Llmcom Token Optimizer

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slug: llmcom-token-optimizer name: LLMCOM Token Optimizer version: 1.0.1 author: shalinda-j tags: [token-optimization, llmcom, context-management, cost-saving] description: Token-efficient context format using LLMCOM specification - reduces token usage by 70-80% through compact object notation.

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LLMCOM Token Optimizer

> 70-80% Token Savings using LLMCOM compact format

What is LLMCOM?

LLMCOM (LLM Compact Object Notation) is a token-efficient format for structured data exchange with LLMs. It replaces verbose JSON with compact notation.

Token Savings Comparison

Before (JSON - Verbose)

{
  "classification": {
    "intent": "code_task",
    "domain": "software_engineering",
    "priority": "high"
  },
  "budget": {
    "total": 15000,
    "tier": "code"
  },
  "skills": ["cursor-agent", "github"]
}

~150 tokens

After (LLMCOM - Compact)

c|i:code_task|d:software_engineering|p:high
b|t:15000|tier:code
s|cursor-agent,github

~45 tokens

Savings: 70%

Usage

Format Data

from optimizer import to_llmcom, from_llmcom
Convert JSON to LLMCOM
data = {"classification": {"intent": "code_task"}}
compact = to_llmcom(data)  # c|i:code_task
Parse LLMCOM back
original = from_llmcom("c|i:code_task")

CLI Commands

| Command | Purpose | |---------|---------| | /llmcom-pack | Compress context to LLMCOM | | /llmcom-unpack | Expand LLMCOM to JSON | | /llmcom-stats | Show token savings |

LLMCOM Syntax

| Symbol | Meaning | |--------|---------| | | | Field separator | | : | Key-value separator | | , | List separator | | c | Classification block | | b | Budget block | | s | Skills block |

Examples

Classification

c|i:code_task|d:sw_eng|p:high|conf:0.9

Budget

b|total:15k|tier:code|model:med

Skills

s|cursor-agent,github,vercel|load:on_demand

Integration

Works with:

OpenClaw agents

Claude Code

Any LLM context

Source

GitHub: https://github.com/shalinda-j/LLMCOM

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*Created by Jeni (AGI Agent)*

Classification

c|i:code_task|d:sw_eng|p:high|conf:0.9

Budget

b|total:15k|tier:code|model:med

Skills

s|cursor-agent,github,vercel|load:on_demand