🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risk factors, but the incomplete maintainer profile and possible shell execution commands raise concerns about its safety.

Incomplete maintainer profile
Potential benign shell execution

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: The shell execution appears to be benign, possibly for help or version checking purposes, but could indicate potential execution risks if the command is dynamically determined.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The maintainer has an incomplete profile and seems to be new or inactive, which raises some concerns but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

Test runner config found: pyproject.toml
3 test file(s) detected (e.g. test_cli.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (9520 chars)

○ 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

17 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

6 unique contributor(s) across 100 commits in ob-labs/agentseek
Active community — 5 or more distinct contributors

🔬 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)

and is not None result = subprocess.run([command, "--help"], capture_output=True, text=True, check=F

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: apache.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ob-labs/agentseek appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 agentseek

Create a mini-application called 'DataWorkflowManager' that leverages the 'agentseek' Python package to manage and automate complex data workflows in real-time. This application will serve as a powerful tool for data scientists and analysts to streamline their data processing tasks, ensuring that every step of the workflow is executed efficiently and accurately. Here’s a step-by-step guide on how to build this application:

1. **Setup**: Begin by installing the 'agentseek' package along with any necessary dependencies. Ensure your development environment is set up correctly.
2. **Define Workflow Components**: Use 'agentseek' to define various components of your data workflows, such as data ingestion, transformation, and analysis steps. Each component should be designed to handle specific tasks within the workflow.
3. **Runtime Data Management**: Implement a feature where users can input their data directly into the application or upload files. Utilize 'agentseek' to manage these data inputs efficiently, ensuring they are stored and accessed correctly during the workflow execution.
4. **Real-Time Execution**: Develop a system that allows the defined workflows to run in real-time. Use 'agentseek' to monitor the progress of each workflow step and ensure smooth transitions between different stages.
5. **Error Handling & Logging**: Incorporate robust error handling and logging mechanisms. If any step fails, 'agentseek' should automatically log the issue and provide options for recovery or rerouting data to alternative processes.
6. **User Interface**: Create a user-friendly interface where users can view the status of their workflows, edit existing workflows, and create new ones. The UI should also display logs and error messages for troubleshooting purposes.
7. **Customization Options**: Allow users to customize their workflows by adding, removing, or modifying steps. This flexibility ensures that the application can adapt to various data processing needs.
8. **Documentation & Support**: Finally, write comprehensive documentation detailing how to use 'DataWorkflowManager', including examples of common workflows and best practices. Provide support through a forum or FAQ section to assist users with any issues they encounter.

By following these steps, you will have developed a fully functional mini-app that not only showcases the capabilities of the 'agentseek' package but also provides practical value to users dealing with complex data workflows.