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.
- Same result — AI produces identical output from the compressed instruction.
- Cross-platform — Compressed instructions work on ChatGPT, Claude, Gemini, DeepSeek, Kimi, 豆包, 元宝.
- 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
- Paste it into any AI conversation
- 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: INLINECODE0
- 70% fewer tokens. Same result.
Compression Templates
| What you want | Verbose prompt | Compressed |
|---|
| Short summary | "Give me a brief summary of the main points" | INLINECODE1 |
| 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, Eastsoft Inc., Canada.
Less Token
在摘要任务上节省40-65%的令牌。将冗长的自然语言提示压缩为任何AI都能理解的结构化单行指令。
此技能仅为文本到文本的转换器。 它不访问文件、获取URL、执行命令或调用外部服务。它仅将您的摘要提示转换为压缩语法。
您将获得
- 1. 减少40-65%的令牌 — 将长摘要提示压缩为单行指令。
- 相同结果 — AI从压缩指令生成相同的输出。
- 跨平台 — 压缩指令适用于ChatGPT、Claude、Gemini、DeepSeek、Kimi、豆包、元宝。
- 无需安装 — 无需命令行、brew、npm、二进制文件或API密钥。复制、粘贴，完成。
使用方法
- 1. 从此技能页面复制完整的协议文本
- 将其粘贴到任何AI对话中
- AI响应 — 即可开始压缩
快速测试
粘贴后，尝试：
- - 压缩此内容：请用3个专业要点总结本文档的关键点
- AI返回：[SUM|sty=bullets,cnt=3,ton=pro]=>[OUT]
- 减少70%的令牌。相同结果。
压缩模板
| 您想要的内容 | 冗长提示 | 压缩后 |
|---|
| 简短摘要 | 给我一个主要观点的简要总结 | [SUM\ | len=short]=>[OUT] |
| 3个要点 |
用3个简洁的要点总结 | [SUM\|sty=bullets,cnt=3]=>[OUT] |
| 专业报告 | 用Markdown创建一份专业执行摘要 | [SUM\|ton=pro,sty=executive,fmt=md]=>[OUT] |
| 仅关键发现 | 仅提取关键发现和重要数据 | [SUM\|key=findings]=>[OUT] |
| 总结+翻译 | 总结然后翻译成中文 | [SUM\|len=short]=>[TRANSLATE\|lang=zh]=>[OUT] |
| 比较+总结 | 比较这两者并总结差异 | [CMP]=>[DIFF]=>[SUM\|sty=bullets]=>[OUT] |
| 重新格式化摘要 | 用Markdown格式的要点总结 | [SUM\|sty=bullets]=>[FMT\|fmt=md]=>[OUT] |
前后对比
之前（28个词）：
请仔细阅读本文档，识别最重要的点和关键要点，然后使用要点编写一份简洁的专业摘要。
之后（7个词）：
[SUM|key=important,sty=bullets,ton=pro]=>[OUT]
减少75%的令牌。相同结果。
之前（22个词）：
从上述文本中提取主要发现，并将其改写为适合商业受众的简短执行摘要。
之后（5个词）：
[SUM|sty=executive,ton=pro]=>[OUT]
减少77%的令牌。相同结果。
对比
| 功能 | 基于命令行的工具 | Less Token |
|---|
| 需要安装 | 是（brew、npm、二进制文件） | 否 |
| 需要API密钥 |
是 | 否 |
| 适用范围 | 单一平台 | 任何AI平台 |
| 令牌效率 | 标准提示 | 减少40-65%的令牌 |
| 设置时间 | 5-10分钟 | 30秒 |
| 外部依赖 | 多个 | 零 |
测试平台
ChatGPT ✅ · Claude ✅ · Gemini ✅ · DeepSeek ✅ · Kimi ✅ · 豆包 ✅ · 元宝 ✅
链接
- - 协议与工具：https://ilang.ai
- 完整词典：https://github.com/ilang-ai/ilang-dict
- 研究：https://research.ilang.ai
许可证
MIT — 免费使用、分享和在此基础上构建。
© 2026 I-Lang Research, Eastsoft Inc., 加拿大。
