axonflow-google-adk-plugin

v1.0.2 suspicious
5.0
Medium Risk

AxonFlow governance plugin for Google Agent Development Kit (ADK)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, and obfuscation areas but exhibits suspicious commit patterns and an author with limited history, raising concerns about its legitimacy.

  • Suspicious commit patterns and limited author history
  • Low risk in network, shell, and obfuscation areas
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has suspicious commit patterns and an author with limited history, suggesting potential risk.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_plugin.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.getaxonflow.com/docs/integration/google-adk
  • Detailed PyPI description (6824 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 30 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 13 commits in getaxonflow/axonflow-google-adk-plugin
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: getaxonflow.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: All 13 commits happened within 24 hours

  • All 13 commits happened within 24 hours
⚠ 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 axonflow-google-adk-plugin 
Your task is to create a fully-functional mini-app that integrates with Google's Agent Development Kit (ADK) through the 'axonflow-google-adk-plugin' Python package. This app will serve as a bridge between your local development environment and Google's ADK, allowing you to manage and interact with Google's services more efficiently. Here’s a detailed breakdown of what your mini-app should accomplish:

1. **Setup**: Begin by setting up a virtual environment for your project. Install the necessary dependencies, including the 'axonflow-google-adk-plugin'. Ensure that you have the appropriate credentials and permissions to access Google's ADK.
2. **Authentication**: Implement a secure method for authenticating with Google's ADK. This could involve using OAuth 2.0 credentials or any other recommended authentication method provided by Google.
3. **Agent Management**: Your app should allow users to create, update, delete, and retrieve information about agents managed through Google's ADK. Each agent represents a conversational interface, such as a chatbot or voice assistant.
4. **Training Data Handling**: Users should be able to upload, download, and manage training data associated with their agents. Training data is crucial for improving the performance of conversational interfaces.
5. **Testing and Validation**: Integrate functionality that allows users to test their agents and validate responses based on specific inputs. This feature should provide feedback on how well the agent performs under different scenarios.
6. **Deployment Options**: Offer options for deploying agents to different environments (e.g., testing, staging, production). This includes managing versions and rolling out updates seamlessly.
7. **User Interface**: Develop a simple yet effective user interface where users can interact with all the above functionalities. Consider using frameworks like Tkinter or PyQt for a desktop application, or Flask/Django for a web-based solution.
8. **Documentation and Support**: Provide comprehensive documentation for your app, detailing setup instructions, usage examples, and troubleshooting tips. Also, include a support system where users can report issues or request new features.

Throughout the development process, make sure to leverage the 'axonflow-google-adk-plugin' package effectively. Use its core features to streamline interactions with Google's ADK, focusing on ease-of-use and robustness. Your final product should not only demonstrate technical proficiency but also showcase creativity in solving real-world problems related to conversational AI.

💬 Discussion Feed

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