agentwatcher

v0.1.0 suspicious
4.0
Medium Risk

DevTools overlay for browser-based AI agents — see what your agent is doing in real time.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risk factors based on the current analysis, but its metadata suggests it was recently created by a single contributor with limited history, which raises some concerns.

  • Metadata risk score is elevated due to limited historical data and single contributor.
  • No significant technical risks detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • 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 shows signs of being newly created and maintained by a single contributor with limited history, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4392 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

  • 4 type-annotated function signatures (partial)
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 4 commits in yubinkim444/agentwatch
  • Single author with few commits — possibly a personal or throwaway project

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Single contributor with only 4 commit(s) — possibly throwaway account

  • Single contributor with only 4 commit(s) — possibly throwaway account
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "yubinkim444" 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 agentwatcher 
Create a real-time monitoring tool for browser-based AI agents using the 'agentwatcher' Python package. This tool will allow users to observe the behavior and actions of their AI agents directly within the browser environment. Here are the steps and features to include in the project:

1. **Setup Environment**: Ensure you have Python installed on your system and create a virtual environment. Install necessary packages including 'agentwatcher'.
2. **Initialize Project Structure**: Create a directory for your project and set up basic files such as `requirements.txt`, `README.md`, and a main script file.
3. **Integrate 'agentwatcher'**: Use 'agentwatcher' to initialize a monitoring session for your AI agent. Configure it to log actions performed by the agent in real-time.
4. **Develop User Interface**: Design a simple user interface that displays the live feed of the agent's activities. This could include visual elements like graphs, charts, or console logs depending on the type of data being monitored.
5. **Implement Real-Time Updates**: Ensure the UI updates in real-time as the agent performs actions. This involves setting up websockets or similar technology for continuous data streaming.
6. **Add Customization Options**: Allow users to customize the monitoring experience by choosing which actions to track or how to visualize the data.
7. **Testing and Debugging**: Thoroughly test the application to ensure all components work seamlessly together. Fix any bugs that arise during testing.
8. **Documentation and Deployment**: Write comprehensive documentation explaining how to install and use the application. Consider deploying the application to a cloud service for public access.

By following these steps, you'll create a powerful and user-friendly tool for developers and researchers working with browser-based AI agents.