allsolve

v0.4.4 suspicious
6.0
Medium Risk

Quanscient Allsolve SDK

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential external communications and questionable metadata, including low repository activity and sparse commit history.

  • Moderate network risk due to HTTP session establishment
  • Concerning metadata and maintenance practices
Per-check LLM notes
  • Network: The observed network behavior indicates the package establishes HTTP sessions with custom User-Agent headers, which could be benign but might also indicate external communication.
  • Shell: No shell execution patterns were detected, suggesting low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's low activity and sparse commit history raise concerns, along with the maintainer's lack of a proper author name and limited package history.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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 (2435 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
  • 721 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 2 commits in Quanscient-Public/allsolve-sdk-python
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • lient is None: return requests.Session() return client._http_session def get_allow_insecure_h
  • self._http_session = requests.Session() self._http_session.headers["User-Agent"] = f"allso
✓ 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: quanscient.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
⚠ 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 allsolve 
Create a fully-functional mini-application that leverages the Quanscient Allsolve SDK (Python package 'allsolve') to solve complex mathematical problems, particularly focusing on differential equations and optimization tasks. This application will serve as a user-friendly tool for mathematicians, engineers, and scientists who need to quickly analyze and solve complex mathematical models.

### Application Overview:
- **Name:** MathSolver Pro
- **Core Functionality:** Solving differential equations and optimization problems.
- **Target Users:** Mathematicians, engineers, and scientists.
- **Features:**
  - User-friendly interface for inputting mathematical models.
  - Real-time visualization of solutions.
  - Support for both initial value problems and boundary value problems.
  - Optimization tools for finding optimal solutions under given constraints.
  - Integration with popular scientific libraries for enhanced functionality.
  - Detailed documentation and examples for easy understanding.

### Steps to Develop the Application:
1. **Setup Environment:**
   - Install necessary Python packages including 'allsolve' and any other dependencies like matplotlib for plotting and numpy for numerical operations.
2. **Design User Interface:**
   - Create a simple yet effective GUI using Tkinter or PyQt for users to input their mathematical models.
3. **Implement Core Functionality:**
   - Utilize the 'allsolve' package to define and solve differential equations and optimization problems based on user inputs.
4. **Add Visualization Features:**
   - Implement real-time plotting of solutions using matplotlib or similar libraries to provide visual feedback.
5. **Enhance with Additional Tools:**
   - Integrate additional functionalities such as solving systems of equations, handling partial differential equations, and performing sensitivity analysis.
6. **Testing and Validation:**
   - Conduct thorough testing with various mathematical models to ensure accuracy and reliability.
7. **Documentation and Examples:**
   - Provide comprehensive documentation and example cases to guide users through different functionalities.
8. **Deployment:**
   - Package the application for distribution and make it available for download.

### How 'allsolve' is Utilized:
- For solving differential equations, users will input the equation along with initial/boundary conditions. The application will then use 'allsolve' to compute the solution, which can be displayed graphically.
- For optimization tasks, users can specify the objective function and constraints. The application will leverage 'allsolve' to find the optimal solution and present it alongside relevant metrics.

This project aims to showcase the power and versatility of the 'allsolve' package while providing a practical tool for solving real-world mathematical challenges.

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