babelbetes

v0.3.1 safe
3.0
Low Risk

A Data Processing Tool to Standardize Publicly Available Clinical Diabetes Trial Data

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to concerns over maintainer history, but there are no clear signs of malicious intent or supply-chain attacks.

  • No network calls or shell executions detected
  • Low risk of obfuscation and credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: Low risk due to lack of suspicious elements, but concerns about maintainer history suggest potential low effort or inactive status.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: conftest.py
  • 10 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11184 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 56 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in nudgebg/babelbetes
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository nudgebg/babelbetes appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with babelbetes 
Your task is to create a fully functional mini-application that leverages the 'babelbetes' package to standardize and analyze publicly available clinical diabetes trial data. This application will serve as a valuable tool for researchers and healthcare professionals by providing insights into various aspects of diabetes trials. Here are the steps and features you need to include:

1. **Setup and Installation**: Begin by installing the 'babelbetes' package and any other necessary Python libraries such as pandas and numpy. Ensure your environment is set up correctly for data processing.

2. **Data Collection**: Write a function that collects diabetes trial data from public sources such as PubMed, ClinicalTrials.gov, or other relevant databases. Use 'babelbetes' to handle the initial parsing and formatting of the raw data.

3. **Data Standardization**: Implement a feature that standardizes the collected data using 'babelbetes'. This includes converting units, normalizing patient demographics, and ensuring consistency across different datasets.

4. **Data Analysis**: Develop an analysis module that performs statistical analyses on the standardized data. Include features like calculating mean blood glucose levels, assessing the efficacy of different treatments, and comparing outcomes across various demographic groups.

5. **Visualization**: Create visual representations of the analyzed data using matplotlib or seaborn. Visualizations should include graphs showing trends over time, comparisons between different treatment methods, and demographic breakdowns.

6. **User Interface**: Build a simple command-line interface (CLI) that allows users to interact with the application. Users should be able to specify which datasets to process, view analysis results, and generate reports.

7. **Documentation and Testing**: Provide comprehensive documentation explaining how to use the application and its functionalities. Also, ensure robust testing of all components to guarantee reliability.

By completing these steps, you'll have developed a powerful yet user-friendly tool for analyzing diabetes clinical trial data, demonstrating the versatility and utility of the 'babelbetes' package.

💬 Discussion Feed

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