Context Compressor

Compress contextual information to optimize token usage for AI processing.

Updated Mar 11, 2026

Search Tags:#summarization #context #efficiency #compression #token optimization #large language models

Ready to run

npx skills add https://github.com/LuuKhoaHoc/erp-template --skill context-compressor-luukhoahoc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Context Compressor
Source: https://github.com/LuuKhoaHoc/erp-template/tree/main/.agent/skills/context-compressor
Command: npx skills add https://github.com/LuuKhoaHoc/erp-template --skill context-compressor-luukhoahoc

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 within a limited token budget, ensuring that understanding is maintained while minimizing resource consumption.

Core Features & Use Cases

Context Compression: Employs various strategies like summarization, outlining, and diff-only modes to reduce token usage.
Token Efficiency: Prioritizes essential information and avoids loading unnecessary data.
Use Case: When working with extensive codebases or lengthy documents, this Skill helps maintain a high-level understanding without exceeding token limits, allowing for more efficient AI-driven analysis and task completion.

Quick Start

Use the context compressor skill to summarize the file src/auth/login.ts.

Frequently Asked Questions about Context Compressor

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

FAQPage Schema

How do I reduce token usage when processing large codebases with AI?▼

To reduce token usage when processing large codebases, you can compress contextual information using summarization, outlining, and diff-only modes. This maintains high-level understanding while minimizing resource consumption and avoiding token limits.

What is context compression for large language models?▼

Context compression for large language models is the process of optimizing token usage by applying strategies like summarization and progressive disclosure. It ensures essential information is prioritized without loading unnecessary data.

How do I summarize a lengthy document for AI-driven analysis without exceeding token limits?▼

You can summarize a lengthy document for AI-driven analysis by applying context compression techniques like outlining and diff-only modes. This allows efficient task completion while staying within the token budget.

What's the best way to maintain context during ongoing task execution when token limits are a constraint?▼

The best way to maintain context during ongoing task execution under token constraints is progressive disclosure. This strategy manages context effectively by prioritizing essential information and avoiding loading unnecessary data.

Can I use diff-only mode to optimize token efficiency for code analysis?▼

Yes, you can use diff-only mode to optimize token efficiency for code analysis. It reduces token usage by focusing strictly on changes rather than loading the entire file or codebase context.

Why does my AI processing lose understanding of lengthy documents when approaching token limits?▼

AI processing loses understanding of lengthy documents near token limits because full context exceeds the budget. Applying context compression strategies like summarization and outlining ensures understanding is maintained while minimizing resource consumption.