aws-resource-validator-marketplace-discovery

v2.0.3 safe
3.0
Low Risk

Pydantic v2 models for AWS marketplace_discovery, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SAFE

The package presents minimal risks based on the analysis. It does not engage in network calls, shell executions, or any form of obfuscation that might indicate malicious activity.

  • No network calls
  • No shell execution
  • No obfuscation patterns
  • Low credential risk
Per-check LLM notes
  • Network: No network calls detected, which is not unusual if the package is purely local and does not interact with external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution, which is safe.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate use without hidden malicious activities.
  • Metadata: The maintainer has a new or inactive account with minimal package history and no author name provided.

📦 Package Quality Overall: Low (3.8/10)

○ 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 (342 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
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 75 commits in CoreOxide/aws_resource_validator
  • Small but multi-author team (3–4 contributors)

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository CoreOxide/aws_resource_validator 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 aws-resource-validator-marketplace-discovery 
Create a Python-based utility named 'MarketplaceResourceInspector' that leverages the 'aws-resource-validator-marketplace-discovery' package to validate AWS Marketplace resources against predefined Pydantic v2 models. This tool will serve as a robust validator for developers and system administrators who manage AWS Marketplace resources, ensuring they adhere to specific validation rules defined within the Pydantic models.

Step-by-Step Guide:
1. Set up the project structure, including a virtual environment, necessary dependencies, and a basic command-line interface (CLI).
2. Integrate the 'aws-resource-validator-marketplace-discovery' package into your project to access its Pydantic models.
3. Develop a function that takes input from the user (either via a file upload or direct CLI input) representing an AWS Marketplace resource configuration.
4. Implement a validation mechanism using the Pydantic models provided by 'aws-resource-validator-marketplace-discovery'.
5. Display the validation results to the user, indicating whether the resource configuration is valid according to the models.
6. Optionally, implement additional features such as:
   - Logging of validation activities for audit purposes.
   - Support for multiple validation scenarios based on different Pydantic models.
   - An interactive mode allowing users to correct configurations in real-time.
7. Write comprehensive documentation detailing how to use the tool, including examples and best practices.
8. Ensure the codebase is well-documented and follows PEP 8 style guidelines.
9. Test the application thoroughly under various scenarios to ensure reliability and accuracy.
10. Package the application as a standalone executable using tools like PyInstaller or similar, making it easily distributable.

💬 Discussion Feed

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