🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk with no network calls, shell executions, or obfuscation. However, the metadata risk due to the unavailability of the repository and the maintainer's single package presence slightly increases suspicion.

No network calls
Single package maintainer
Repository not found

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 no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The repository is not found and the maintainer has only one package, which could indicate potential risk.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_core.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3903 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 7.0

Partial type annotation coverage

Type checker (mypy / pyright / pytype) referenced in project
65 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Philip W Fowler" 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 amygda

Create a fully-functional mini-application that leverages the 'amygda' Python package to analyze the growth of mycobacteria on antibiotic plates and infer Minimum Inhibitory Concentrations (MICs). This application will serve as a tool for microbiologists and researchers to streamline their analysis process.

Step-by-Step Instructions:
1. Set up a virtual environment for your project and install the 'amygda' package along with any other necessary dependencies.
2. Design a user-friendly interface where users can upload images of antibiotic plates containing mycobacterial cultures.
3. Implement image processing functionality using 'amygda' to automatically detect and quantify bacterial growth on the plates.
4. Use 'amygda' to calculate the MICs based on the quantified growth data.
5. Provide visualizations such as graphs or charts to display the MIC values and growth trends.
6. Allow users to save and export their analysis results in formats like CSV or PDF.
7. Include error handling and validation checks to ensure data integrity and usability.
8. Document the code thoroughly and provide clear instructions on how to run and use the application.

Suggested Features:
- Batch processing capability to analyze multiple images at once.
- Integration with cloud storage services for easy data sharing and collaboration.
- Real-time feedback and progress indicators during analysis.
- Advanced settings for customizing the analysis parameters according to specific research needs.
- Support for different types of antibiotic plates and mycobacterial strains.

How 'amygda' is Utilized:
- 'amygda' will be used for the core functionalities of detecting bacterial colonies, quantifying their growth, and inferring MICs from the growth data. Users will input plate images, and 'amygda' will process these images to extract relevant information. The application will then present this information in a comprehensible format, enabling users to make informed decisions about antibiotic effectiveness against mycobacteria.