amplpy-copt

v0.2.4 safe
3.0
Low Risk

copt extension for amplpy

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all checks with no signs of network calls, shell executions, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest a supply-chain attack.

  • No network calls
  • No shell executions
  • No obfuscation patterns
  • No credential harvesting patterns
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account; however, there are no other suspicious flags.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (791 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in ampl/ampls-api
  • Two distinct contributors found

🔬 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

Email domain looks legitimate: ampl.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://ampl.com/
✓ Git Repository History

Repository ampl/ampls-api appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Filipe Brandão" 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 amplpy-copt 
Create a Python-based application named 'OptiSolver' that leverages the 'amplpy-copt' package to solve complex linear programming problems. OptiSolver will serve as a user-friendly tool for both educational purposes and practical problem-solving in fields such as operations research, economics, and engineering.

Step 1: Set Up Your Environment
- Ensure you have Python installed along with pip.
- Install the required packages including 'amplpy-copt'.
- Create a virtual environment if needed for dependency management.

Step 2: Define the Application Structure
- Design a simple GUI using Tkinter for ease of use.
- Implement a backend that handles mathematical formulations and computations using amplpy-copt.

Step 3: Core Functionality Implementation
- Develop a function to parse input data from the user interface.
- Use amplpy-copt to formulate and solve the linear programming model based on the provided data.
- Display the solution back to the user through the GUI in an understandable format.

Suggested Features:
- Support for different types of linear constraints (equalities, inequalities).
- Ability to handle large datasets efficiently.
- Visualization of the solution space and optimal solution path using matplotlib.
- Export results to CSV or Excel for further analysis.

Utilization of 'amplpy-copt':
- Utilize amplpy-copt to define variables, objective functions, and constraints in your models.
- Leverage its capabilities to solve these models and retrieve solutions.
- Explore advanced features like sensitivity analysis and scenario planning.

Your goal is to create a versatile tool that not only solves linear programming problems but also educates users about the process and importance of optimization in decision-making.

💬 Discussion Feed

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