aws-resource-validator-aiops

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows very low risk across all key indicators with no network or shell activity, and no signs of obfuscation or credential harvesting. The metadata risk is slightly elevated due to limited author information.

  • No network calls detected
  • No shell execution patterns
  • No obfuscation patterns
  • No credential harvesting patterns
  • Sparse author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is sparse, indicating potential unreliability, but no clear signs of malicious intent.

📦 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 (294 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-aiops 
Create a fully functional mini-application called 'AWS AIOPS Resource Validator' using the Python package 'aws-resource-validator-aiops'. This tool aims to validate and ensure the integrity of AWS AIOPS resources based on predefined schemas provided by the package. The application should include a command-line interface (CLI) for ease of use. Here are the steps and features your project should implement:

1. **Setup**: Install the necessary dependencies including 'aws-resource-validator-aiops' and any other required Python packages such as boto3 for AWS interaction.
2. **Configuration**: Allow users to configure their AWS credentials either through environment variables or a configuration file. Ensure these credentials are securely handled.
3. **Resource Validation**: Implement functions that utilize the pydantic models from 'aws-resource-validator-aiops' to validate various AWS AIOPS resources. This includes but is not limited to OpsCenter, Anomaly Detectors, and Insights.
4. **CLI Commands**: Develop CLI commands for each type of resource validation. For example, `validate_opscenter`, `validate_anomaly_detector`, etc.
5. **Output Reporting**: After validating resources, provide a detailed report on the CLI indicating which resources passed the validation and which ones did not. Include suggestions for fixing any issues found.
6. **Interactive Mode**: Offer an interactive mode where users can input specific details about their resources and get real-time feedback on the validity of those inputs against the schema.
7. **Documentation**: Provide comprehensive documentation on how to install, configure, and use the application effectively.
8. **Testing**: Write unit tests for all validation functions to ensure they work correctly under different scenarios.

By following these guidelines, you will create a robust and user-friendly tool that leverages the power of 'aws-resource-validator-aiops' to maintain high standards in AWS AIOPS resource management.

💬 Discussion Feed

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