Less Token

Less Token

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):

[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):

[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, iLang Inc., Canada.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ilang-ai
• Source: ilang-ai/ilang-openclaw
• License: MIT
• Homepage: https://ilang.ai

Install and usage instructions live in the source repository linked above.