Software Development
Token Diet
Cut LLM output tokens 40–70% by stripping grammatical scaffolding while preserving every fact — telegraphic output modes, when they pay (pipelines, long sessions) and when they don't (single shots, human-facing prose), with the mode lines to switch on demand. Use when asked make the model respond tersely, cut output token costs, caveman mode, or compress agent-to-agent messages. Produces the diet-mode instruction block ready to paste, the three compression levels with examples, the economics of when each pays, and the never-diet list.
- Publisher / source
- mohitagw15856/pm-claude-skills (found on the CSV list)
- Upstream record
- mohitagw15856/pm-claude-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication · recorded passed scan 2026-11-03
- Content fingerprint
50eb44e4be4ed18ddd40492f7c5c270b413da4d6215b5b69013a311980a81626