lmc

Yes, you can reduce token consumption by applying configurable retention levels during lossless maximal compaction. This prunes verbose prose and optional content from documentation while preserving essential logic, yielding ultra-compact outputs for token-critical workflows.

Lossless maximal compaction is a technique that compresses content to maximal token efficiency while preserving core functionality, signatures, and essential logic. It drops verbose prose, redundant comments, and optional content to generate ultra-compact outputs.

You can configure retention levels by running the compaction command on a target file. This allows you to specify the scope of minimal summary generation, balancing the pruning of optional content and comments against the preservation of core logic.

Yes, token optimization for Python source files preserves class definitions and core logic. The compaction process specifically retains essential behavior while dropping bloated content, ensuring the compacted output remains functionally lossless.

The primary limitation of lossless code compaction is that excessive pruning of comments and verbose prose may reduce human readability. While core logic and signatures are preserved, the ultra-compact output is optimized for token efficiency rather than documentation clarity.