🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity or unusual behavior. Its primary function appears to be aggregation, which does not typically involve network calls, shell executions, or obfuscation.

No network calls
No shell execution
No obfuscation
No credential harvesting
Single package from author

Per-check LLM notes

Network: No network calls detected, which is normal for a library focused on aggregations without external dependencies.
Shell: No shell execution detected, which aligns with the expected behavior for a library performing aggregations.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package, indicating a potentially new or less active account.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_aggregations.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6023 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

19 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 34 commits in wgalka/Means
Two distinct contributors found

🔬 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 wgalka/Means appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "wgalka" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aggregationslib

Create a financial calculator application using Python that leverages the 'aggregationslib' package to perform various types of aggregation calculations. This application will help users understand different methods of calculating averages and aggregates, which are crucial in financial analysis and decision-making processes. Here’s how you can structure the project:

1. **Introduction**: Briefly introduce the purpose of the application and explain why understanding different aggregation methods is important in finance.
2. **Setup**: Set up your development environment by installing Python and the necessary packages, including 'aggregationslib'.
3. **Application Structure**: Define the main components of your application. Consider having a user interface where users can input data and select the type of aggregation they want to perform.
4. **Core Functionality**:
   - Implement functions to calculate arithmetic mean, geometric mean, harmonic mean, and other aggregations provided by 'aggregationslib'. Each function should take a list of numbers as input and return the calculated value.
   - Use 'aggregationslib' to implement these functions efficiently.
5. **User Interface**:
   - Design a simple command-line interface where users can input their dataset and choose from a menu of aggregation options.
   - Alternatively, create a basic web interface using Flask or Django if you are familiar with web frameworks.
6. **Features**:
   - Allow users to save and load datasets from files.
   - Provide visual representations of the data and results using matplotlib or a similar library.
   - Include explanations of each aggregation method used in the application.
7. **Testing**: Write tests for each of your aggregation functions to ensure they work correctly.
8. **Documentation**: Create a README file explaining how to install and use the application, along with any additional notes on the implementation and usage of 'aggregationslib'.
9. **Deployment**: If applicable, deploy the application to a platform like Heroku or GitHub Pages so others can access it online.

By completing this project, you will gain practical experience in using 'aggregationslib' to perform complex calculations and develop a useful tool for financial analysis.