token-formatter

To reduce token usage for LLM prompts, you can compress verbose Markdown documentation into token-efficient formats. This process applies configurable compression rules and abbreviations while preserving the essential meaning needed for context window optimization.

Yes, you can check token counts using a Python script with optional tiktoken-based counting. Running the provided count_tokens.py script on your input file calculates the exact token count, helping you decide if compression is necessary for your LLM context window.

The best way to compress a verbose README for an LLM context window is applying configurable compression levels that transform structured data, lists, and parameters into token-smart formats. This preserves essential meaning while applying abbreviations and symbols to minimize token usage.

Yes, token compression works with standard Python scripts and pipe operations. You can run the compress.py script directly on an input Markdown file or pipe content into it using standard command-line operations with configurable compression levels.

Configurable compression levels are available for token-efficient formatting of documentation, allowing you to control the intensity of verbose content reduction. You can specify a level, such as level 2, when running the Python script to balance token savings against the preservation of essential meaning.

You should avoid token compression on Markdown files when the uncompressed text is already within your LLM context window limits, or when maximum detail and original phrasing are strictly required. Aggressive compression levels might alter structured data or parameters, risking the loss of nuanced meaning.