🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators and no clear signs of malicious activity. The primary concern is the potential for network communications, though this is likely benign given the package's description.

Network risk due to possible external service communication
Low risk in other categories such as shell execution, obfuscation, and credential handling

Per-check LLM notes

Network: The presence of network calls suggests the package may be designed to communicate with external services, but without further context, it's hard to determine if this is intended or malicious.
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 lacks PyPI classifiers, suggesting low effort or a new account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

ey self._client = httpx.AsyncClient( headers=headers, timeout=ht
io") client._client = httpx.AsyncClient( transport=httpx.MockTransport(handler),
88") client._client = httpx.AsyncClient(transport=httpx.MockTransport(handler)) try:
) client._client = httpx.AsyncClient(transport=httpx.MockTransport(handler)) try:

✓ 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 4.0

2 maintainer concern(s) found

Author "Agent Control Team" 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 agent-control-evaluator-galileo

Create a comprehensive evaluation tool for AI agents using the 'agent-control-evaluator-galileo' package. This tool will serve as a platform for developers and researchers to assess the performance of their AI agents across various tasks. The application should include the following functionalities:

1. **Agent Registration**: Allow users to register their AI agents by providing necessary credentials and details.
2. **Task Assignment**: Implement a system where registered agents can be assigned specific tasks based on their capabilities and the task requirements.
3. **Evaluation Metrics**: Utilize the 'agent-control-evaluator-galileo' package to define and apply evaluation metrics such as accuracy, efficiency, and adaptability. These metrics should be customizable to cater to different types of tasks.
4. **Performance Reports**: Generate detailed performance reports for each agent after completing their assigned tasks. These reports should include comparative analysis with other agents and suggestions for improvement.
5. **User Interface**: Develop a user-friendly interface that allows easy interaction with the tool. Users should be able to view agent profiles, assign tasks, and access performance reports.
6. **Integration Capabilities**: Ensure the tool can integrate with external systems and APIs to fetch additional data or perform automated evaluations.
7. **Security Measures**: Implement robust security measures to protect agent data and ensure the integrity of the evaluation process.

The 'agent-control-evaluator-galileo' package will be crucial in defining the evaluation criteria and processing the results from the agents' performances. Your task is to design and implement this tool, ensuring it is scalable, efficient, and user-friendly.