aadr-subset

v0.3.0 suspicious
4.0
Medium Risk

Declarative AADR panel subsetting from YAML selectors; the missing first-class tool for cohort definitions in ancient-DNA / population-genetics workflows.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low direct risks but raises concerns due to lack of repository activity and sparse maintainer information, suggesting potential issues with legitimacy.

  • Repository has no recent activity
  • Sparse maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is safe.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository has no activity and the maintainer's information is sparse, raising concerns about its legitimacy.

🔬 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: mailbox.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 aadr-subset 
Create a Python-based utility named 'AADRSelector' that leverages the 'aadr-subset' package to streamline the process of defining and applying cohort-specific genetic panels in ancient DNA research. This utility will allow researchers to easily subset their data according to predefined criteria stored in YAML files, enhancing reproducibility and efficiency in their workflows. Here’s how the application should work and what it should include:

1. **Project Setup**: Initialize a new Python project with necessary dependencies including 'aadr-subset'. Ensure all dependencies are listed in a requirements.txt file for easy installation.
2. **YAML Configuration**: Users should be able to define their selection criteria in YAML format. These configurations will include details like sample IDs, specific genetic markers, and other relevant attributes.
3. **Subset Creation**: Implement functionality to read these YAML configurations and use them to subset the AADR panel data accordingly. The utility should support various types of subsetting based on different criteria.
4. **Output Management**: Provide options for outputting the subsetted data in formats suitable for further analysis, such as CSV or JSON. Additionally, include an option to save the subset configuration for future reference or modification.
5. **User Interface**: Develop a simple command-line interface (CLI) that guides users through the process of selecting or creating a configuration file, running the subsetting operation, and viewing or saving the results.
6. **Documentation**: Include comprehensive documentation detailing how to install and use the utility, along with examples of valid YAML configurations and expected outputs.
7. **Testing**: Write unit tests to ensure the utility functions correctly under various scenarios, including edge cases where input data might be incomplete or invalid.

This utility aims to simplify the complex task of cohort definition in ancient DNA studies, making it more accessible and efficient for researchers working in this field.