🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to potential insecure network communication practices, but shows no signs of malicious intent or direct exploitation risks such as shell execution or credential harvesting.

Moderate network risk due to unverified HTTPS requests
No evidence of obfuscation, shell execution, or credential harvesting

Per-check LLM notes

Network: The use of unverified HTTPS requests may indicate insecure network communication practices, which could expose the system to man-in-the-middle attacks.
Shell: No shell execution patterns detected, suggesting low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Brief PyPI description (252 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

132 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

secureRequestWarning) r = requests.get(href, headers=headers, stream=True, verify=False) # NOSONAR
secureRequestWarning) r = requests.head(href, headers=headers, verify=False) # NOSONAR return r

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Gisaïa" 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 aias-common

Create a Python-based mini-application that leverages the ARLAS AIAS common library (aias-common) to enhance data analysis and visualization capabilities. This application will serve as a tool for users to input various datasets, perform basic statistical analyses, and visualize the results using interactive charts and graphs. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have the latest version of Python installed. Then, install the aias-common package along with other necessary packages such as pandas for data manipulation, matplotlib and seaborn for plotting graphs.
2. **Data Input Interface**: Design a user-friendly interface where users can upload their CSV files or enter data directly into the application. This interface should allow users to select specific columns for analysis.
3. **Data Processing**: Utilize functions from the aias-common package to preprocess the data. This might include handling missing values, normalizing data, and applying transformations. Use pandas for more advanced data manipulation tasks.
4. **Statistical Analysis**: Implement basic statistical analysis functionalities such as calculating mean, median, mode, standard deviation, etc., using both built-in Python libraries and functions from aias-common. The goal is to provide insights into the dataset's central tendencies and dispersion.
5. **Visualization**: Develop visualizations that reflect the analyzed data. Use matplotlib and seaborn for plotting histograms, scatter plots, box plots, and other relevant types of graphs. Integrate these visualizations with the output of the statistical analysis to provide a comprehensive view of the data.
6. **Interactive Features**: Enhance the application by adding interactive elements like sliders to adjust parameters in real-time, dropdown menus to choose different datasets, and buttons to trigger different types of analysis or visualization.
7. **Documentation & Testing**: Document all parts of your code thoroughly. Include comments explaining why certain choices were made and how different parts of the application work together. Test your application extensively to ensure it handles various edge cases gracefully.
8. **Deployment**: Once the application is fully functional and tested, consider deploying it online so others can access it easily. Services like Heroku or AWS can be used for deployment.

By following these steps, you’ll create a versatile tool that not only showcases the capabilities of the aias-common package but also provides practical value to anyone interested in analyzing and visualizing data.