A high-performance context compression tool that replaces verbose JSON with LLM Compact Object Notation to achieve 70-80% token savings.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install llmcom-token-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install llmcom-token-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is LLMCOM Token Optimizer?
LLMCOM Token Optimizer is a specialized utility designed for developers and AI orchestrators who need to manage large contexts without skyrocketing costs. By utilizing the LLM Compact Object Notation (LLMCOM) specification, this skill transforms traditional, wordy JSON structures into ultra-compact strings. This approach is particularly effective for high-frequency agent communications where every token saved directly translates to lower latency and reduced API expenses.
Integrating this into your Openclaw Skills workflow allows your AI agents to maintain more history, handle more complex instructions, and operate within tighter budget constraints. It bridges the gap between human-readable configuration and machine-efficient data exchange, making it an essential tool for production-grade AI deployments.
LLMCOM Token Optimizer Use Cases
- Reducing operational costs for high-volume AI agent workflows by compressing repetitive metadata.
- Maximizing context window availability for long-running sessions or complex coding tasks.
- Streamlining inter-agent communication by passing compact tokens instead of full JSON payloads.
- Monitoring and auditing token efficiency across different LLM provider tiers.
How LLMCOM Token Optimizer Works
- The user or agent provides a standard JSON object representing classification, budget, or skill data.
- The to_llmcom function parses the structured data and maps it to the LLMCOM syntax using specific block identifiers (c, b, s).
- Verbose keys and formatting characters are replaced with pipes (|), colons (:), and commas (,) to create a dense string.
- The compact string is injected into the LLM context, significantly reducing the total token count.
- When necessary, the from_llmcom utility expands the compact notation back into valid JSON for programmatic use by the agent or system.
LLMCOM Token Optimizer Setup
Install the LLMCOM Token Optimizer directly into your environment using the following commands:
# Install the optimizer package
pip install llmcom-optimizer
# Enable the Openclaw Skills integration
openclaw install llmcom-token-optimizer
LLMCOM Token Optimizer Data Schema & Taxonomy
The skill utilizes a compact syntax to organize context into three primary functional blocks:
| Block Identifier | Block Name | Description |
|---|---|---|
c |
Classification | Defines intent, domain, priority, and confidence levels. |
b |
Budget | Manages token limits, tiering, and model specifications. |
s |
Skills | Lists available agent capabilities and loading strategies. |
Example Schema Mapping:
- JSON:
{"classification": {"intent": "code_task"}} - LLMCOM:
c|i:code_task
LLMCOM Token Optimizer Advanced Features
- Bi-directional transformation between JSON and LLMCOM for seamless API integration.
- Real-time token savings analytics via the
/llmcom-statscommand. - Native support for Openclaw Skills, Claude Code, and multi-agent orchestration frameworks.
- Customizable block identifiers for specialized developer workflows.
SKILL.md
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