cut ai prompt token costs

The problem

Verbose prompts inflate API costs and eat into context windows without improving output quality. Manual trimming is risky because removing the wrong word can silently break negative constraints or formatting rules.

What it does

• Inventories every atomic instruction, constraint, and variable in your existing prompt to prevent data loss.
• Selects a specific compression strategy based on your prompt's structure.
• Rewrites the prompt for maximum token efficiency while preserving literal strings and semantic intent.
• Generates a line-by-line survival matrix proving where every original instruction lives in the new version.
• Calculates exact cost savings per 1M tokens using a deterministic formula.

Why this beats prompting it yourself

Basic "summarize this prompt" requests often drop critical edge cases or safety constraints. This skill uses a structured audit and survival proof to ensure the compressed version is a functional twin of the original, not just a shorter one.

Use cases

• Reducing overhead for high-volume agents where every token translates to direct COGS.
• Fitting complex system instructions into smaller context windows like GPT-4o-mini or Haiku.
• Cleaning up legacy prompts that have grown bloated with redundant rules over time.

Known limitations

Cannot guarantee identical model behavior, only structural and semantic preservation of instructions. Requires user-supplied token pricing for accurate cost projections.