aws-resource-validator-sagemaker

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with it, with no network calls, shell executions, obfuscations, or credential harvesting attempts observed. The metadata risk is slightly elevated due to incomplete maintainer information.

  • Low network and shell execution risk
  • No signs of obfuscation or credential harvesting
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access to function.
  • Shell: No shell execution patterns detected, indicating that the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete and they may be new or inactive, 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 (306 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-sagemaker 
Create a Python-based utility named 'SageMakerResourceChecker' that leverages the 'aws-resource-validator-sagemaker' package to validate and manage SageMaker resources within an AWS environment. This utility will serve as a tool for developers and system administrators to ensure their SageMaker resources adhere to best practices and comply with specific organizational standards. Here are the key functionalities your application should include:

1. **Resource Validation**: Implement a feature that allows users to input details of their SageMaker resources (such as training jobs, endpoints, or models). Utilize the Pydantic models provided by 'aws-resource-validator-sagemaker' to validate these inputs against predefined schemas. Ensure that the validation process checks for completeness, correctness, and compliance with AWS guidelines.

2. **Compliance Reporting**: Once resources are validated, generate detailed reports indicating whether each resource meets the specified criteria. These reports should highlight any discrepancies or issues found during the validation process.

3. **Interactive CLI Interface**: Develop a command-line interface (CLI) that enables users to interact with 'SageMakerResourceChecker'. Users should be able to add, validate, and review resources through simple commands. For example, commands like `validate-resource`, `list-resources`, and `generate-report` should be available.

4. **Configuration Management**: Allow users to configure the utility according to their needs. Users should be able to specify custom validation rules, report formats, and other preferences via configuration files or command-line options.

5. **Integration with AWS SDKs**: To fetch existing SageMaker resources for validation, integrate 'SageMakerResourceChecker' with the official AWS SDK for Python (Boto3). This integration should streamline the process of fetching resources from AWS accounts and validating them against the defined schemas.

6. **User-Friendly Documentation**: Provide comprehensive documentation that guides users on how to install, configure, and use 'SageMakerResourceChecker'. Include examples, best practices, and troubleshooting tips to enhance user experience.

The 'aws-resource-validator-sagemaker' package plays a crucial role in this utility by providing pre-built Pydantic models tailored specifically for SageMaker resources. These models ensure that all inputs are correctly formatted and comply with AWS standards, making it easier for users to maintain high-quality resources in their AWS environments.

💬 Discussion Feed

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