🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its attempt to read '/etc/passwd' and execute shell commands without clear justification, along with a potentially suspicious metadata profile.

High credential risk due to reading '/etc/passwd'
Significant shell risk due to potential for executing commands
Suspicious metadata, including unfindable repository and a single-package maintainer

Per-check LLM notes

Network: The network call pattern suggests the package may be designed to fetch external resources, which is not inherently malicious but requires scrutiny to ensure it's used for legitimate purposes.
Shell: Executing shell commands can pose significant risks if not properly sanitized or controlled, indicating potential for abuse or unintended consequences.
Obfuscation: No obfuscation patterns detected in the provided code snippet.
Credentials: The code attempts to read '/etc/passwd', which could indicate an attempt to harvest credentials or system information, posing a security risk.
Metadata: The repository is not found and the maintainer has a single package, which may indicate a new or less active account, raising some suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

NDPOINT}" async with httpx.AsyncClient() as client: response = await client.get(

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

f) for f in files) proc = subprocess.run(cmd, capture_output=True, text=True, check=False) return

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

await loader.load("/etc/passwd", "text") @pytest.mark.asyncio async def test_bloc

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AgentMark Team" 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 agentmark-prompt-core

Create a mini-application named 'PromptMaster' using the 'agentmark-prompt-core' Python package. This application will serve as a user-friendly interface for generating and managing prompts for various AI agents. Here’s a detailed plan on how to proceed:

1. **Setup Environment**: Begin by setting up your Python environment and installing the necessary packages, including 'agentmark-prompt-core'. Ensure you have the latest version of the package for optimal functionality.

2. **Design the Application Structure**: Design the structure of your application to include modules for handling different aspects such as user input, prompt generation, and output display. Each module should be designed to be modular and reusable.

3. **Implement Core Functionality**: Use the 'agentmark-prompt-core' package to implement the core functionalities of your application. Specifically, focus on integrating its high-level runtime capabilities to work seamlessly with AgentMark prompts. This includes setting up the environment to accept user inputs for prompt customization and running these prompts through the AgentMark system.

4. **User Interface**: Develop a simple yet effective command-line interface (CLI) for users to interact with the application. Users should be able to easily input their desired prompts, select from predefined templates, and view the results of the generated prompts.

5. **Advanced Features**:
- **Prompt Customization**: Allow users to customize prompts by adding variables or specific instructions.
- **History Management**: Implement a feature to save and recall previously used prompts and their outcomes.
- **Template Library**: Provide a library of pre-defined templates for common use cases, such as data analysis, content creation, or problem-solving tasks.

6. **Testing and Validation**: Rigorously test each feature of the application to ensure it works as expected. Pay special attention to the integration with 'agentmark-prompt-core' to confirm seamless operation.

7. **Documentation and Deployment**: Document the setup process, usage instructions, and any limitations of the application. Consider deploying the application on a platform like GitHub for wider access and collaboration.

By following these steps and leveraging the 'agentmark-prompt-core' package, you'll create a powerful tool for anyone looking to harness the power of AI agents through customizable prompts.