🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of network calls, shell execution, or code obfuscation, indicating low risk. However, the maintainer's single package raises a slight concern about potential supply-chain risks.

No network calls or shell executions detected
Single package from maintainer raises minor suspicion

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution detected, indicating the package does not perform system-level commands that could be exploited.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to stealing secrets or credentials.
Metadata: The maintainer has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

🔬 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 2.0

1 maintainer concern(s) found

Author "Michael Chen" 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 agentic-delivery

Your task is to create a mini-application called 'DeliveryScheduler' using the Python package 'agentic-delivery'. This application will help users manage their delivery schedules efficiently by providing functionalities such as scheduling deliveries, tracking them, and generating reports.

Step 1: Setup the Environment
- Install the necessary dependencies including 'agentic-delivery' and other required packages like Flask for the web interface.

Step 2: Design the Application Structure
- Create a basic structure for your application with separate modules for scheduling, tracking, and reporting.
- Integrate 'agentic-delivery' to handle the backend processes related to scheduling and tracking.

Step 3: Implement Scheduling Functionality
- Allow users to input delivery details such as date, time, location, and type of goods.
- Use 'agentic-delivery' to automate the process of sending these details to a predefined delivery script or pipeline.

Step 4: Implement Tracking Functionality
- Provide users with a way to track their deliveries by entering a unique identifier.
- Utilize 'agentic-delivery' to fetch real-time status updates from the delivery pipeline.

Step 5: Implement Reporting Functionality
- Enable users to generate reports on past deliveries including details such as delivery time, status, and any issues encountered.
- Use 'agentic-delivery' to compile data from previous delivery scripts and pipelines into comprehensive reports.

Suggested Features:
- User authentication to secure personal delivery information.
- Notifications for delivery updates via email or SMS.
- Integration with mapping services to provide visual delivery routes.
- Support for multiple languages to cater to a diverse user base.

How to Utilize 'agentic-delivery':
- For scheduling, use 'agentic-delivery' to run scripts that prepare and send out delivery instructions.
- For tracking, leverage 'agentic-delivery' to query the current status of deliveries based on provided identifiers.
- For reporting, utilize 'agentic-delivery' to access historical data and compile it into useful reports.