context-optimization

Summarize conversation history and mask tool outputs to reduce token usage.

7|1|Updated Jan 14, 2026

Search Tags:#cost reduction #context optimization #token reduction #observation masking #kv-cache #agent efficiency

Ready to run

npx skills add https://github.com/eyadsibai/ltk --skill context-optimization-eyadsibai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-optimization
Source: https://github.com/eyadsibai/ltk/tree/main/plugins/ltk-core/skills/context-optimization
Command: npx skills add https://github.com/eyadsibai/ltk --skill context-optimization-eyadsibai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing and reducing the token count within an AI agent's context window, thereby lowering operational costs and improving efficiency.

Core Features & Use Cases

Context Compression: Summarizes message history and tool outputs to fit more information within token limits.
Observation Masking: Replaces verbose tool outputs with concise references, significantly reducing token usage.
KV-Cache Optimization: Organizes context for better cache hit rates in stable workloads.
Context Partitioning: Splits complex tasks among sub-agents with isolated contexts.
Use Case: When an agent's conversation history is growing too large, use this skill to summarize older turns and mask lengthy tool outputs, allowing the agent to maintain focus and stay within budget.

Quick Start

Use the context-optimization skill to summarize the last 5 turns of the conversation history.

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 my AI agent's conversation history grows too large?▼

To reduce token costs for large conversation histories, apply context compaction techniques like summarizing older message turns and masking verbose tool outputs with concise references. This optimizes agent context, cuts expenditure, and maintains focus within budget limits.

What is observation masking and how does it optimize agent context?▼

Observation masking optimizes agent context by replacing lengthy tool outputs with concise references. This selective data elision significantly reduces token usage, allowing the agent to process more relevant information within the context window.

How do I manage context window limitations for complex tasks?▼

Manage context window limitations for complex tasks by partitioning the context and splitting the workload among sub-agents. This isolates contexts, keeping token counts manageable while preserving effective context capacity for each specific task.

What's the best way to improve KV-cache hit rates for stable agent workloads?▼

The best way to improve KV-cache hit rates is through KV-cache optimization, which organizes the agent's context specifically for stable workloads. This structural organization maximizes cache efficiency and reduces redundant token processing.

Can I summarize specific conversation turns to fit more information within token limits?▼

Yes, you can summarize specific conversation turns to fit more information within token limits. Context compression uses algorithmic summarization to condense message history, effectively expanding the usable context capacity without exceeding token constraints.

When should I not use context partitioning for agent memory management?▼

You should avoid context partitioning for agent memory management when tasks require tight cross-referencing of shared data. Splitting sub-agents isolates contexts, which can hinder performance if the workload relies on continuous access to the full conversation history.