Project overview
Token-Diet lays out an ordered optimization workflow for LLM applications: prune retrieved context first, reuse cached material, route work by complexity, keep structured state, and compress prose only at the final step. It also defines metrics and guardrails for token use, latency, cost, and quality, while excluding code, schemas, legal text, URLs, and identifiers from linguistic compression.
Repository facts
- Primary language
- Not detected
- License
- MIT
- Repository updated
- May 19, 2026
- Default branch
- main
Resource types
General skill
Use cases
AI and agent development
Platforms
Claude Code, Codex, and more
Capabilities
MemorySecurity guardrailSummarization