🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential code obfuscation and lack of detailed metadata, suggesting low maintainer effort or possible malicious intent.

Obfuscation risk of 5/10
Metadata risk of 4/10

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution from the package.
Obfuscation: The observed patterns may indicate an attempt to obfuscate code, but without further context, it's uncertain if this is malicious.
Credentials: No clear signs of credential harvesting detected.
Metadata: The package shows signs of low maintainer effort and lacks proper author information, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (967 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

171 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

pl.col('seg_a').list.eval( (
pl.col('seg_b').list.eval( (

✓ 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

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 agglovar

Create a mini-application named 'GenoIntersect' using Python and the 'agglovar' package, which specializes in handling genomic variant data efficiently. This tool will allow users to upload two sets of genomic variant data in VCF format, transform them into a compatible format for processing, and then find intersections between these datasets. The application should have the following features:

1. **User Interface**: Develop a simple yet intuitive web-based interface where users can upload their VCF files.
2. **Data Transformation**: Utilize 'agglovar' to convert uploaded VCF files into a format optimized for quick processing and storage.
3. **Intersection Calculation**: Implement functionality to calculate the intersection of variants between the two transformed datasets. This includes identifying common variants across both datasets.
4. **Visualization**: Provide a basic visualization of the intersected variants, highlighting their positions on a chromosome.
5. **Download Option**: Allow users to download the results of the intersection as a new VCF file.

The 'agglovar' package plays a crucial role in transforming and intersecting genomic data quickly and efficiently. Ensure that your application leverages its capabilities to handle large datasets and provide real-time feedback to the user.