axmp-ai-agent-core

v1.1.0 safe
3.0
Low Risk

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🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no direct evidence of malicious intent. However, there are some minor concerns regarding network security and developer experience.

  • network risk due to lack of context around token handling
  • low developer activity based on metadata
Per-check LLM notes
  • Network: The use of httpx for network calls is common and suggests legitimate HTTP requests, but the lack of context around token handling could indicate potential exposure risks.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package and there are no PyPI classifiers, indicating low effort or possibly an inexperienced developer.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14696 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

  • 322 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • for tokens async with httpx.AsyncClient( verify=self._core_settings.http_ssl_verify
  • Profile() async with httpx.AsyncClient( verify=self._core_settings.http_ssl_verify
  • try: async with httpx.AsyncClient( verify=self._core_settings.http_ssl_verify
  • l file_data = httpx.get(file.upload_url).content # encode and decode
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Kilsoo Kang" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with axmp-ai-agent-core 
Create a personalized AI-driven task management system using the 'axmp-ai-agent-core' Python package. This system will help users organize their daily tasks and provide intelligent suggestions based on user behavior and preferences. Here’s a detailed breakdown of what the application should achieve and how it will utilize the 'axmp-ai-agent-core' package:

1. **User Registration & Login**: Allow users to register and log into the system securely.
2. **Task Creation & Management**: Users can create, edit, delete, and categorize tasks. Each task should have fields such as title, description, due date, priority level, and category.
3. **Intelligent Task Suggestion**: Utilize the 'axmp-ai-agent-core' package to analyze user behavior patterns and suggest tasks based on historical data and current context. For example, if a user frequently completes tasks related to work during morning hours, the system should suggest similar tasks for mornings.
4. **Reminders & Notifications**: Implement a notification system that sends reminders before the due dates of tasks. Use the 'axmp-ai-agent-core' to enhance these notifications by personalizing them based on the user’s interaction history with the app.
5. **Integration with Calendar Apps**: Allow integration with popular calendar applications like Google Calendar to sync task deadlines and updates.
6. **Analytics Dashboard**: Provide users with an analytics dashboard that shows task completion rates, time spent on each task, and other productivity metrics.
7. **Customization Options**: Let users customize the look and feel of the app, including themes and interface layouts.
8. **Feedback Loop**: Incorporate a feedback loop where users can rate the accuracy and usefulness of the task suggestions provided by the AI agent.

To implement the above features, you’ll need to leverage the 'axmp-ai-agent-core' package for its machine learning capabilities, which will enable the system to learn from user interactions and improve over time. Specifically, use the package to train models on user data, predict task suggestions, and refine the notification system based on user feedback. Ensure that all sensitive data is handled securely and complies with relevant privacy regulations.

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