An intelligent routing and 4D vector compression tool designed to optimize token usage by 60-80% while preserving 97% semantic meaning.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install uptef-4d-compression-smart-router
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install uptef-4d-compression-smart-router using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
UPTEF 4D Compression + Smart Router is a high-performance utility designed for developers looking to maximize the efficiency of their LLM interactions. As a core component of the Openclaw Skills ecosystem, it utilizes an advanced four-dimensional vector mapping system—Space, Energy, Information, and Time—to condense input text without losing critical context. By implementing a rule-based Smart Router, the skill automatically identifies text types (dialogue, data, or theory) and applies the most suitable compression version (A, B, or C) to ensure optimal results.
This skill is particularly valuable for managing large-scale AI workflows where context window limits and token costs are significant constraints. Unlike simple summarizers, this tool leverages the UPTEF framework to perform S-E-I-T classification and redundancy purging, resulting in high-density outputs that remain 96-98% semantically accurate. It provides a professional-grade solution for anyone looking to integrate more intelligence into their local agent workflows via Openclaw Skills.
To get started with this skill, ensure you have the necessary system dependencies installed before using the CLI.
# Requirement: jq and awk must be available in your environment
# Install the skill via ClAWHub
clawhub install uptef-4d-compression-smart-router
# Basic usage for intelligent routing
/compress [your_text_or_file_path]
# Fast 4D compression shortcut
/4d [text]
The skill organizes its output into a structured format to ensure clarity for both humans and AI agents. It utilizes the following taxonomy:
| Dimension | Description | Metadata Captured |
|---|---|---|
| Space (S) | Content Boundaries | Structural key points and scope |
| Energy (E) | Value Weighting | Semantic priority scores (1.0 to 3.0) |
| Information (I) | Core Vectors | Central themes and essential data points |
| Time (T) | Sequential Flow | Chronological or logical step progression |
For document processing, it generates a directory structure containing the original text, a compressed 4D version, and a machine-readable JSON vector file.
--batch flag to process multiple historical dialogues or files simultaneously, providing a total cost-saving report.Loading
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