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context-compression

Compress AI conversation histories to reduce token usage and prevent context limits.

Updated Aug 23, 2026
Search Tags:#summarization#llm#session management#ai agent#token optimization#context compression
One-click install
npx skills add https://github.com/rustams/inbound --skill context-compression-rustams
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-compression
Source: https://github.com/rustams/inbound/tree/main/.cursor/skills/context-compression
Command: npx skills add https://github.com/rustams/inbound --skill context-compression-rustams

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of managing large conversation histories in AI agent sessions, preventing context window limits and reducing token usage for cost and performance optimization.

Core Features & Use Cases

  • Context Compression: Implements strategies like anchored iterative summarization and opaque compression to reduce token count.
  • Tokens-Per-Task Optimization: Focuses on minimizing total tokens consumed for task completion, not just per request.
  • Use Case: When an AI agent is working on a large codebase or a long-running task that generates millions of tokens, this skill can compress the history to keep the agent focused and efficient, preventing it from "forgetting" critical information.

Quick Start

Use the context-compression skill to summarize the conversation history to reduce token usage.

Frequently Asked Questions about context-compression

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

FAQPage Schema
How do I compress AI conversation history to optimize token usage?▼

AI context compression uses anchored iterative summarization and opaque compression to reduce token count while maintaining session intent, file modifications, decisions, and next steps.

What is anchored iterative summarization for AI session management?▼

Anchored iterative summarization is a context compression technique that progressively summarizes conversation history while anchoring critical details like file modifications, decisions, and next steps to prevent the AI from forgetting key information.

When do I need context compression for an AI coding agent?▼

You need context compression when an AI agent works on a large codebase or long-running task generating millions of tokens, preventing context window limitations and keeping the agent focused and efficient.

How do I optimize tokens-per-task for long-running AI agent sessions?▼

Optimize tokens-per-task by applying regenerative full summaries to the conversation history, minimizing the total tokens consumed for task completion rather than just reducing tokens per individual request.

Does context compression work with large codebases to prevent context window limits?▼

Yes, context compression addresses the artifact trail problem for coding agents by managing extensive conversation histories, preventing context window limits, and reducing overall token usage for cost and performance optimization.

What are the limitations of opaque compression for AI context management?▼

While opaque compression reduces token count effectively, it focuses on tokens-per-task optimization which may abstract away granular conversational details, relying on regenerative full summaries to retain critical session intent and next steps.

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