --skill context-optimization-marinvch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-optimization
Source: https://github.com/marinvch/ai-os/tree/main/.agents/skills/context-engineering-collection/skills/context-optimization
Command: npx skills add https://github.com/marinvch/ai-os --skill context-optimization-marinvch

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Context optimization extends the effective capacity of limited context windows by applying techniques like KV-cache optimization, observation masking, compaction, and context partitioning, reducing token costs while preserving task fidelity.

Core Features & Use Cases

KV-cache optimization to maximize prefix reuse and cut costs
Observation masking to replace verbose tool outputs with compact references
Context compaction to summarize long histories without losing critical state
Context partitioning to split tasks across sub-agents for large problems
Budgeting and monitoring hooks to track token usage and trigger optimizations

Quick Start

Invoke the optimization pass when context usage approaches limits and monitor token savings while preserving essential state.

Frequently Asked Questions about context-optimization

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

FAQPage Schema

How do I optimize context window usage for long-running agent workflows?▼

Reduce token usage in long-running agent workflows by applying observation masking, context compaction, and KV-cache optimization. These techniques preserve task state while cutting token costs within limited context windows.

What is observation masking and how does it reduce token usage?▼

Observation masking reduces token usage by replacing verbose tool outputs with compact references. This technique preserves critical state information while cutting the tokens consumed by long histories.

How do I summarize long context histories without losing task state?▼

Summarize long histories without losing task state by using context compaction. This technique preserves critical state information while significantly cutting the tokens consumed by long histories.

Can I split large documents across sub-agents for scalable reasoning?▼

Yes, you can split large documents across sub-agents using context partitioning. This enables scalable reasoning while maintaining a modular and debuggable design for large problems.

How do I track token usage and trigger context optimizations automatically?▼

Track token usage and trigger context optimizations automatically by using budgeting and monitoring hooks. These hooks monitor consumption and invoke optimization passes when context usage approaches limits.

When should I avoid using context partitioning for token reduction?▼

Avoid context partitioning when tasks require tightly coupled state or cannot be split into independent sub-problems. It is best suited for large documents and workflows requiring scalable reasoning.