🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate and serves its intended purpose without any signs of malicious intent or obfuscation.

Low risk scores across all categories.
No indications of shell execution, obfuscation, or credential harvesting.

Per-check LLM notes

Network: The network calls appear to be part of normal service and health check operations, not indicative of malicious activity.
Shell: No shell execution patterns detected, which is expected and safe.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 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

5 type-annotated function signatures (partial)

○ 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 score 6.0

Found 4 network call pattern(s)

名的 Agent 服务信息""" with httpx.Client(timeout=15.0) as client: resp = client.get(f"{se
ID 反向查找 Agent""" with httpx.Client(timeout=15.0) as client: resp = client.get(f"{se
"""同步健康检查""" with httpx.Client(timeout=15.0) as client: resp = client.get(f"{se
nt 服务信息""" async with httpx.AsyncClient(timeout=15.0) as client: resp = await client.get

✓ 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: example.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Your Name" 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 aid-resolver

Develop a decentralized agent discovery utility using the 'aid-resolver' Python package. This utility will enable users to discover and interact with various decentralized agents across a network without relying on a centralized authority. Here’s a detailed plan for building this application:

1. **Project Setup**: Initialize your project with the necessary Python dependencies, including 'aid-resolver'. Ensure you have a virtual environment set up for Python.

2. **Core Functionality**:
- **Agent Discovery**: Implement a feature that allows users to query the network for available agents based on specific criteria such as location, service type, or availability status.
- **Agent Interaction**: Once discovered, provide a mechanism for users to communicate directly with these agents, possibly by sending requests or commands.

3. **User Interface**: Design a simple yet effective command-line interface (CLI) for users to interact with the utility. Consider adding options for advanced users to customize queries and interactions.

4. **Security Measures**: Since the utility deals with decentralized networks, ensure that security measures are in place to protect user data and communications. This might include encryption for data in transit and secure storage of any user credentials or keys.

5. **Documentation and Testing**: Write comprehensive documentation detailing how to install, configure, and use the utility. Include examples and best practices. Also, develop a suite of tests to verify the functionality and reliability of your application.

6. **Integration with 'aid-resolver'**: Utilize the 'aid-resolver' package to handle the underlying mechanics of agent discovery and interaction. Familiarize yourself with its API and integrate it seamlessly into your application to leverage its capabilities.

7. **Deployment and Maintenance**: Plan for deployment of your utility, considering cloud-based solutions if applicable. Also, outline a maintenance strategy to address future updates or issues.

By following these steps, you'll create a powerful tool for interacting with decentralized networks, demonstrating the practical application of the 'aid-resolver' package.