Caveman Input Compressionv0.1.0

A developer compresses their agent's initialization files (SOUL.md, IDENTITY.md) into caveman-speak, cutting input tokens by ~45% and reducing API costs on every session load. This is ideal for frequent iterative development where token consumption adds up.

On a low-end Raspberry Pi running an AI agent, the user compresses MEMORY.md and HEARTBEAT.md to fit within strict token limits, enabling the agent to retain context longer without hitting API ceilings.

A CI/CD pipeline uses caveman-compress on AGENTS.md and HEARTBEAT.md to shrink bootstrap files, reducing agent initialization time from seconds to sub-second, speeding up automated testing and deployment.

A researcher testing new compression strategies compresses USER.md with automatic .original.md backup, ensuring they can revert if the caveman-speak degrades agent performance. The backup prevents accidental overwrite.

An AI team uses the fallback chain: Claude CLI for best quality, MiniMax for budget, DeepSeek for speed. They override COMPRESSION_MODEL to deepseek32 for cheap fast compression on routine files, saving money.

Offer a monthly subscription that auto-compresses agent workspace files for users, reducing their API costs. Revenue from recurring fees based on number of files or sessions.

Expose caveman-compress as an API endpoint where users pay per compression request, with higher pricing for sensitive files like AGENTS.md requiring feature flags.

License the skill to companies for internal use on their AI agent workspaces, including custom model integration and priority support. Revenue from annual enterprise licenses.