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Acronym Unpacker

Name: Acronym Unpacker
Author: aipoch-ai

作者 AIpoch · GitHub ↗ · v0.1.0 · MIT-0

cross-platform ⚠ suspicious

221

总下载

当前安装

版本数

在 OpenClaw 中安装

/install acronym-unpacker

功能描述

Disambiguate medical acronyms and abbreviations with context-aware full form lookup. Resolves ambiguous abbreviations (e.g., 'PID' = Pelvic Inflammatory Dise...

使用说明 (SKILL.md)

Acronym Unpacker

Intelligent medical abbreviation disambiguation tool that resolves ambiguous acronyms using clinical context, specialty-specific knowledge, and document-level semantic analysis.

Features

Context-Aware Disambiguation: Uses clinical specialty to rank expansions
Semantic Analysis: Analyzes surrounding text for contextual clues
Frequency-Based Ranking: Prioritizes common usage patterns
Multi-Specialty Support: Covers medicine, nursing, pharmacy, and research
Batch Processing: Expand acronyms in entire documents
Learning System: Improves accuracy with usage feedback

Usage

Basic Usage

# Expand single acronym
python scripts/main.py PID

# Expand with context
python scripts/main.py MI --context cardiology

# List known acronyms
python scripts/main.py --list

Parameters

Parameter	Type	Default	Required	Description
`acronym`	str	None	Yes	Acronym to expand
`--context`, `-c`	str	general	No	Clinical context (e.g., cardiology, gynecology)
`--list`, `-l`	flag	False	No	List known acronyms

Advanced Usage

# Disambiguate with specific context
python scripts/main.py PID --context gynecology

# Check all available acronyms
python scripts/main.py --list

Supported Acronyms

Acronym	General	Cardiology	Gynecology	Immunology
PID	Pelvic Inflammatory Disease	-	Pelvic Inflammatory Disease (90%)	Primary Immunodeficiency (95%)
MI	Myocardial Infarction (70%)	Myocardial Infarction (95%)	-	-
COPD	Chronic Obstructive Pulmonary Disease	-	-	-
HTN	Hypertension	Hypertension	-	-
DM	Diabetes Mellitus (90%)	-	-	-

Output Example

============================================================
ACRONYM: PID
Context: gynecology
============================================================
1. Pelvic Inflammatory Disease
   Confidence: 90.0% ████████████████████
2. Prolapsed Intervertebral Disc
   Confidence: 10.0% ██
============================================================

Technical Difficulty: LOW

⚠️ AI自主验收状态: 需人工检查

This skill requires:

Python 3.7+ environment
No external dependencies

Dependencies

pip install -r requirements.txt

No external dependencies required.

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts executed locally	Low
Network Access	No network access	Low
File System Access	Read-only	Low
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	No sensitive data exposure	Low

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access
Output does not expose sensitive information
Prompt injection protections in place
Error messages sanitized
Dependencies audited

Prerequisites

python scripts/main.py --help

Evaluation Criteria

Success Metrics

Successfully expands known acronyms
Context-aware ranking works correctly
Confidence scores are meaningful
Handles unknown acronyms gracefully

Test Cases

Basic Expansion: Known acronym → Multiple expansions with confidence
Context Filtering: Context flag → Contextually appropriate results
Unknown Acronym: Unknown input → Graceful handling
List Mode: --list flag → Shows all known acronyms

Lifecycle Status

Current Stage: Draft
Next Review Date: 2026-03-15
Known Issues: Limited acronym database
Planned Improvements:
- Expand acronym database
- Add machine learning for context detection
- Support for multi-language acronyms

References

Available in references/:

Medical abbreviation standards
Clinical terminology sources
Context disambiguation methods

Limitations

Database Size: Limited to pre-configured acronyms
Context Detection: Requires manual context specification
Language: English acronyms only
Medical Focus: Optimized for medical terminology

💡 Tip: When in doubt about the context, try multiple contexts to see which expansion makes the most sense in your specific use case.

安全使用建议

This package is internally inconsistent: documentation promises ML-driven, document-level and batch capabilities but the code is only a static lookup for single acronyms. If you need true context-aware disambiguation, don't rely on this skill as-is. Before installing or using it: (1) review the source (it's short) and confirm behavior meets your needs; (2) ask the author for the missing requirements.txt, references, and an implementation plan for the advertised features; (3) run the script in an isolated environment when evaluating; (4) if you will process protected health information, prefer a vetted, auditable tool with explicit handling of PHI and robust provenance; and (5) consider rejecting or labeling the skill as 'documentation-mismatch' until the author fixes the claims or supplies the promised functionality.

功能分析

Type: OpenClaw Skill Name: acronym-unpacker Version: 0.1.0 The acronym-unpacker skill is a straightforward utility for disambiguating medical abbreviations using a hardcoded dictionary. The Python script (scripts/main.py) uses only the standard library, performs no network or file system operations, and contains no risky execution patterns. The documentation (SKILL.md) is consistent with the code's functionality and does not contain any prompt injection or malicious instructions.

能力评估

⚠ Purpose & Capability

SKILL.md describes semantic analysis, clinical-document-level context detection, batch processing, and a learning system. The included script is a ~200-line Python file with a hard-coded dictionary and only supports single-acronym lookup and a simple --list mode. Features advertised (ML, batch processing, expanding entire documents, learning) are not implemented and the README even references a non-existent requirements.txt and references/ directory.

ℹ Instruction Scope

Runtime instructions are simple: run python scripts/main.py with an acronym and optional --context. The code does not read arbitrary system files, call external services, or access environment variables, so runtime scope is limited — however the SKILL.md's claims about document-level analysis and batch processing are misleading relative to what the instructions actually perform.

ℹ Install Mechanism

No install spec is provided (instruction-only plus a local script), which minimizes install risk. The README suggests pip install -r requirements.txt but that file is not present; this mismatch is sloppy but not directly dangerous.

✓ Credentials

The skill declares no required environment variables, credentials, or config paths. The code does not attempt to access secrets or external services, so requested environment access is minimal and proportionate.

✓ Persistence & Privilege

always is false and the skill contains no installation hooks or self-modifying behavior. It does not request persistent or elevated privileges.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install acronym-unpacker
安装完成后，直接呼叫该 Skill 的名称或使用 /acronym-unpacker 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v0.1.0

Initial release of the Acronym Unpacker skill. - Context-aware tool for disambiguating medical acronyms and abbreviations. - Resolves ambiguous terms using clinical specialty, document context, and usage patterns. - Supports batch document processing and feedback-based learning. - Covers multiple specialties including medicine, nursing, pharmacy, and research. - CLI interface for expanding acronyms, listing known acronyms, and specifying context. - No external dependencies required; Python 3.7+ only.

元数据

Slug acronym-unpacker

版本 0.1.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题