Less Token

Save 40-65% tokens on summarization tasks. Compress verbose summary prompts into structured one-line instructions. Text-to-text translator only — no CLI, no API key, no install, no external dependencies. Works on ChatGPT, Claude, Gemini, DeepSeek, Kimi. Instruction-only, zero dependencies.

Save 40-65% tokens on summarization tasks. Compress verbose natural language prompts into structured one-line instructions that any AI understands.

This skill is a text-to-text translator only. It does not access files, fetch URLs, execute commands, or call external services. It only converts your summarization prompts into compressed syntax.

What You Get

1. 40-65% fewer tokens — Compress long summarization prompts into one-line instructions.
2. Same result — AI produces identical output from the compressed instruction.
3. Cross-platform — Compressed instructions work on ChatGPT, Claude, Gemini, DeepSeek, Kimi, 豆包, 元宝.
4. No install — No CLI, no brew, no npm, no binary, no API key. Copy, paste, done.

How to Use

1. Copy the full protocol text from this skill page
2. Paste it into any AI conversation
3. AI responds — ready to compress

Quick Test

After pasting, try:

• "Compress this: Please summarize the key points from this document in 3 professional bullet points"
• AI returns: [SUM|sty=bullets,cnt=3,ton=pro]=>[OUT]
• 70% fewer tokens. Same result.

Compression Templates

What you want	Verbose prompt	Compressed
Short summary	"Give me a brief summary of the main points"	[SUM|len=short]=>[OUT]
3 bullet points	"Summarize in 3 concise bullet points"	[SUM|sty=bullets,cnt=3]=>[OUT]
Professional report	"Create a professional executive summary in Markdown"	[SUM|ton=pro,sty=executive,fmt=md]=>[OUT]
Key findings only	"Extract only the key findings and important data"	[SUM|key=findings]=>[OUT]
Summarize + translate	"Summarize then translate to Chinese"	[SUM|len=short]=>[TRANSLATE|lang=zh]=>[OUT]
Compare + summarize	"Compare these two and summarize the differences"	[CMP]=>[DIFF]=>[SUM|sty=bullets]=>[OUT]
Reformat summary	"Summarize as bullet points in Markdown"	[SUM|sty=bullets]=>[FMT|fmt=md]=>[OUT]

Before & After

Before (28 words):

Please read through this document carefully, identify the most important points and key takeaways, then write a concise professional summary using bullet points.

After (7 words):

text

[SUM|key=important,sty=bullets,ton=pro]=>[OUT]

75% fewer tokens. Same result.

Before (22 words):

Take the main findings from the text above and rewrite them as a short executive summary suitable for a business audience.

After (5 words):

text

[SUM|sty=executive,ton=pro]=>[OUT]

77% fewer tokens. Same result.

Comparison

Feature	CLI-based tools	Less Token
Install required	Yes (brew, npm, binary)	No
API key required	Yes	No
Works on	Single platform	Any AI platform
Token efficiency	Standard prompts	40-65% fewer tokens
Setup time	5-10 minutes	30 seconds
External dependencies	Multiple	Zero

Tested Platforms

ChatGPT ✅ · Claude ✅ · Gemini ✅ · DeepSeek ✅ · Kimi ✅ · 豆包 ✅ · 元宝 ✅

Links

• Protocol & tools: https://ilang.ai
• Full dictionary: https://github.com/ilang-ai/ilang-dict
• Research: https://research.ilang.ai

License

MIT — Free to use, share, and build on.

© 2026 I-Lang Research, Eastsoft Inc., Canada.