How do I trigger the optimization workflow?

You can invoke it explicitly for specific files or use the --foryou flag to tell Claude to optimize the current context for its own consumption.

How does the AI Context Optimizer reduce token counts?

It uses context engineering to remove redundant explanations, filler phrases, and verbose transitions, replacing them with high-density bullet points and imperative language.

Will using this skill cause me to lose important information?

The skill follows a specific 'Preserve' logic that protects domain-specific knowledge, critical constraints, and actionable instructions while only removing 'common knowledge' that AI already understands.

Why does this skill convert content to English?

English is currently the most token-efficient language for major AI models, allowing you to pack significantly more information into the same context window compared to other languages.

AI Context Optimizer

Name: AI Context Optimizer
Author: reedom

byreedom

생산성 및 워크플로

Optimizes content for AI consumption by reducing token counts while preserving essential context and instructions.

소개

The AI Context Optimizer (ai-optimization) skill is a specialized utility designed for context engineering, allowing users to maximize the efficiency of AI prompt windows. It ruthlessly prunes redundant explanations, filler words, and verbose transitions while carefully preserving critical constraints, domain-specific knowledge, and actionable rules. By typically reducing token usage by 30-70%, this skill helps developers fit more information into limited context budgets, ensuring that Claude or other models receive the most high-density, relevant information possible for complex tasks.

주요 기능

Automated removal of redundancy, filler words, and verbose transitions
Token reduction of 30-70% via context engineering principles
0 GitHub stars
Standardized XML and Markdown table formatting for optimal AI parsing
Smart preservation of critical domain knowledge and technical constraints
Translation to token-efficient English for multilingual source material

사용 사례

Cleaning up transient content to fit more actionable data into a single turn
Compressing large documentation or codebase summaries for Claude's context window
Optimizing system prompts to save costs and improve response speed

소개

주요 기능

Automated removal of redundancy, filler words, and verbose transitions
Token reduction of 30-70% via context engineering principles
0 GitHub stars
Standardized XML and Markdown table formatting for optimal AI parsing
Smart preservation of critical domain knowledge and technical constraints
Translation to token-efficient English for multilingual source material

사용 사례

Cleaning up transient content to fit more actionable data into a single turn
Compressing large documentation or codebase summaries for Claude's context window
Optimizing system prompts to save costs and improve response speed