A sophisticated semantic compression tool designed to drastically reduce token consumption while maintaining 100% information accuracy.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install semantic-compress
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 semantic-compress 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 Semantic Compress?
Semantic Compress is a high-performance utility within the Openclaw Skills ecosystem designed to optimize how AI agents handle large volumes of text. Unlike traditional summarization which might lose specific details, this skill focuses on removing linguistic redundancy—such as conversational filler, repetitive pleasantries, and unnecessary transitionary phrases—while keeping the core semantic skeleton intact. This ensures that your AI models receive the most concentrated form of information possible, saving costs and extending the effective context window.
By leveraging advanced LLM-based distillation, Semantic Compress enables developers to maintain complex, long-running conversations and process massive documents without hitting token limits or sacrificing the quality of the agent's reasoning. It is an essential component for any developer looking to build professional-grade AI applications using Openclaw Skills.
Semantic Compress Use Cases
- Compressing long-form conversation histories to maintain context in multi-turn dialogues.
- Distilling lengthy technical documents or articles while preserving every critical data point and conclusion.
- Cleaning up redundant context after multiple rounds of interaction to minimize prompt overhead.
- Converting historical chat data into high-density long-term memory storage.
- Batch processing text datasets to reduce costs associated with large-scale API calls.
How Semantic Compress Works
- The text is passed to the Semantic Compress module where it is analyzed for key arguments, data, and logical structures.
- The skill identifies redundant elements such as repetitive confirmations, decorative adjectives, and process-oriented 'fluff'.
- It applies a layered compression strategy, preserving recent interactions in full while distilling older history into core facts.
- A specialized prompt ensures the output remains logically coherent and fully understandable for subsequent LLM processing.
- Optionally, a verification step can be triggered to ensure the compressed text retains the ability to answer questions as accurately as the original source.
Semantic Compress Setup
To integrate this capability into your workflow, you can use the provided scripts within the Openclaw Skills directory.
For CLI usage:
node scripts/compress.js input.txt [output.txt]
# Or using the Python implementation
python scripts/compress.py input.txt
For API integration:
const { semanticCompress } = require('./index.js');
const result = semanticCompress(text, {
targetCompression: 0.5,
preserveAccuracy: true
});
console.log(result.compressed);
Semantic Compress Data Schema & Taxonomy
The skill returns a structured object detailing the efficiency of the compression process:
| Property | Type | Description |
|---|---|---|
compressed |
String | The high-density version of the input text. |
originalLength |
Number | The character or token count of the source material. |
compressedLength |
Number | The size of the text after processing via Openclaw Skills. |
ratio |
Percentage | The calculated efficiency gain (usually 30% to 50% of original size). |
logicPreserved |
Boolean | Confirmation that the semantic structure remains intact. |
Semantic Compress Advanced Features
- Layered Context Retention: Automatically maintains the last 3-5 rounds of a conversation in full while compressing the distant past.
- Custom Compression Targets: Developers can define target ratios to balance between extreme brevity and detailed context.
- Multi-Agent Compatibility: Designed to work seamlessly as a pre-processor for other Openclaw Skills and agents.
- Accuracy Verification: An optional hook that uses a secondary LLM pass to verify that no critical information was lost during the compression phase.
SKILL.md
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