Compress CLAUDE.md, todos, and preferences into caveman format with backups.
npx skills add https://github.com/huangzida/skills --skill compress-huangzida
Please help me install this Agent Skill. Skill: compress Source: https://github.com/huangzida/skills/tree/main/compress Command: npx skills add https://github.com/huangzida/skills --skill compress-huangzida
Manual management of memory files for AI prompts leads to token bloat and slower responses. This skill compresses natural language content into caveman format while preserving essential substance, URLs, code blocks, and structure.
Run caveman compress on a target natural-language file to produce a compressed version and a backup.
High-intent search queries and answers about installing and using this skill.
You can compress natural language memory files into caveman format to reduce input tokens while preserving substance, URLs, code blocks, and file paths. This targets CLAUDE.md, todos, and preferences to shorten prompt length.
Caveman format compression is a technique that transforms natural language memory files into a compact representation to save tokens. It preserves essential substance, URLs, code blocks, headings, and file paths while reducing overall length.
Yes, the compression workflow creates a backup of your original content in FILE.original.md before safely overwriting the source file with the compressed caveman format version.
Validation follows compression to ensure fidelity by checking the output against the source. It uses a deterministic CLI workflow with retry loops to guarantee the compressed file preserves essential substance and structure.
You can compress todos and preferences in addition to CLAUDE.md. The tool targets all these natural language memory files, applying deterministic processing to reduce tokens across your memory documentation.
The best way to automate token savings is running a deterministic CLI compression workflow on your memory files. It safely overwrites sources with compressed caveman format text and validates fidelity through retry loops.
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