🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network usage, shell execution, and credential handling. However, it shows signs of potential supply-chain risks due to low maintainer activity and poor metadata quality.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
Obfuscation: The observed patterns seem to be part of a legitimate computation process rather than malicious obfuscation.
Credentials: No signs of credential harvesting or secret handling were detected.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

]].sum() perf['p'] = perf.eval('k/n') n_animal = len(perf.index.unique('animal_id'))
'sum': 'correct'}) \ .eval('correct/n') \ .unstack('trial_type') n_animals

✓ 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: ohsu.edu>

✓ 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 abtsdata

Develop a Python-based mini-application named 'ABTS Manager' that leverages the 'abtsdata' package to manage and analyze ABTS (Assay Batch Tracking System) data efficiently. This tool will serve as a versatile solution for researchers and lab technicians who need to track, manipulate, and visualize assay batch data. The application should include the following functionalities:

1. **Data Importation**: Allow users to import ABTS data from various file formats such as CSV, Excel, or JSON. Use the 'abtsdata' package's capabilities to ensure seamless data handling and conversion.
2. **Data Manipulation**: Implement functions to clean, filter, and transform the imported data according to user-defined criteria. For example, users should be able to filter out specific batches based on date ranges or sample types.
3. **Batch Analysis**: Integrate statistical analysis tools within the application to provide insights into the ABTS data. Users should be able to perform basic statistics like mean, median, mode, and standard deviation for different batch parameters.
4. **Visualization**: Create visual representations of the analyzed data using libraries such as Matplotlib or Seaborn. Visualizations should include bar charts, line graphs, and histograms to help users understand trends and patterns in the data.
5. **Reporting**: Develop a feature that generates comprehensive reports based on the analyzed data. These reports should be exportable in formats such as PDF or Word.
6. **User Interface**: Design a simple and intuitive graphical user interface (GUI) using Tkinter or PyQt to make the application accessible to non-technical users.

The 'abtsdata' package will be utilized throughout the development process for its lightweight yet powerful tools designed specifically for managing ABTS data. It will streamline data handling tasks, ensuring accuracy and efficiency in data processing and analysis.