AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks for obfuscation and credential harvesting, but the metadata risk is moderate due to the unavailability of the repository and the maintainer's limited package history.
- No obfuscation or credential harvesting patterns detected
- Repository not found and maintainer has only one package
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
- Metadata: The repository is not found and the maintainer has only one package, which may indicate a new or less active account.
Package Quality Overall: Low (3.2/10)
○ 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 (981 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
7 type-annotated function signatures (partial)
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
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
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Craig Horton" 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 aibvf
Create a mini-application named 'AIInvestmentScorer' that leverages the 'aibvf' package to score potential AI investment opportunities based on the AI BVF protocol. This tool will assist investors in evaluating the potential success of AI startups and projects. Here’s a detailed plan for building this application: 1. **Project Setup**: Start by setting up a new Python environment and installing the 'aibvf' package. Ensure all necessary dependencies are installed. 2. **Data Input Module**: Develop a module where users can input data about an AI startup or project they are considering investing in. This data might include metrics like team expertise, technology maturity, market demand, and financial health. 3. **Scoring Engine Integration**: Use the 'aibvf' package to integrate the scoring engine into your application. This involves configuring the engine with the appropriate parameters as defined by the AI BVF protocol. 4. **Validator Functionality**: Implement validation checks using the 'aibvf' validator to ensure the input data meets the criteria set forth by the AI BVF protocol. This ensures accurate scoring. 5. **Scoring Output**: After validating the input data, use the scoring engine to generate a comprehensive score for the investment opportunity. This score should provide insights into the potential success of the AI project. 6. **User Interface**: Design a simple yet effective user interface that allows users to easily input data and view their scores. Consider integrating this with a web framework like Flask or Django for a more interactive experience. 7. **Report Generation**: Enhance the application by adding functionality to generate detailed reports based on the scoring results. These reports should offer actionable insights and recommendations for potential investors. 8. **Testing and Documentation**: Thoroughly test the application to ensure it functions correctly and accurately reflects the principles of the AI BVF protocol. Document all aspects of the application, including setup instructions, usage guidelines, and API documentation if applicable. By following these steps, you'll create a valuable tool for anyone looking to make informed decisions when investing in AI-related ventures.