🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package does not exhibit typical signs of malicious activity such as network calls or shell execution, the low maintainer activity and poor metadata quality raise concerns about its legitimacy and potential for supply-chain attacks.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package is expected to communicate with external services.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns about its legitimacy and security.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (5118 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8051/api/v1

✓ 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 algomancy-api

Your task is to develop a fully-functional mini-application using the 'algomancy-api' Python package. This package provides a FastAPI HTTP interface for managing scenarios and data, which you will utilize to create a user-friendly tool for data scientists and analysts. Your application should allow users to perform the following operations:

1. **Scenario Management**: Users should be able to create, retrieve, update, and delete (CRUD) scenarios.
2. **Data Management**: Enable users to upload datasets, manage them through CRUD operations, and associate these datasets with specific scenarios.
3. **Scenario Execution**: Implement functionality for executing scenarios with associated data, providing real-time feedback on execution status and results.
4. **User Authentication**: Integrate basic authentication to ensure only authorized users can access and modify scenarios and data.
5. **Logging and Monitoring**: Add logging capabilities to track user actions and scenario executions, enhancing traceability and debugging.

### Detailed Steps:

- **Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary dependencies including 'algomancy-api'. Initialize a new FastAPI project and configure it to use 'algomancy-api'.
- **Authentication**: Implement user authentication using FastAPI's built-in support for OAuth2. Create endpoints for user registration, login, and logout functionalities.
- **Scenario Management**: Develop endpoints to manage scenarios. These should include creating new scenarios, retrieving details of existing ones, updating scenario configurations, and deleting scenarios.
- **Data Management**: Design endpoints for handling dataset uploads, retrieval, updates, and deletions. Each dataset should be linked to one or more scenarios.
- **Execution Endpoint**: Create an endpoint for executing scenarios with their associated data. This endpoint should handle the execution process, monitor its progress, and provide feedback to the user.
- **Logging**: Incorporate logging mechanisms to record all significant actions taken within the application, such as scenario creation, data uploads, and execution attempts.
- **Testing and Deployment**: Thoroughly test your application using various test cases to ensure all functionalities work as expected. Once tested, deploy your application to a cloud platform like Heroku or AWS.

### Additional Features:

Consider adding features like:
- A simple frontend UI using HTML/CSS/JavaScript for better user interaction.
- Support for multiple users with role-based access control.
- Integration with popular data visualization tools to display execution results graphically.

By completing this project, you'll gain hands-on experience with FastAPI, 'algomancy-api', and other related technologies, while also building a useful tool for managing data science workflows.