🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to sparse metadata and inaccessible repository, which raises concerns about its legitimacy. However, there are no direct indications of malicious activity.

Sparse author details and inaccessible git repository
Moderate network risk

Per-check LLM notes

Network: The use of an HTTP client suggests the package might be performing network requests, which could be legitimate if it's designed to interact with APIs or fetch data.
Shell: No shell execution patterns detected, indicating no immediate risk associated with shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's details are sparse and the git repository is not accessible, raising concerns about the package's legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

sed: self._http = httpx.AsyncClient(timeout=self._timeout) return self._http async

✓ 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: aegro.com.br>

✓ 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 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 aegro

Your task is to develop a comprehensive mini-application using the 'aegro' Python package, which serves as a command-line interface for managing agricultural data through the Aegro API. This application will streamline the process of collecting, analyzing, and visualizing crop growth data for farmers. Here’s a detailed breakdown of your objectives and steps to achieve them:

1. **Project Setup**: Begin by setting up your Python environment and installing the 'aegro' package. Ensure you have access to the Aegro API documentation for understanding its endpoints and functionalities.

2. **Core Functionality**:
- **Data Collection**: Implement a feature to fetch real-time crop growth data from the Aegro API. This could include soil moisture levels, temperature, humidity, and other environmental factors affecting crops.
- **Data Storage**: Develop a mechanism to store collected data locally or in a cloud-based database. This will enable users to track historical data over time.
- **Data Analysis**: Integrate basic analysis tools to help users understand trends and patterns in their crop growth data. Consider implementing functions like anomaly detection, growth rate calculation, and yield prediction based on historical data.

3. **Enhanced Features**:
- **Visualization**: Create visual representations of the data, such as graphs and charts, to provide an intuitive view of crop health and growth over time.
- **Alert System**: Set up an alert system that notifies users via email or SMS when specific conditions are met (e.g., soil moisture below a certain threshold).
- **User Interface**: Although primarily a CLI tool, consider adding a simple web interface using Flask or Django to make it more accessible to non-technical users.

4. **Testing & Documentation**:
- **Unit Testing**: Write unit tests for each function to ensure reliability.
- **Documentation**: Provide clear documentation on how to install and use the application, including examples of API calls and expected outputs.

5. **Deployment**:
- Prepare the application for deployment on a server or cloud platform. Ensure that all dependencies are managed properly.

The 'aegro' package will be central to fetching and processing data from the Aegro API. Your goal is to create a robust, user-friendly tool that leverages the power of AI and machine learning to enhance agricultural productivity.