ajolopy

v0.1.8 suspicious
4.0
Medium Risk

The Python framework for building AI-native applications in production.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to suspicious metadata and a new maintainer with limited activity. However, there are no immediate indications of malicious intent or significant security risks like shell execution or credential theft.

  • Suspicious non-HTTPS links in metadata
  • New maintainer with minimal activity
Per-check LLM notes
  • Network: The presence of network calls might be legitimate if the package requires internet connectivity for its functionality, but it should be verified if the purpose is clear and necessary.
  • Shell: No shell execution patterns were detected, which is a positive sign.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package has suspicious non-HTTPS links and a new maintainer with minimal activity, indicating potential risk.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 14 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11293 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 272 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in jcocano/ajolopy
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • eck timeout. """ with socket.create_connection((host, port), timeout=_NETWORK_TIMEOUT_S): # Connect
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000/chat
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 ajolopy 
Create a mini-application named 'AI-TaskManager' using the Python package 'ajolopy'. This application should serve as a robust platform for managing tasks and projects with AI-driven enhancements. The app will leverage 'ajolopy' to handle task creation, prioritization, due dates, and reminders while incorporating AI for predicting task completion times based on historical data and user behavior patterns.

Step-by-Step Guide:
1. Initialize the project by setting up a virtual environment and installing 'ajolopy'.
2. Design the database schema to store tasks, projects, users, and historical task completion times.
3. Implement user authentication and authorization to ensure secure access to task and project management features.
4. Develop CRUD operations for tasks and projects using 'ajolopy', ensuring that each operation integrates AI predictions for better task management.
5. Integrate a feature that allows users to input task details, including due dates, priorities, and descriptions. Use 'ajolopy' to predict completion times based on these inputs and historical data.
6. Create a calendar view that displays upcoming tasks and projects, with AI-generated reminders sent via email or SMS based on predicted completion times.
7. Implement a dashboard that provides insights into productivity, such as average time spent on similar tasks, most productive days of the week, etc., utilizing 'ajolopy' for real-time analytics.
8. Ensure the application supports multi-user environments, allowing team members to collaborate on projects and tasks.
9. Test the application thoroughly to ensure all features work as expected and are integrated seamlessly with 'ajolopy'.
10. Deploy the application to a cloud service provider like AWS, Azure, or Google Cloud, making sure it scales well and remains secure.

Suggested Features:
- Task Prioritization: Allow users to prioritize tasks and automatically reorder them based on urgency and deadlines.
- Project Milestones: Enable users to set milestones within projects and receive notifications when they are approaching or have been met.
- Historical Data Analysis: Provide users with historical data analysis to help them make informed decisions about future task management.
- AI-Driven Recommendations: Offer personalized recommendations based on user behavior and preferences to optimize their workflow.
- Mobile Compatibility: Ensure the application is accessible and fully functional on mobile devices, enhancing user experience.

How 'ajolopy' is Utilized:
- For handling the backend logic of task and project management, including the integration of AI algorithms for prediction and recommendation systems.
- To manage user data securely and efficiently, leveraging 'ajolopy's built-in security features.
- For implementing real-time analytics and reporting functionalities, which are critical for providing valuable insights to users.
- To facilitate seamless deployment and scaling of the application, ensuring it can handle a growing number of users and tasks.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!