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.
Best use case
less-token is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using less-token should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/less-token/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How less-token Compares
| Feature / Agent | less-token | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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, Eastsoft Inc., Canada.
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