amfs-crewai

v0.1.0 suspicious
4.0
Medium Risk

Use AMFS as CrewAI Memory storage backend

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential suspicious metadata, despite showing no direct signs of malicious activity such as network calls, shell execution, or obfuscation.

  • Metadata risk noted
  • Lack of description
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The package shows several low-effort and potentially suspicious signs, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 12 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

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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 amfs-crewai 
Create a crew management tool using the 'amfs-crewai' Python package, which serves as a memory storage backend for AMFS (Assisted Memory File System). This tool will streamline the process of managing a team's tasks, assignments, and communication. Here’s a detailed outline of the steps and features to implement:

1. **Project Setup**: Start by setting up a virtual environment and installing the 'amfs-crewai' package alongside other necessary dependencies such as Flask for web development.
2. **User Authentication**: Implement user registration and login functionalities. Utilize Flask-Security for handling user authentication and authorization processes.
3. **Crew Management**: Allow users to create and manage their teams. Each team should have a unique identifier and be stored in the AMFS backend via 'amfs-crewai'.
4. **Task Assignment**: Enable users to assign tasks to different members within their teams. Tasks should include details like task description, deadline, priority level, and status (e.g., pending, in progress, completed).
5. **Communication Interface**: Provide a simple messaging system where team members can communicate about ongoing tasks. Messages should be stored securely in AMFS.
6. **Analytics Dashboard**: Develop a dashboard that provides insights into team performance based on task completion rates, average response times, etc. This dashboard should leverage the data stored in AMFS to generate meaningful analytics.
7. **Integration with External Tools**: Consider integrating your application with popular productivity tools like Slack or Google Calendar, allowing seamless task synchronization and notifications.
8. **Testing & Deployment**: Thoroughly test the application for functionality, security, and usability. Once satisfied, deploy it on a cloud platform such as Heroku or AWS.

For each feature, ensure you're utilizing the 'amfs-crewai' package effectively to store and retrieve data related to crew management, task assignment, and communication. This will showcase the power and efficiency of AMFS as a memory storage backend for complex applications.

💬 Discussion Feed

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