🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and lacks complete author information, raising concerns about its authenticity and purpose.

Potential obfuscation through base64 encoding
Incomplete author metadata

Per-check LLM notes

Obfuscation: The use of base64 encoding may indicate an attempt to obfuscate code, but it could also be used for legitimate purposes such as data encryption or transmission.
Credentials: No clear patterns indicating credential harvesting were detected.
Metadata: The author information is incomplete, which raises some suspicion, but there are no other red flags.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

✦ High Documentation 9.0

Well-documented package

Documentation URL: "Documentation" -> https://fluid.quantmind.com/
3 documentation file(s) (e.g. data_dispatcher.py)
Detailed PyPI description (3611 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

287 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 100 commits in quantmind/aio-fluid
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

x.AsyncClient: return httpx.AsyncClient(**kwargs) def new_response( self, response: htt

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

") cursor_bytes = base64.b64decode(base64_bytes) values, limit, filters, search_tex

✓ 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: quantmind.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository quantmind/aio-fluid appears legitimate

⚠ 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 aio-fluid

Create a fully functional mini-application called 'FluidTaskManager' using the Python package 'aio-fluid'. This application will serve as a task management tool for users to create, manage, and track their tasks efficiently. The application should have the following features:

1. User Authentication: Implement a simple user registration and login system.
2. Task Creation: Users should be able to create new tasks with details such as title, description, due date, and priority level.
3. Task Management: Allow users to edit, delete, and mark tasks as completed.
4. Task Filtering: Enable users to filter tasks based on their status (e.g., pending, completed).
5. Real-Time Updates: Ensure that changes made by one user are reflected in real-time for all users logged into the same task.
6. Notifications: Send notifications to users when tasks are marked as completed or when deadlines are approaching.
7. Data Persistence: Use a database to store user information and task details persistently.
8. User Interface: Develop a clean and intuitive web interface for interacting with the application.

The 'aio-fluid' package will be utilized to handle asynchronous operations and backend services efficiently. For example, use 'aio-fluid' to manage user sessions, handle task updates in real-time, and perform database operations asynchronously to ensure smooth and fast performance of the application. Additionally, leverage 'aio-fluid' tools to monitor and optimize the application's backend processes for scalability and reliability.