🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across multiple categories such as network, shell, and obfuscation risks. While there are some concerns regarding metadata quality and maintainer activity, these alone do not indicate malicious intent.

Low risk scores across various categories
Poor metadata quality and low maintainer activity

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

18 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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 anip-grpc

Create a real-time stock price monitoring application using the 'anip-grpc' package. This application will connect to a simulated stock market service via gRPC and provide users with live updates on stock prices of their chosen companies. The app should have the following functionalities:

1. User Interface: Develop a simple yet intuitive UI where users can input stock symbols (e.g., AAPL for Apple Inc.) and monitor the real-time price changes.
2. gRPC Connection: Utilize 'anip-grpc' to establish a connection with the stock market service. This involves setting up the necessary client-side gRPC bindings provided by 'anip-grpc'.
3. Real-Time Updates: Implement a mechanism within your application to periodically request and receive updated stock prices from the server. Display these updates in real-time on the user interface.
4. Historical Data Retrieval: Allow users to fetch historical data for a specific stock over a defined period (e.g., last 5 days). Use 'anip-grpc' to query the server for this information.
5. Notifications: Integrate a feature that sends push notifications when the price of a monitored stock reaches a certain threshold set by the user.
6. Error Handling: Ensure robust error handling to manage potential issues such as network failures or invalid stock symbols entered by the user.
7. Documentation: Provide comprehensive documentation detailing how to run the application, including setup instructions for the required environment and any additional dependencies.

The goal is to demonstrate the capabilities of 'anip-grpc' in facilitating real-time communication between a client application and a remote service, while also showcasing the practical application of gRPC in developing financial tools.