🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious intent or unusual behavior, with all primary risks scoring very low. The only concern is the metadata quality, but this alone does not indicate any significant threat.

No network calls
No shell execution
No obfuscation
No credential harvesting
Incomplete author information

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution detected, which is expected unless the package's functionality involves system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk but requires further investigation due to incomplete author information and low metadata quality.

📦 Package Quality Overall: Low (3.0/10)

○ 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 (2183 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

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 27 commits in AnatoliyLarkin/anchorminer
Two distinct contributors found

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AnatoliyLarkin/anchorminer appears legitimate

⚠ 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 anchorminer

Create a Python-based mini-application that predicts MHC-I anchor positions using the 'anchorminer' package. This tool will allow researchers and bioinformaticians to input a sequence of amino acids and receive predictions about potential anchor positions for Major Histocompatibility Complex class I molecules. Here’s a detailed step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed and create a virtual environment. Install the 'anchorminer' package along with any other necessary dependencies such as Biopython for handling biological sequences.

2. **Design User Interface**: Design a simple command-line interface (CLI) for users to interact with the application. The CLI should accept inputs like the amino acid sequence and optional parameters such as specific alleles if the user wishes to predict for a particular MHC-I molecule type.

3. **Sequence Validation**: Implement a function to validate the input sequence. Ensure it contains only valid amino acids and is within an acceptable length range for analysis.

4. **Anchor Position Prediction**: Utilize the 'anchorminer' package to predict anchor positions based on the provided sequence. Customize the prediction process to optionally use allele-specific Position Weight Matrices (PWMs) if specified by the user.

5. **Output Results**: Display the predicted anchor positions in a readable format. Include additional information such as confidence scores or probability values associated with each prediction.

6. **Optional Features**:
   - **Visualization**: Provide a feature to visualize the predicted anchor positions over the sequence using a simple plot or graph.
   - **Report Generation**: Allow users to generate a report summarizing the predictions, including the input sequence, predicted anchor positions, and any relevant statistics.
   - **Batch Processing**: Enable batch processing where multiple sequences can be analyzed at once.

7. **Testing and Documentation**: Write test cases to ensure the accuracy and reliability of your application. Document the code and provide instructions on how to install and use the application effectively.

By following these steps, you'll develop a valuable tool for predicting MHC-I anchor positions, enhancing research capabilities in immunology and related fields.