🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with low risks across all categories except metadata, where the maintainer's single package suggests they might be new or less active.

No network calls detected.
No shell execution patterns detected.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution patterns detected, indicating the package does not execute external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account.

🔬 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AndiEcker" 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 aedev-project-tpls

Create a Python-based utility called 'ProjectBootstrapper' that simplifies the process of setting up new Python projects by leveraging the 'aedev-project-tpls' package. This tool will allow developers to quickly generate project skeletons tailored to different types of Python applications (e.g., web apps, data analysis projects, machine learning projects). The application should be able to handle the following tasks:

1. **Project Type Selection**: Users should be able to choose from a predefined set of project types (web app, data science, machine learning, etc.) when initiating a new project.
2. **Template Customization**: Allow users to customize their project templates by adding or removing specific files and directories based on their needs.
3. **Configuration Setup**: Automatically configure necessary settings such as virtual environment setup, dependency management (using pipenv or poetry), and basic project documentation.
4. **Code Generation**: Generate initial code files (e.g., main.py, models.py, views.py) according to the selected project type.
5. **Interactive Mode**: Provide an interactive command-line interface where users can answer questions about their project requirements and preferences, which then guides the template selection and customization process.
6. **Integration with Version Control**: Automatically initialize a Git repository for the newly created project and provide instructions for setting up remote repositories.

To achieve these functionalities, you will extensively utilize the 'aedev-project-tpls' package to manage and apply the appropriate project templates. This includes handling file structure generation, default file content creation, and ensuring consistency across different project types. Additionally, document your implementation process and provide examples on how other developers can extend or modify the available project templates using 'aedev-project-tpls'.