ilang-compress
Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install openclaw-skills-ilang-compress
Repository
Skill path: skills/adsorgcn/ilang-compress
Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: openclaw.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install ilang-compress into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/openclaw/skills before adding ilang-compress to shared team environments
- Use ilang-compress for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw 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
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
---
## Skill Companion Files
> Additional files collected from the skill directory layout.
### README.md
```markdown
# 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
```
### _meta.json
```json
{
"owner": "adsorgcn",
"slug": "ilang-compress",
"displayName": "I-Lang Compress",
"latest": {
"version": "2.3.1",
"publishedAt": 1772630364945,
"commit": "https://github.com/openclaw/skills/commit/f6e573208a00b6c2f7dbfd60592bff0983e4b326"
},
"history": []
}
```