🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks identified. The credential risk is slightly elevated due to retrieval of AWS region from environment variables, but there are no indications of malicious activities.

Low network, shell, and obfuscation risks
Moderate credential risk due to environment variable usage
Low metadata risk suggesting limited maintenance

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.
Credentials: The code retrieves an AWS region from environment variables, which is a common practice but should be carefully managed to avoid hard-coded secrets.
Metadata: The package shows signs of low maintenance and lack of community involvement, but no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

Test runner config found: pyproject.toml
Test runner config found: conftest.py
14 test file(s) detected (e.g. __init__.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (5158 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
113 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 score 2.5

Found 1 credential access pattern(s)

egion name """ return os.environ.get('AWS_REGION', 'us-east-1') def is_local_mode() -> bool: """

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agrifrika.com>

✓ 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 4.0

2 maintainer concern(s) found

Author "Agrifrika Team" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agrifrika-shared

Create a small agricultural data analysis tool using the 'agrifrika-shared' Python package. This tool will serve as a mini-application that helps farmers and agricultural researchers analyze soil quality data and predict crop yields based on historical and current environmental conditions. Here are the steps and features you should include:

1. **Setup**: Begin by installing the 'agrifrika-shared' package. Ensure that your environment supports Python and has the necessary dependencies.
2. **Data Input**: Design a user-friendly interface where users can upload soil quality reports and weather data. These could be CSV files containing various parameters such as pH levels, nutrient content, temperature, rainfall, etc.
3. **Data Processing**: Utilize the shared utilities from 'agrifrika-shared' to preprocess the uploaded data. This includes cleaning the data, handling missing values, and normalizing the dataset.
4. **Analysis**: Implement functions to analyze the data. Use the models provided by 'agrifrika-shared' to perform predictive analysis on crop yields based on the input data. Include visualizations of the analysis results, such as graphs showing predicted yield trends over time.
5. **Reporting**: Create a feature that generates a summary report based on the analysis. This report should include key insights, recommendations for improving soil quality, and predictions for future crop yields.
6. **Integration**: Optionally, integrate the tool with other services, such as cloud storage for data backup and sharing, or real-time weather updates via APIs.
7. **Documentation**: Provide clear documentation on how to use the tool, including setup instructions and usage guides. Ensure that the documentation highlights the benefits of using 'agrifrika-shared' for agricultural data analysis.

This project aims to showcase the versatility and practical applications of 'agrifrika-shared', making it accessible for those in the agricultural sector looking to leverage data-driven decision-making.