🤖 AI Analysis

Final verdict: SAFE

The package presents a low risk profile with no signs of obfuscation or credential harvesting. The metadata risk is slightly elevated due to the package being new and the maintainer having only one package, but this alone is insufficient to suggest any malicious intent.

No obfuscation patterns detected
No credential harvesting patterns detected
New package with limited maintainer history

Per-check LLM notes

Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
Metadata: The package is newly released and the maintainer has a single package, which may indicate a lower level of trust but does not necessarily imply malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

try: completed = subprocess.run( command, cwd=self.root,

✓ 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

Repository yazeed-km/AgentGuard appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "AgentGuard CI Contributors" 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 agentguardCI

Develop a fully-functional mini-application called 'AgentGuardian' that leverages the capabilities of the 'agentguardCI' package to ensure robustness and reliability in AI agents that interact with various software tools. This application will serve as a CI (Continuous Integration) testing suite specifically designed for AI agents that use external tools, ensuring they behave as expected across different environments and configurations.

Step 1: Understand the Basics
- Begin by installing the 'agentguardCI' package using pip.
- Study the documentation to understand how 'agentguardCI' works and its core functionalities.

Step 2: Define the Scope
- Identify common tools that AI agents might use, such as version control systems (e.g., Git), issue trackers (e.g., Jira), and other development tools.
- Determine the key behaviors or interactions that need to be tested, like committing changes, opening issues, or running scripts.

Step 3: Design the Architecture
- Create a modular design where each module tests a specific aspect of an AI agent's interaction with a tool.
- Implement a configuration system to allow users to specify which tools and behaviors to test.

Step 4: Develop Core Features
- Develop test cases for each identified behavior using 'agentguardCI'. For example, create a test case to verify if an AI agent correctly commits code changes to a Git repository.
- Utilize 'agentguardCI' to simulate different scenarios and configurations, ensuring the AI agent behaves as expected under various conditions.
- Integrate logging and reporting mechanisms to provide detailed feedback on test results.

Step 5: Enhance Functionality
- Add support for custom tools not covered by default configurations.
- Implement a feature to automatically generate test cases based on user-defined rules or patterns.
- Provide options for users to customize test parameters, such as timeout settings or retry limits.

Step 6: User Interface
- Design a simple command-line interface (CLI) for interacting with 'AgentGuardian'.
- Include options for specifying test configurations, running tests, and viewing results.
- Optionally, develop a basic web interface for more advanced users who prefer graphical interfaces.

Step 7: Documentation and Testing
- Write comprehensive documentation explaining how to install, configure, and use 'AgentGuardian'.
- Conduct thorough testing to ensure all features work as intended, including edge cases and error handling.

By following these steps, you'll create a powerful yet easy-to-use tool for developers and researchers working with AI agents that interact with external tools, ensuring their applications remain reliable and secure.