🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some concerning signs, particularly the metadata indicating low effort and potential lack of transparency, while other risks remain low.

Metadata risk at 6/10
No clear description provided

Per-check LLM notes

Network: The observed network call pattern suggests the package is making HTTP requests to an API endpoint, which could be for legitimate purposes like reporting usage or fetching updates.
Shell: No shell execution patterns were detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several signs of low effort and potential lack of transparency, raising suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

rip("/") self._http = httpx.Client(base_url=self.api_url, timeout=timeout) def submit_inte

✓ 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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agentbrake

Create a fault-tolerant chatbot application using Python and the 'agentbrake' package. This chatbot will interact with users via text messages and will be designed to gracefully handle failures when communicating with external services such as weather APIs or news feeds. Here’s a detailed breakdown of what your application should achieve:

1. **User Interaction**: Implement a simple command-line interface where users can input queries related to current weather conditions or latest news headlines.
2. **External Service Integration**: Integrate the chatbot with at least two different external services (e.g., OpenWeatherMap API for weather data and NewsAPI for news headlines).
3. **Circuit Breaker Mechanism**: Use 'agentbrake' to implement a circuit breaker pattern around each external service call. This ensures that if an external service fails repeatedly, the chatbot does not continue to attempt calls to it, thus preventing further failure cascades and improving overall system resilience.
4. **Fallback Mechanisms**: When an external service is down, provide fallback responses to the user, such as suggesting alternative ways to obtain the requested information or offering generic content.
5. **Logging and Monitoring**: Implement logging to record all interactions and failures. Additionally, consider adding a basic monitoring feature to alert developers when a service goes down or recovers.
6. **Configuration Management**: Allow for easy configuration of the external services’ API keys and other settings without modifying the codebase directly.
7. **Testing**: Write unit tests and integration tests to ensure that the circuit breaker logic works correctly under various failure scenarios.

Your goal is to demonstrate how 'agentbrake' can enhance the reliability of applications that depend on multiple external services. Ensure your application is well-documented and easy to extend.