amulety

v2.1.3 suspicious
4.0
Medium Risk

Python package to create embeddings of BCR amino acid sequences.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate suspicious activity due to incomplete metadata and potential obfuscation. While it does not pose immediate threats like network calls or credential theft, the lack of comprehensive documentation and the presence of shell executions raise concerns.

  • Incomplete author metadata
  • Potential obfuscation
  • Execution of external commands
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution attempts to run external commands like 'igblastn' and 'tcremp-run', possibly for functionality purposes, but could indicate risky behavior if not properly documented.
  • Obfuscation: The obfuscated code may indicate an attempt to evade detection or analysis, but it could also be a legitimate practice in certain applications like cryptography.
  • Credentials: No clear patterns of credential harvesting were detected.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_bcr_embeddings.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5146 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 23 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in immcantation/amulety
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • odel.to(device) model.eval() except ImportError as e: logger.error("transf
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • e system.""" try: subprocess.run(["igblastn", "-help"], capture_output=True, check=True)
  • ..") try: pipes = subprocess.Popen(command_igblastn, stdout=subprocess.PIPE, stderr=subprocess.
  • } """ subprocess.run(command, shell=True, check=True, text=True, capture_output=T
  • els try: result = subprocess.run(["tcremp-run", "-h"], capture_output=True, text=True, timeou
  • subprocess.run(command, shell=True, check=True, text=True, capture_output=True) except
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: yale.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository immcantation/amulety appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amulety 
Your task is to develop a mini-application called 'BCRAnalyzer' using Python, which leverages the 'amulety' package to analyze BCR (B-cell receptor) amino acid sequences. This application will help researchers understand the complexity of immune responses by providing insights into BCR sequences through advanced embedding techniques. Here’s a step-by-step guide on how to build this application:

1. **Setup Project Environment**: Start by setting up a virtual environment for your project and install necessary packages including 'amulety'. Ensure you have a clean and isolated environment to avoid dependency conflicts.

2. **Data Input Module**: Develop a module where users can input their BCR amino acid sequences either through a file upload or directly into the application interface. This module should validate the input data to ensure it meets the expected format and structure required by 'amulety'.

3. **Embedding Generation**: Use 'amulety' to generate embeddings from the provided BCR sequences. This involves understanding the core functionalities of 'amulety' such as sequence preprocessing, embedding model selection, and embedding generation. Document how these steps are performed within your application.

4. **Analysis Tools**: Implement tools within the application that allow users to perform various analyses on the generated embeddings. For example, clustering similar sequences together, identifying unique patterns, or visualizing embeddings in a lower-dimensional space using techniques like t-SNE or PCA. These tools should provide meaningful insights into the relationships between different BCR sequences.

5. **Visualization Interface**: Create a user-friendly visualization interface where users can explore the results of their analysis. This could include interactive plots, heatmaps, or other graphical representations of the data. The goal is to make complex information accessible and understandable to non-technical users.

6. **Documentation and Reporting**: Include a feature where users can generate reports summarizing their findings. This report should include key metrics derived from the analysis, visualizations, and any other relevant information that aids in interpreting the results.

7. **Testing and Validation**: Finally, thoroughly test your application with different datasets to ensure its reliability and accuracy. Validate the outputs against known benchmarks or manually curated datasets to ensure the quality of the analysis.

Suggested Features:
- Support for multiple input formats (FASTA, CSV, etc.)
- Real-time feedback on data validation errors
- Integration with popular machine learning libraries for advanced analytics
- Customizable visualization options based on user preferences
- Detailed documentation and tutorials for new users

By following these steps and incorporating the suggested features, you'll create a powerful tool for researchers working with BCR sequences. Remember to leverage 'amulety' effectively to ensure accurate and insightful analysis.

💬 Discussion Feed

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