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Ilang Compress

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Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.

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  • Added September 5, 2026
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Scanned September 5, 2026

npx -y skills add dvcrn/openclaw-skills-marketplace --skill ilang-compress --agent claude-code

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SKILL.md
---
name: ilang-compress
description: "Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings."
homepage: https://ilang.ai
---

# I-Lang Compress

An AI-native prompt compression protocol created by a Chinese developer.

Compress natural language prompts into dense structured instructions that any AI understands natively. 40-65% token savings, zero training needed.

## Why I-Lang

Token is money. Every prompt you send to GPT/Claude/Gemini, you pay by token. I-Lang compresses your instructions into a fraction of the original size — AI reads it just as well, you pay less.

## How to compress

When the user asks to compress a prompt, convert it to I-Lang syntax following these rules.

### Syntax

Single operation: `[VERB:@ENTITY|mod1=val1,mod2=val2]`
Pipe chain: `[VERB1:@SRC]=>[VERB2]=>[VERB3:@DST]`
Each step receives previous output as @PREV.

### Available Verbs (62)

Data I/O: READ, WRIT, DEL, LIST, COPY, MOVE, STRM, CACH, SYNC, Π
Transform: Σ, Δ, φ, ∇, DEDU, ∂, CHNK, FLAT, NEST, λ, REDU, PIVT, TRNS, ENCD, DECD, ξ, ζ, EXPN, θ, FMT
Analysis: ψ, CLST, SCOR, BNCH, AUDT, VALD, CNT, μ, TRND, CORR, FRCS, ANOM
Generation: CREA, DRFT, PARA, EXTD, SHRT, STYL, TMPL, FILL
Output: Ω, DISP, EXPT, PRNT, LOG
Meta: VERS, HELP, DESC, INTR, SELF, ECHO, NOOP

### Modifiers (28)

tgt, src, dst, frm, to, scp, dep, rng, whr, mch, exc, lim, off, top, bot, fmt, lng, sty, ton, len, col, row, srt, grp, typ, enc, chr, cap

### Entities (14)

@R2, @COS, @GH, @DRIVE, @LOCAL, @WORKER, @CF, @SCREEN, @LOG, @NULL, @STDIN, @SRC, @DST, @PREV

### Compression Guidelines

- Output the compressed I-Lang instruction first, then a brief explanation of what each step does.
- Use pipe chains for multi-step operations.
- Use Greek symbols where applicable (Σ for merge, Δ for diff, φ for filter, etc.)
- Maximize compression while preserving complete semantics.
- If input is ambiguous, ask the user for clarification.

## Examples

**Input:** Read the config file from GitHub and format it as JSON
**Output:** `[READ:@GH|path=config.json]=>[FMT|fmt=json]`
**Explanation:** READ fetches from GitHub, FMT converts to JSON format.
**Saved:** 55%

**Input:** Filter all fatal errors from system logs
**Output:** `[φ:@LOG|whr="lvl=fatal"]`
**Explanation:** φ (filter) selects only entries matching fatal level.
**Saved:** 55%

**Input:** Read all markdown files, merge them, summarize in 3 bullets, output
**Output:** `[LIST:@LOCAL|mch="*.md"]=>[Π:READ]=>[Σ|len=3]=>[Ω]`
**Explanation:** LIST finds files, Π batch-reads, Σ summarizes to 3 items, Ω outputs.
**Saved:** 65%

## Links

- Homepage: https://ilang.ai
- Dictionary: https://github.com/ilang-ai/ilang-dict

## Author

Built by ilang-ai from China. I-Lang is open source under MIT license.

I-Lang v2.0

Files in this skill

  • README.md1 KB
  • SKILL.md2.8 KB
  • examples/example1.json149 B
  • examples/example2.json143 B
  • examples/example3.json168 B
  • prompt.md1.5 KB
  • skill.json606 B

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