🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across multiple dimensions including network, shell, obfuscation, and credential risks. While there are some concerns regarding metadata quality and maintainer activity, these do not indicate malicious intent.

Low risk scores across all assessed categories.
Metadata quality and maintainer activity could be improved.

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 external commands that could pose a risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 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 (1340 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

Email domain looks legitimate: amesa.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 amesa-cli-dev

Create a fully-functional command-line utility called 'AmesApp' using the 'amesa-cli-dev' package. AmesApp will serve as a versatile tool for managing and interacting with various datasets, particularly those related to economic data analysis. Your task is to design and implement a user-friendly interface that leverages the core functionalities of 'amesa-cli-dev'. Here’s a step-by-step guide on how to proceed:

1. **Setup**: Begin by installing the 'amesa-cli-dev' package in your Python environment. Ensure you have Python 3.8 or higher installed.
2. **Initialization**: Create a main function that initializes AmesApp. This function should display a welcome message and present the user with a list of available commands.
3. **Commands Implementation**:
   - **Load Data**: Implement a command that allows users to load datasets into AmesApp. Utilize 'amesa-cli-dev' to streamline the process of loading data from CSV files or other supported formats.
   - **Data Analysis**: Develop commands that perform basic statistical analyses on loaded datasets. Use 'amesa-cli-dev' to enhance these operations, ensuring they are efficient and user-friendly.
   - **Visualization**: Integrate a feature that visualizes dataset trends and patterns. Use 'amesa-cli-dev' to generate interactive plots and charts directly from the command line.
   - **Save Results**: Add functionality to save analysis results and visualizations. Ensure users can specify file formats and locations for saving their work.
4. **User Interface**: Design a clean and intuitive command-line interface. Each command should be easy to remember and use, with clear prompts guiding the user through each step.
5. **Testing**: Thoroughly test all implemented features to ensure AmesApp functions correctly under various scenarios. Pay special attention to error handling and user feedback mechanisms.
6. **Documentation**: Write comprehensive documentation for AmesApp, detailing how to install, configure, and use the tool effectively. Include examples and best practices for common tasks.

Suggested Features:
- Support for real-time data streaming.
- Integration with external data sources like APIs.
- Advanced filtering options for datasets.
- Customizable visualization themes and styles.
- Exporting data and visualizations to multiple formats (CSV, JSON, PNG).

Your goal is to create a robust, flexible, and user-friendly tool that showcases the capabilities of 'amesa-cli-dev' in practical applications.