Verified:GitHub

add-token-efficiency

Enforce compression patterns for AI resource generation to minimize token consumption.

8|2|Updated Mar 2, 2026

Search Tags:#prompt engineering #token efficiency #resource optimization #developer productivity #cost reduction #ai compression

Ready to run

npx skills add https://github.com/brabos-ai/code-addiction --skill add-token-efficiency

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: add-token-efficiency
Source: https://github.com/brabos-ai/code-addiction/tree/main/framwork/.codeadd/skills/add-token-efficiency
Command: npx skills add https://github.com/brabos-ai/code-addiction --skill add-token-efficiency

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of high token consumption in AI interactions by enforcing strict compression patterns for all generated resources, ensuring clarity and conciseness.

Core Features & Use Cases

Mandatory Compression Patterns: Defines rules for minifying JSON, using Markdown lists for instructions, employing glob patterns for paths, and avoiding decorative formatting.
Token Optimization: Helps reduce costs and improve processing speed by minimizing the amount of text sent to and from AI models.
Use Case: When creating new commands or skills, this Skill ensures that their descriptions and associated data are formatted in the most token-efficient way possible, adhering to predefined abbreviations and structural guidelines.

Quick Start

Apply the token efficiency guidelines to compress the provided JSON specification.

Frequently Asked Questions about add-token-efficiency

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

FAQPage Schema

How do I reduce token consumption when generating AI commands and documentation?▼

To reduce token consumption, apply mandatory compression patterns that minify JSON, use Markdown lists, and employ glob patterns. This enforces strict formatting rules for AI resource generation to minimize text while preserving clarity.

What is prompt compression and how does it optimize AI resource generation?▼

Prompt compression is the practice of minifying JSON, using Markdown lists, and avoiding decorative formatting to optimize AI resources. It reduces token consumption and processing costs while preserving clarity for commands and documentation.

Can I use glob patterns and abbreviations to minimize token usage in AI prompts?▼

Yes, you can use glob patterns for paths and predefined abbreviations to minimize token usage. The token efficiency guidelines enforce these specific formatting rules to compress commands, skills, and documentation effectively.

What's the best way to format JSON specifications to save AI processing costs?▼

The best way to format JSON specifications is through mandatory minification. By enforcing strict compression patterns, you eliminate decorative formatting and minimize text sent to AI models, directly reducing costs and improving processing speed.

Does token efficiency for AI prompts affect the clarity of generated skills and documentation?▼

Token efficiency does not sacrifice clarity. The compression patterns are designed to minimize token consumption while preserving clarity, ensuring that commands, skills, and documentation remain understandable despite strict formatting constraints.

When should I apply token compression patterns to my AI development workflow?▼

You should apply token compression patterns when creating new commands, skills, or documentation. This ensures descriptions and associated data are formatted in the most token-efficient way possible, reducing costs across any AI resource generation.