🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks across all assessed categories with no signs of malicious behavior or supply-chain attack indicators.

No network calls detected.
No shell execution patterns observed.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

Test runner config found: conftest.py
4 test file(s) detected (e.g. __init__.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

34 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

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

Email domain looks legitimate: synnaxlabs.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 alamos

Imagine you're tasked with building a data analysis tool that helps researchers manage and analyze large datasets efficiently. This tool, named 'DataInsight', will utilize the 'alamos' Python package, which is designed to streamline data processing tasks but lacks an official description. Your goal is to create a fully functional mini-app that showcases the potential of 'alamos'. Here’s how you can approach this project step-by-step:

1. **Project Setup**: Start by setting up your development environment. Ensure you have Python installed, then install the 'alamos' package using pip. Since 'alamos' has no official documentation, you'll need to explore its functionality through its source code or any available examples.
2. **Core Functionality**: Design 'DataInsight' to load datasets from CSV files. Use 'alamos' to preprocess these datasets, including cleaning, normalization, and transformation steps. Ensure that 'alamos' is utilized for at least two different preprocessing tasks.
3. **Interactive Features**: Implement an interactive dashboard where users can select specific columns and apply various data transformations provided by 'alamos'. Users should be able to visualize the transformed data in real-time using libraries like Matplotlib or Seaborn.
4. **Advanced Analysis**: Integrate 'alamos' to perform advanced statistical analyses on the dataset. This could include hypothesis testing, regression analysis, or clustering algorithms. Document the methods used and their outputs clearly.
5. **User Interface**: Develop a user-friendly interface that allows users to upload their own datasets and choose from a range of predefined analyses. The UI should also display results in an easily understandable format.
6. **Documentation and Testing**: Write comprehensive documentation explaining each feature of 'DataInsight' and how 'alamos' is integrated into the application. Include unit tests to ensure the reliability of the data processing and analysis functions.
7. **Deployment**: Finally, prepare 'DataInsight' for deployment. Consider hosting it as a web application or a desktop application that users can download and run locally.

Suggested Features:
- A robust data import/export mechanism
- Real-time data visualization updates
- Customizable data transformation pipelines
- Detailed reporting and result exporting options
- Support for multiple file formats (CSV, Excel, etc.)

By completing this project, you will not only demonstrate your ability to work with unfamiliar packages but also showcase your skills in data analysis, software design, and user interface development.