🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to low repository activity and a single package from the author, raising concerns about potential supply-chain attacks.

Low repository activity
Single package from author

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function properly.
Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository has low activity and the author has a single package, which may indicate a new or less active maintainer.

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Kathleen Kiker" 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 adam-assist

Create a Python-based desktop application named 'AssistMate' that leverages the capabilities of the 'adam-assist' package to facilitate data analysis and visualization tasks. This application will serve as a powerful tool for researchers and analysts who need to process complex datasets efficiently. The application should have a user-friendly interface built with Tkinter, allowing users to upload their dataset files directly from their local machine. Once uploaded, the application should utilize the 'adam-assist' package to perform advanced data processing tasks such as normalization, feature extraction, and outlier detection.

The core functionality of the application includes:
1. Data Import: Users should be able to import various types of data files (CSV, Excel, SQL databases).
2. Data Processing: Implement functions within the 'adam-assist' package to preprocess the imported data, including cleaning, normalization, and transformation operations.
3. Visualization: Provide visual representations of the processed data through graphs and charts using libraries like Matplotlib or Seaborn.
4. Reporting: Allow users to generate reports summarizing the findings from their data analysis.
5. Export: Enable users to export the processed data and reports in different formats (PDF, Excel, CSV).

Additionally, consider incorporating the following advanced features:
- Real-time data streaming support for continuous data analysis.
- Integration with cloud storage services for seamless data backup and retrieval.
- Customizable dashboards where users can select specific data processing pipelines based on their needs.
- Machine learning model training and evaluation capabilities using pre-built models within the 'adam-assist' package.

The application should demonstrate proficiency in utilizing the 'adam-assist' package for its core functionalities, showcasing its efficiency and effectiveness in handling complex data tasks.