andro

v0.3 suspicious
4.0
Medium Risk

Reference-guided CLI tool for finding and annotating human rDNA units in FASTA sequences

🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package shows no direct signs of malicious intent or activity, the low repository activity and single contributor raise concerns about its maintenance and reliability.

  • Low repository activity
  • Single contributor
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communication for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The repository's low activity and single contributor suggest potential risk, but insufficient evidence for malicious intent.

📦 Package Quality Overall: Low (3.0/10)

○ 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 (2740 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

  • 78 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 2 commits in dmelkovic/andro
  • Single author with few commits — possibly a personal or throwaway project

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "D. Melkovic" 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 andro 
Create a command-line tool named 'rDNAAnalyzer' using the Python package 'andro'. This tool will allow users to analyze FASTA files containing DNA sequences to identify and annotate human rDNA units within them. The application should provide the following functionalities:

1. **Input Handling**: Users should be able to input a FASTA file path as a command-line argument.
2. **Sequence Analysis**: Utilize the 'andro' package to process the provided FASTA file and identify rDNA units.
3. **Annotation Output**: Display annotated results of the identified rDNA units in a user-friendly format. Optionally, allow the output to be saved to a new FASTA file or a tab-separated values (TSV) file.
4. **Help/Usage Information**: Include comprehensive help documentation accessible via command-line options, explaining how to use the tool and what each option does.
5. **Error Handling**: Implement robust error handling to manage cases where the input file is not found, is incorrectly formatted, or other potential issues.
6. **Customization Options**: Provide options for users to customize the analysis process, such as setting thresholds for unit detection or specifying reference genomes.

The project should demonstrate proficiency in utilizing the 'andro' package's core functionalities while also showcasing best practices in Python programming, including modular design, efficient data handling, and clear documentation.

💬 Discussion Feed

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