Compress natural language markdown files into a concise format while preserving code blocks.
npx skills add https://github.com/achmf/KostaHub --skill compress-achmf
Please help me install this Agent Skill. Skill: compress Source: https://github.com/achmf/KostaHub/tree/main/.agents/skills/compress Command: npx skills add https://github.com/achmf/KostaHub --skill compress-achmf
This Skill addresses the issue of high input token consumption by compressing natural language memory files into a concise, caveman-style format while preserving technical integrity.
Use the compress skill to reduce the token size of the CLAUDE.md file in the current directory.
High-intent search queries and answers about installing and using this skill.
To reduce input token usage for markdown memory files, compress natural language documentation into a concise format. This process strips filler words while preserving technical structures like code blocks to optimize LLM context consumption.
Yes, markdown compression preserves code blocks and technical metadata. The compression mechanism operates strictly on natural language text, ensuring that URLs, code snippets, and technical structures remain fully intact during the token optimization process.
The best way to compress CLAUDE.md files for token optimization is using automated scripts that convert text to a concise format. This approach safely reduces file size while maintaining the core technical integrity required by the LLM.
You can safely compress documentation files without losing data by using automated validation and backup mechanisms. Original files are backed up before transformation, and multi-step validation with automated retries ensures content integrity throughout the compression process.
Yes, a key limitation is that sensitive files like credentials or private keys should not be compressed. Built-in safety heuristics automatically detect and prevent the compression of these sensitive files to ensure data security and avoid accidental exposure.
Yes, you need tiktoken along with anthropic dependencies to optimize LLM input tokens for markdown. These libraries provide the necessary token counting and language model integrations to accurately measure and reduce file sizes.
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