context-compression

Compress conversation history to reduce token usage in AI agent sessions.

7|1|Updated Jan 14, 2026

Search Tags:#session management #token optimization #agent memory #context compression #llm efficiency #conversation summarization

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing large amounts of information in AI agent sessions, preventing excessive token consumption and ensuring efficient operation.

Core Features & Use Cases

Context Summarization: Implements strategies to condense conversation history and session data.
Token Optimization: Reduces the number of tokens used, leading to cost savings and faster processing.
Use Case: When an agent has been active for a long session and is approaching token limits, this skill can compress the existing context to make room for new information and continued processing.

Quick Start

Use the context compression skill to summarize the current conversation history.

Frequently Asked Questions about context-compression

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

FAQPage Schema

How do I reduce token usage in long-running AI agent sessions?▼

Reduce token usage in long-running AI agent sessions by implementing context compression strategies that summarize conversation history and condense session data. This maintains session state within token limits while optimizing processing efficiency.

What is context compression for LLM efficiency?▼

Context compression for LLM efficiency is the process of condensing conversation history and session data to manage large amounts of information. It prevents excessive token consumption during complex task execution and long-running agent conversations.

When do I need conversation summarization for agent memory management?▼

You need conversation summarization for agent memory management when an active agent approaches token limits. Compressing existing context makes room for new information and ensures continued processing during complex task execution.

What is the best way to optimize tokens-per-task in complex agent executions?▼

The best way to optimize tokens-per-task in complex agent executions is applying context summarization strategies to condense session state. This reduces overall token counts, leading to cost savings and faster processing speeds.

Can I maintain session state within token limits using context compression?▼

Yes, you can maintain session state within token limits using context compression. It condenses conversation history and session data to prevent exceeding limits while preserving necessary context for continued agent operations.