I-Lang Prompt Compression Engine

Problem

Each prompt sent to GPT, Claude, or Gemini is billed by token. Long natural-language instructions, especially repeated multi-step workflows, become unnecessarily verbose. The I-Lang compression engine addresses this by rewriting prompts into a compact structured notation that still conveys intent to the model.

How It Works

The skill generates I-Lang text rather than executing commands. Core syntax includes a single operation such as [VERB:@ENTITY|mod1=val1] and pipe chains like [VERB1:@SRC]=>[VERB2]=>[VERB3:@DST], where @PREV receives the previous step's output. It uses verbs such as READ, WRIT, DEL, SYNC, and Σ, entities such as @GH, @DRIVE, and @LOCAL, and modifiers such as fmt, lim, and srt. For example, “read a config file from GitHub and format it as JSON” becomes [READ:@GH|path=config.json]=>[FMT|fmt=json]; “filter fatal errors from logs” becomes [φ:@LOG|whr="lvl=fatal"]. Output usually starts with the compressed instruction, followed by a brief step-by-step explanation.

Limits and Cautions

I-Lang output is text notation, not executable code. However, it contains action verbs and resource references that an agent or tool may interpret as commands, so real actions can occur if passed to an execution environment. Review output before using it downstream. If the source prompt is ambiguous, the skill should ask for clarification rather than guess. It is useful for compressing prompts, structuring multi-step workflow descriptions, and estimating model input size, but it is not a script generator and does not guarantee identical comprehension across all models.