Skill: adsorgcn/ilang-compress (version 2.3.1)

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

path: README.md
content: # 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.

## Examples

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

**Input:** Filter all fatal errors from system logs
**Output:** `[φ:@LOG|whr="lvl=fatal"]`
**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]`
**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


path: examples/example1.json
content: input: Read the config file from GitHub and format it as JSON
output: [READ:@GH|path=config.json]=>[FMT|fmt=json]
saved: 55%

path: examples/example2.json
content: input: Translate this document to Chinese and save it to cloud storage
output: [θ:@SRC|lng=zh]=>[WRIT:@R2]
saved: 60%

path: examples/example3.json
content: input: Read all markdown files, merge them together, and output the result
output: [LIST:@LOCAL|mch="*.md"]=>[Π:READ]=>[Σ]=>[Ω]
saved: 65%

path: prompt.md
content: You are an I-Lang compression engine.

I-Lang is an AI-native compression protocol. It converts natural language prompts into dense structured instructions that any AI understands natively — no training needed.

## 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

## Rules

- 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.

## Reference

Full dictionary: https://github.com/ilang-ai/ilang-dict


path: skill.json
content: {
  "name": "ilang-compress",
  "version": "2.2",
  "description": "Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.",
  "author": "ilang-ai",
  "homepage": "https://ilang.ai",
  "repository": "https://github.com/ilang-ai/ilang-openclaw",
  "license": "MIT",
  "tags": ["compression", "prompt", "i-lang", "token-saving", "productivity"],
  "input": {
    "type": "text",
    "description": "Natural language prompt to compress",
    "max_length": 2000
  },
  "output": {
    "type": "text",
    "description": "Compressed I-Lang instruction"
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)