🤖 AI Analysis

Final verdict: SAFE

The package has low risks across various checks including network, shell, and obfuscation risks. While there are some concerns about metadata quality and maintainer activity, these alone do not indicate a supply-chain attack.

Low network and shell execution risks
No signs of obfuscation or credential harvesting

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 no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns but not conclusive evidence of malice.

🔬 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: protonmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 acquisition-namespace

Create a mini-application called 'DataPipelineExplorer' that leverages the 'acquisition-namespace' Python package to manage and visualize hierarchical paths in acquisition data pipelines. This tool will be designed to help users easily navigate through complex nested directory structures commonly found in scientific data acquisition processes. Here’s a step-by-step guide on what your application should achieve:

1. **Setup and Initialization**: Start by setting up a virtual environment and installing the necessary packages including 'acquisition-namespace'. Ensure that you include documentation on how to install and set up the virtual environment.
2. **Hierarchical Path Creation**: Utilize the 'acquisition-namespace' package to create a hierarchical structure of directories based on user-defined YAML files. These YAML files will specify the structure of the data pipeline, including directories and subdirectories.
3. **Interactive Directory Navigation**: Develop an interactive command-line interface that allows users to navigate through the created hierarchical directory structure. Users should be able to move between parent and child directories, view contents of each directory, and receive feedback about their current location within the hierarchy.
4. **Directory Modification Tools**: Implement tools within the application that allow users to modify the existing directory structure. This includes adding new directories, renaming existing ones, and deleting unnecessary directories.
5. **Visualization Feature**: Integrate a feature that generates a visual representation of the hierarchical structure. This could be in the form of a tree diagram or any other suitable visualization method that clearly shows the relationship between different levels of the hierarchy.
6. **Export Functionality**: Add functionality to export the current state of the hierarchical structure back into a YAML file, allowing users to save their modifications and use them as input for future sessions.
7. **Error Handling and Documentation**: Ensure robust error handling throughout the application to manage issues such as invalid inputs or missing directories. Additionally, provide comprehensive documentation on how to use the application, including examples of YAML files and typical usage scenarios.

By following these steps, you'll create a powerful yet user-friendly tool that simplifies the management and exploration of complex data acquisition pipelines.