🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential handling but has a moderate metadata risk due to the maintainer's limited package history and recent commit activity, suggesting potential supply-chain concerns.

moderate metadata risk
short timeframe for all commits

Per-check LLM notes

Network: The observed network call pattern is typical for health checks and may be part of normal package functionality.
Shell: No shell execution patterns were detected, indicating low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and all commits were made within a short timeframe, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

httpx try: resp = httpx.get(f"http://{host}:{port}/health", timeout=2.0) click.e
nt is None: _client = httpx.AsyncClient( limits=httpx.Limits(max_keepalive_connections=2

✓ 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 score 2.5

Git history flags: All 16 commits happened within 24 hours

All 16 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Varun Pratap Bhardwaj" 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 agentassert-typec-proxy

Create a mini-application named 'AIProxyConnector' that acts as a bridge between various AI services and a user interface, ensuring that all interactions adhere to predefined behavioral contracts using the 'agentassert-typec-proxy' package. This application will enable users to interact with different AI agents seamlessly while maintaining consistency and reliability across these interactions. Here’s a step-by-step guide on how to build it:

1. **Setup Project Environment**: Initialize a new Python environment and install the necessary packages including 'agentassert-typec-proxy'. Ensure your development environment is set up with Python 3.8 or higher.

2. **Define Behavioral Contracts**: Use 'agentassert-typec-proxy' to define formal behavioral contracts for different AI agents. These contracts will specify expected responses and behaviors from each AI service under specific conditions or inputs.

3. **Integrate AI Services**: Integrate at least three different AI services into your application, such as a language model, a recommendation engine, and a sentiment analysis tool. Each service should be configured to work within the constraints defined by its behavioral contract.

4. **Develop User Interface**: Create a simple web-based user interface using Flask or Django where users can select which AI service they wish to interact with and input their queries or data.

5. **Implement Proxy Logic**: Utilize 'agentassert-typec-proxy' to implement the proxy logic. This involves routing requests from the user interface to the appropriate AI service, enforcing the behavioral contracts, and forwarding the responses back to the user interface.

6. **Testing and Validation**: Implement automated tests to validate that the proxy correctly enforces the behavioral contracts across all integrated AI services. Test various edge cases to ensure robustness.

7. **Documentation and Deployment**: Write comprehensive documentation detailing how to use the 'AIProxyConnector' application, including setup instructions, usage examples, and troubleshooting tips. Prepare the application for deployment on a cloud platform like AWS or Heroku.

Suggested Features:
- Real-time interaction feedback indicating if the request is being processed according to the behavioral contract.
- A dashboard within the user interface showing statistics on AI service performance and adherence to contracts.
- Logging of all interactions for audit purposes.
- Ability to dynamically add or remove AI services and update their behavioral contracts without restarting the application.

By following these steps and incorporating the suggested features, you will create a powerful yet easy-to-use tool for managing interactions with multiple AI services, leveraging the 'agentassert-typec-proxy' package to ensure reliable and consistent behavior.