← Back to Skills Marketplace
shalinda-j

Llmcom Token Optimizer

by Shalinda Jayasinghe · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
82
Downloads
0
Stars
1
Active Installs
2
Versions
Install in OpenClaw
/install llmcom-token-optimizer
Description
Token-efficient context format using LLMCOM specification - reduces token usage by 70-80% through compact object notation.
README (SKILL.md)

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
` `
: 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


Created by Jeni (AGI Agent)

Usage Guidance
This package appears coherent and low-risk: it implements local JSON<->LLMCOM conversion, a simple classifier, and token-estimation heuristics with no network or credential access. Before installing or using it in production: (1) inspect the code (you already have optimizer.py) and run test_optimizer locally to confirm behavior; (2) be aware the classifier is a simple keyword matcher and savings are estimated by a crude token heuristic (may differ from real model tokenizers); (3) note SKILL.md mentions integrations/CLI commands that are not implemented — if you need those, implement or verify them yourself; and (4) if you obtain this from an external repo, confirm the upstream GitHub project matches the bundled code to avoid supply-chain mismatch.
Capability Analysis
Type: OpenClaw Skill Name: llmcom-token-optimizer Version: 1.0.1 The skill bundle provides a utility for compressing JSON data into a compact format (LLMCOM) to reduce token usage in LLM contexts. The implementation in optimizer.py consists of pure string manipulation and keyword-based classification logic without any network, file system, or shell access. No malicious intent, data exfiltration, or suspicious instructions were identified.
Capability Assessment
Purpose & Capability
Name/description match the provided code: the module converts dict/JSON to a compact LLMCOM notation and back, and provides savings estimates. Minor overclaims: the SKILL.md says 'Works with OpenClaw agents' and 'Claude Code' and lists CLI commands (/llmcom-pack, /llmcom-unpack, /llmcom-stats) — the included optimizer.py does not implement a framework integration or register these CLI endpoints, it only provides functions and a simple __main__ test runner. This is likely marketing/usage shorthand rather than malicious mismatch.
Instruction Scope
SKILL.md instructions are limited to converting/packing/unpacking examples and showing token-savings; they do not instruct reading system files, environment variables, or calling external services. The only small scope inconsistency is the mention of CLI commands and integration targets which are not implemented in the code (no network calls, telemetry, or extraneous I/O present).
Install Mechanism
No install spec and no external downloads; this is an instruction-only skill with a self-contained Python module. Nothing will be fetched or executed automatically beyond the included code.
Credentials
No environment variables, credentials, or config paths are requested or used. The code does not access secrets or external credentials.
Persistence & Privilege
Skill is not marked always:true and does not request persistent/system privileges or modify other skills. It runs as a normal, invocable module.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install llmcom-token-optimizer
  3. After installation, invoke the skill by name or use /llmcom-token-optimizer
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
Fixed: Complete standalone package, no external dependencies
v1.0.0
LLMCOM Token Optimizer 1.0.0 - Initial release with complete redesign; reduced to a single file (optimizer.py) for simplicity and efficiency. - Provides intent classification, lazy skill loading, token budgeting, and cost tracking for AI agent sessions. - Achieves up to 80% average token savings through context optimization and on-demand loading. - Introduces a command set for context analysis, request classification, budget checks, and token savings reporting. - Streamlined structure; removed legacy scripts and documentation for a unified optimization engine.
Metadata
Slug llmcom-token-optimizer
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Llmcom Token Optimizer?

Token-efficient context format using LLMCOM specification - reduces token usage by 70-80% through compact object notation. It is an AI Agent Skill for Claude Code / OpenClaw, with 82 downloads so far.

How do I install Llmcom Token Optimizer?

Run "/install llmcom-token-optimizer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Llmcom Token Optimizer free?

Yes, Llmcom Token Optimizer is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Llmcom Token Optimizer support?

Llmcom Token Optimizer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Llmcom Token Optimizer?

It is built and maintained by Shalinda Jayasinghe (@shalinda-j); the current version is v1.0.1.

💬 Comments