Compress JSON payloads to reduce token usage in LLM prompts.
npx skills add https://github.com/trmaphi/dotfiles --skill toon-trmaphi
Please help me install this Agent Skill. Skill: toon Source: https://github.com/trmaphi/dotfiles/tree/main/.claude/skills/toon Command: npx skills add https://github.com/trmaphi/dotfiles --skill toon-trmaphi
JSON payloads often cause token bloat in LLM prompts. TOON reduces token usage by stripping structural noise from JSON, making prompts leaner and faster to process.
Pipe your JSON through toon to produce a compact, token-efficient JSON payload suitable for LLM prompts.
High-intent search queries and answers about installing and using this skill.
To compress JSON to save tokens in LLM prompts, pipe your JSON data through a utility that strips structural noise like quotes and braces. This makes prompts leaner while preserving essential structure for processing.
The best way to reduce token usage for large API responses in LLM prompts is to flatten nested keys and strip structural noise. This minimizes prompt size to help data fit within token budgets while retaining essential payload information.
Yes, you can restore compressed JSON back to its original structure using a decode path. This ensures you can compress data for prompt efficiency and fully recover the original nested JSON format when needed.
Compressing JSON for token savings preserves nested keys and values through optional key folding, which flattens nested keys to a concise a.b.c: value representation. This retains essential data structure while reducing tokens.
To check token savings before and after JSON compression, use diagnostics statistics to view before and after token counts. This verifies that your prompt size has been effectively reduced for LLM processing.
You should not use JSON compression for LLM prompts if your target LLM model requires strict standard JSON syntax to parse inputs correctly, because stripping quotes and braces creates a non-standard format that may break downstream parsing.
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