🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks, but the incomplete author information and apparent inactivity of the maintainer raise concerns about potential supply-chain attacks.

Incomplete author information
Inactive maintainer

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 immediate risk of executing arbitrary commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is incomplete and the maintainer seems new or inactive, raising some concerns but not definitive proof of malice.

📦 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 (3765 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

86 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

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

No GitHub repository linked

No GitHub repository link found

⚠ 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 agriflux

Create a mini-application called 'Agriflux Insights' that leverages the Maiaddy Cloud Essence Agriflux platform for agricultural intelligence and supply chain optimization. This application will provide farmers and agricultural businesses with real-time data analysis, predictive analytics, and supply chain visibility to improve their operations. Here’s a detailed step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python environment and installing the `agriflux` package along with other necessary libraries such as pandas for data manipulation and matplotlib/seaborn for data visualization.
2. **Data Collection**: Use the `agriflux` package to connect to the Maiaddy Cloud Essence platform and fetch real-time data related to crop health, soil conditions, weather forecasts, and market prices.
3. **Data Analysis**: Implement functions within your application to analyze the collected data using statistical methods and machine learning algorithms. For example, use historical data to predict future crop yields based on current conditions.
4. **Supply Chain Optimization**: Develop a module that optimizes the supply chain by suggesting optimal times for planting, harvesting, and selling crops based on market trends and weather predictions.
5. **User Interface**: Design a simple yet effective user interface where users can input specific parameters like location, type of crop, etc., and receive personalized insights and recommendations.
6. **Visualization**: Utilize matplotlib/seaborn to create visual representations of the data, such as graphs showing predicted yields over time or maps highlighting areas with ideal growing conditions.
7. **Reporting**: Allow users to generate and download reports summarizing key findings from the data analysis and supply chain optimizations.
8. **Integration with Other Tools**: Explore integrating your application with other agricultural tools or platforms for a more comprehensive solution.

The `agriflux` package will be utilized extensively throughout this process for data fetching and analysis. Ensure that you document your code well and include comments explaining how each part of the application works.