ilang-compress
Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.
Best use case
ilang-compress is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.
Teams using ilang-compress 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/ilang-compress/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ilang-compress Compares
| Feature / Agent | ilang-compress | 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?
Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings.
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
# 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
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