An intelligent clinical utility that disambiguates medical acronyms using specialty-specific context and semantic analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install acronym-unpacker
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 acronym-unpacker 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 Acronym Unpacker?
The Acronym Unpacker is a specialized tool designed to solve the inherent ambiguity found in clinical documentation and medical research. By integrating this utility into your Openclaw Skills workflow, you can ensure that abbreviations like PID are correctly expanded based on the clinical department—identifying it as Pelvic Inflammatory Disease in gynecology or Primary Immunodeficiency in immunology. This precision is vital for maintaining data integrity in healthcare applications.
This skill goes beyond simple lookups by applying frequency-based ranking and semantic clues from surrounding text. As part of the Openclaw Skills ecosystem, it provides a developer-friendly way to process medical terminology with high confidence scores, making it an essential asset for any clinical data pipeline or medical AI agent.
Acronym Unpacker Use Cases
- Standardizing clinical notes by expanding abbreviations into their full forms based on department context.
- Automating the cleaning of Electronic Health Record (EHR) datasets for medical research and data analytics.
- Assisting medical transcribers and coders by providing ranked suggestions for ambiguous medical terms.
- Enhancing searchability in medical databases by indexing the full-form versions of common clinical acronyms.
How Acronym Unpacker Works
- The user provides a medical acronym and an optional clinical context flag (e.g., cardiology, gynecology).
- The system analyzes the input and searches its specialty-specific knowledge base for matching expansion candidates.
- Semantic analysis is performed on any provided surrounding text to identify contextual clues that might favor one expansion over another.
- Potential expansions are ranked using a combination of frequency-based usage patterns and context-specific weights.
- The tool returns a structured output with confidence scores for each expansion, allowing the user to make an informed decision or automate the selection.
Acronym Unpacker Setup
To get started with this skill in your Openclaw Skills environment, ensure you have Python 3.7+ installed and follow these steps:
# Install the necessary requirements
pip install -r requirements.txt
# Run a test expansion to verify setup
python scripts/main.py PID --context gynecology
# View the help documentation for all available parameters
python scripts/main.py --help
Acronym Unpacker Data Schema & Taxonomy
The skill utilizes a structured mapping of acronyms across multiple clinical specialties. The data is organized as follows:
| Acronym | Clinical Specialty | Full Form Expansion | Confidence |
|---|---|---|---|
| PID | Gynecology | Pelvic Inflammatory Disease | 90% |
| PID | Immunology | Primary Immunodeficiency | 95% |
| MI | Cardiology | Myocardial Infarction | 95% |
| DM | General Medicine | Diabetes Mellitus | 90% |
Users can also view the full database of supported terms by running the --list command.
Acronym Unpacker Advanced Features
- Context-Aware Disambiguation: Dynamically ranks expansion results based on the provided clinical specialty to ensure medical accuracy.
- Multi-Specialty Support: Includes deep knowledge across medicine, nursing, pharmacy, cardiology, and immunology.
- Batch Processing: Capable of analyzing and expanding multiple acronyms within entire documents for high-volume workflows.
- Learning Feedback Loop: Designed to improve accuracy over time by incorporating usage patterns and specialty-specific feedback.
- Semantic Analysis: Evaluates surrounding text to provide an extra layer of intelligence when context flags are missing.
SKILL.md
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