🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks in terms of network, shell, and obfuscation activities. While there are concerns about its maintenance level, these do not strongly suggest malicious intent.

No network or shell risks detected
Low maintenance efforts noted

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no immediate risk of command injection or system manipulation.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows signs of low maintenance and effort, which could indicate potential risks but does not conclusively point to 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: cam.ac.uk>

✓ 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 abnativ

Create a mini-application called 'AbNatiV-Assessor' using the Python package 'abnativ'. This application will serve as a tool for researchers to assess the nativeness of antibodies based on their sequences. The application should allow users to input antibody sequence data and receive an assessment of its nativeness level.

Step-by-step guide:
1. Set up a user-friendly interface where users can upload antibody sequence files (in FASTA format).
2. Integrate the 'abnativ' package to process the uploaded sequences and perform the nativeness assessment.
3. Display the results in an easy-to-understand format, including a score indicating the nativeness level and a brief explanation of what the score means.
4. Implement a feature to save the results into a CSV file for further analysis or record-keeping.
5. Add a help section explaining the significance of nativeness in antibody research and how the 'abnativ' package works under the hood.

Suggested Features:
- Real-time validation feedback for the uploaded sequence files.
- Option to compare multiple antibody sequences at once.
- Interactive charts to visualize the nativeness scores across different sequences.
- A tutorial video demonstrating how to use the application effectively.

How to Utilize 'abnativ':
Use 'abnativ' to preprocess the antibody sequences, apply the VQ-VAE model for nativeness assessment, and extract the relevant metrics that indicate the nativeness level of each antibody. Ensure that the application leverages the full capabilities of 'abnativ', such as handling large datasets efficiently and providing accurate assessments.