How do I install the Ilang Compress skill?

Run npx skillmds@latest add ilang-ai/ilang-compress in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ilang Compress safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ilang Compress?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ilang Compress free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ilang Compress?

ilang-ai (@ilang-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ilang Compress by ilang-ai — Agent Skill

Name: Ilang Compress
Rating: 4.666666666666667 (3 reviews)
Author: ilang-ai

File contents

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.

Safety note: I-Lang output is text notation, not executable code. The instruction set includes action verbs (READ, WRIT, DEL, COPY, SYNC) and resource references (@GH, @DRIVE, @LOCAL) that describe operations in compressed form but do not execute by themselves. If you pass compressed output to an agent or tool that interprets these as commands, real actions may be triggered. Always review before feeding to execution environments.

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%

Author

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

I-Lang v2.0

---name: ilang-compressdescription: Compress natural language prompts into I-Lang — AI-native structured instructions. 40-65% token savings. Output is text notation only — review before passing to execution agents.--- # 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. > **Safety note**: I-Lang output is text notation, not executable code. The instruction set includes action verbs (READ, WRIT, DEL, COPY, SYNC) and resource references (@GH, @DRIVE, @LOCAL) that describe operations in compressed form but do not execute by themselves. If you pass compressed output to an agent or tool that interprets these as commands, real actions may be triggered. Always review before feeding to execution environments. ## 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

Frequently asked questions

Ilang Compress