A high-integrity semantic compression system for AI agents that reduces token count while ensuring information preservation through iterative validation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 Compress?
The Compress skill is a sophisticated semantic reduction engine designed for Openclaw Skills environments. Unlike traditional bit-perfect lossless algorithms, this tool focuses on semantic integrity, allowing AI agents to shrink large datasets or prompts while maintaining core meaning and critical facts. It is specifically engineered to optimize long-term memory and context window efficiency in complex agentic workflows.
By utilizing a multi-level compression strategy ranging from L1 to L4, users can balance token savings against reconstruction reliability. The system is built on a foundation of rigorous validation, ensuring that any compressed output can be accurately reconstructed before it is finalized for production use.
Compress Use Cases
- Optimizing system prompts to reduce recurring token costs in high-frequency Openclaw Skills deployments.
- Compressing large historical data logs for efficient storage in long-term AI memory systems.
- Summarizing technical documents where specific facts like dates and figures must remain unchanged.
- Experimental research into ultra-low token representations for advanced model testing and prompt engineering.
How Compress Works
- Anchor Extraction: The skill identifies critical entities, numbers, and dates to create a baseline anchor checksum.
- Semantic Compression: The original text is processed and compressed based on the selected level (L1 to L4).
- Reconstruction: A blind reconstruction is performed where the skill attempts to recreate the original information from the compressed version.
- Validation Check: The system compares reconstructed anchors and semantic meaning against the original data.
- Refinement: If discrepancies exist, the system iterates up to three times to incorporate missing information into the compressed string.
- Final Verification: Once convergence is achieved, the validated compressed string is returned with confidence metrics.
Compress Setup
To integrate this compression logic into your Openclaw Skills workspace, follow these standard initialization steps.
# Add the compress skill to your agent configuration
openclaw install skill-compress
# Set your preferred default compression level
export COMPRESSION_LEVEL=L2
Ensure that your environment has sufficient API credits, as the validation loop typically requires 3-4 LLM calls to guarantee semantic accuracy.
Compress Data Schema & Taxonomy
The skill tracks compression metrics and data integrity through a structured metadata schema:
| Field | Description |
|---|---|
original_tokens |
The token count of the raw input text. |
compressed_tokens |
The final token count after semantic reduction. |
anchors |
Array of critical entities (names, dates, currency) used for verification. |
iteration_count |
Number of validation loops performed (Max 3). |
reliability_level |
The chosen tier (L1-L4) reflecting the compression aggressiveness. |
match_rate |
The percentage of anchors successfully reproduced during reconstruction. |
Compress Advanced Features
- Anchor Checksum System: Automated extraction and verification of high-priority data points to prevent hallucination during reconstruction.
- Iterative Validation Loop: A self-correcting mechanism that refines output based on reconstruction failures to ensure production-ready quality.
- Multi-Level Tiers: Support for varying compression ratios, from L1 (0.8x) for high reliability to L4 (0.15x) for experimental research.
- Break-Even Analysis: Built-in logic to determine if compression is cost-effective based on expected retrieval frequency within Openclaw Skills.
- Independent Verification Support: Capability to utilize a different LLM model for the reconstruction phase to eliminate model-specific bias.
SKILL.md
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