AI Analysis
Final verdict: SUSPICIOUS
The package has low risks in terms of network usage, shell execution, and obfuscation, but the metadata suggests potential issues with maintenance and effort level, raising suspicion.
- Low network risk
- Low shell risk
- Low obfuscation risk
- Low credential risk
- Potential low maintenance indicating suspicious activity
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 low risk of command injection or system exploitation.
- 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 could potentially be a low-effort attempt at malicious activity.
Package Quality Overall: Low (2.0/10)
○ 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 (9995 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
○ 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
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: gene.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 assaybench
Create a mini-application named 'BioRanker' that leverages the 'assaybench' Python package to rank genes based on their relevance in biological assays. The application should allow users to input gene expression data from various biological assays and then use AssayBench's evaluation metrics to rank these genes according to their importance. Here’s a detailed breakdown of the steps and features you need to implement: 1. **Setup**: Begin by installing the 'assaybench' package using pip. Ensure all necessary dependencies are also installed. 2. **Data Input Interface**: Develop a user-friendly interface where users can upload their gene expression datasets. These datasets should be in CSV format, containing columns for gene names and expression levels across different samples. 3. **Preprocessing Module**: Implement a preprocessing module that cleans and normalizes the uploaded data. This includes handling missing values, scaling expression levels, and converting gene names to standardized identifiers if necessary. 4. **Gene Ranking Algorithm**: Utilize 'assaybench' to apply its built-in algorithms for gene ranking. These algorithms should consider multiple factors such as differential expression, pathway enrichment, and functional annotations to provide a comprehensive ranking. 5. **Visualization Tool**: Create interactive visualizations to display the ranked genes. Include bar charts showing top-ranked genes, heatmaps illustrating expression patterns, and network graphs depicting interactions between genes. 6. **Evaluation Metrics**: Integrate 'assaybench's evaluation metrics to assess the performance of the ranking algorithms. Provide users with detailed reports on precision, recall, F1-score, and other relevant metrics. 7. **Export Results**: Allow users to export the ranked gene list and evaluation metrics in various formats such as CSV, Excel, or PDF for further analysis or reporting. 8. **Documentation & Help**: Finally, ensure your application comes with comprehensive documentation and a help section that guides users through each step of the process, explaining how 'assaybench' enhances gene ranking accuracy and reliability.
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