AI Analysis
Final verdict: SUSPICIOUS
The package has some potential risks due to subprocess execution without clear context and low maintainer engagement, raising concerns about its reliability and security.
- Shell risk due to subprocess execution
- Low maintainer engagement
Per-check LLM notes
- Network: No network calls detected, which is normal and expected.
- Shell: Subprocess execution is present but without specific commands or context, it's hard to determine intent; could be benign or potentially risky depending on the commands executed.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer engagement and lack of detail, which may indicate it's not well-maintained or trustworthy.
Package Quality Overall: Low (2.8/10)
○ 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 (2684 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
217 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
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
fh.flush() p = subprocess.Popen( command, stdout=subprocess.ommands): p = subprocess.Popen( cmd, stdin=stream_p
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com>
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 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 axiomkit
Create a bioinformatics tool called 'SequenceAnalyzer' using Python and the 'axiomkit' package. This tool will facilitate the analysis of DNA sequences by providing functionalities such as sequence alignment, motif detection, and basic statistical analysis. Here’s a detailed plan on how to develop this application: 1. **Project Setup**: Start by setting up your Python environment. Ensure you have Python installed along with pip. Install axiomkit via pip. 2. **Input Handling**: Develop a module to handle input sequences. Users should be able to input DNA sequences either through command line arguments or from a file. Utilize axiomkit’s I/O utilities to efficiently read and write sequence data. 3. **Sequence Alignment**: Implement a feature to align two DNA sequences. Use axiomkit’s accelerated backend functions to perform sequence alignment efficiently. Provide options for different alignment algorithms such as global, local, or semi-global alignments. 4. **Motif Detection**: Create a module for detecting motifs within a DNA sequence. Utilize axiomkit’s pattern matching capabilities to identify specific nucleotide patterns that may indicate important biological features like transcription factor binding sites. 5. **Statistical Analysis**: Add functionality to perform basic statistical analyses on sequences, such as calculating GC content, sequence length distribution, and other relevant metrics. Leverage axiomkit’s computational efficiency to process large datasets quickly. 6. **Output Presentation**: Design a user-friendly interface for presenting results. Include both command line output and graphical representations if possible. Axiomkit’s visualization utilities can be used here for generating plots and charts. 7. **Documentation and Testing**: Write comprehensive documentation for all modules and functions. Ensure thorough testing of each component to guarantee accuracy and reliability. 8. **Enhancements and Future Work**: Consider adding advanced features like multiple sequence alignment, phylogenetic tree construction, or integration with external databases for more complex analyses. By following these steps, you'll create a robust, efficient, and user-friendly bioinformatics tool that leverages the powerful capabilities of the axiomkit package.
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