prompt-compressor

To compress long prompts without losing semantic meaning, the skill normalizes whitespace, abbreviates phrases, and removes low-information sentences. This reduces token usage in lengthy instructions or system prompts while preserving the core context.

Yes, you can preserve code blocks when compressing verbose prompts by using the preserve_code option. This ensures programming contexts and code snippets remain completely intact during the whitespace normalization and abbreviation process.

The best way to shorten research prompts approaching token limits is applying a target_ratio to guide compression. This removes clutter and abbreviates phrases, effectively compacting the context to fit within strict token constraints.

Yes, prompt compression automatically normalizes whitespace and abbreviates phrases. It identifies and removes low-information sentences to compact lengthy instructions, ensuring the resulting text uses fewer tokens while retaining functional requirements.

A limitation of using target_ratio for prompt compression is that aggressively reducing tokens may strip necessary context. If preserve_code is disabled, formatting within code blocks may also be altered or lost during the abbreviation process.