aws-resource-validator-kinesisanalyticsv2

v2.0.3 safe
3.0
Low Risk

Pydantic v2 models for AWS kinesisanalyticsv2, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all checks with only minor concerns regarding metadata. It does not exhibit any signs of malicious behavior or supply-chain attack.

  • No network calls or shell executions detected
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity without further context.
  • Shell: No shell execution patterns detected, reducing immediate risk of system compromise.
  • Obfuscation: No obfuscation patterns detected, suggesting normal code readability and no hidden malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: The maintainer's author information is incomplete, which raises some concern, but there are no other red flags.

📦 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 (333 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-kinesisanalyticsv2 
Your task is to develop a Python-based mini-application named 'KinesisAnalyticsValidator'. This tool will serve as a comprehensive resource validator for AWS Kinesis Analytics V2, utilizing the 'aws-resource-validator-kinesisanalyticsv2' package. The application should allow users to input various configurations related to AWS Kinesis Analytics V2 resources and validate these configurations against predefined schemas provided by the 'aws-resource-validator-kinesisanalyticsv2' package. The goal is to ensure that the configurations adhere to the correct structure and constraints expected by AWS Kinesis Analytics V2 services, thereby preventing errors during deployment.

### Application Features:
1. **Configuration Input**: Users should be able to input their AWS Kinesis Analytics V2 configurations either via command line arguments or a simple JSON file upload.
2. **Validation Engine**: Utilize the 'aws-resource-validator-kinesisanalyticsv2' package to validate the input configurations. Ensure that all fields are correctly formatted, required fields are present, and optional fields are within allowed ranges/types.
3. **Error Reporting**: Provide a detailed error report if the configuration fails validation. The report should include specific field names and descriptions of why they failed validation.
4. **Success Confirmation**: If the configuration passes validation, the application should confirm success and optionally provide a summary of the validated configuration.
5. **User-Friendly Interface**: Implement a user-friendly command-line interface (CLI) that guides users through the validation process with clear instructions and feedback.
6. **Customizable Validation Rules**: Allow advanced users to customize validation rules by providing additional schema definitions or modifying existing ones through command line arguments.
7. **Integration with AWS SDK**: Optionally, integrate with the AWS SDK for Python (Boto3) to directly validate configurations against live AWS Kinesis Analytics V2 resources.

### Steps to Build the Application:
1. **Set Up Your Development Environment**: Install Python and necessary libraries including 'aws-resource-validator-kinesisanalyticsv2', 'pydantic', and 'boto3'.
2. **Design the CLI Interface**: Use Python's argparse module to create a CLI that accepts configuration files and other relevant parameters.
3. **Implement Configuration Parsing**: Develop functions to parse JSON configuration files into Python objects.
4. **Integrate Validation Logic**: Use the 'aws-resource-validator-kinesisanalyticsv2' package to define and apply validation logic to parsed configurations.
5. **Handle Validation Results**: Implement logic to handle validation results, generating appropriate output based on whether the configuration passed or failed validation.
6. **Test Thoroughly**: Test your application with different configurations to ensure it accurately validates and reports errors.
7. **Document and Package**: Write clear documentation for users and package your application using tools like setuptools for easy distribution.

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