I-Lang Compress

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

Security Scan

The skill is an instruction-only prompt-compression translator (I-Lang) and its declared requirements and instructions are consistent with that purpose.

Purpose & Capability

The name/description (compress natural-language prompts into I-Lang) matches the provided SKILL.md, examples, and manifest. There are no unexpected required binaries, env vars, or config paths. The presence of entities like @GH, @R2, @COS, @LOCAL is reasonable for a compression format that can reference common storage targets — the skill does not itself request access to those services.

Instruction Scope

SKILL.md only instructs the agent how to translate user text into the I-Lang syntax and return a brief explanation. It does not instruct the agent to read local files, access environment variables, contact external endpoints, or execute I-Lang commands. The rule to ask for clarification on ambiguity is appropriately scoped.

Install Mechanism

This is an instruction-only skill with no install spec and no code files to run — lowest-risk install profile. Nothing is downloaded or written to disk by an installer.

Credentials

The skill declares no required environment variables, credentials, or config paths. Although the I-Lang vocabulary includes entities that reference cloud storage and services, the skill itself does not request or require credentials to produce compressed prompts.

Persistence & Privilege

always is false and model invocation is allowed (the platform default). The skill does not request elevated persistence or modification of agent/system configuration.

Assessment

This skill is an instruction-only translator that converts your natural-language prompts into a compact I-Lang representation. It does not itself access your files or cloud accounts and asks no credentials, so installing it is coherent with its stated purpose. Be aware: the compressed outputs can reference external targets (GitHub, cloud storage, local files). If you later pair this skill with another skill or tool that executes I-Lang instructions, that executor would need credentials and could perform actions — review and control any execution-capable skills before allowing them to act on I-Lang output. If you want extra assurance, test the skill with harmless, non-sensitive prompts and verify the outputs match expectations, and confirm the homepage/repository links before trusting it in production.

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