aws-resource-validator-machinelearning

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with minimal risks across all categories checked. The metadata risk slightly increases due to sparse author information, but this alone does not indicate malicious intent.

  • No network or shell execution detected
  • No obfuscation or credential harvesting patterns found
  • Sparse author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is supposed to interact with AWS services.
  • Shell: No shell execution patterns detected, which is expected and safe.
  • 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 lack of transparency, 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 (324 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-machinelearning 
Develop a Python-based CLI tool named 'MLResourceChecker' that leverages the 'aws-resource-validator-machinelearning' package to validate AWS Machine Learning resources against predefined schemas. This tool should allow users to specify AWS Machine Learning resource types such as MLModel, Evaluation, and BatchPrediction, and it should validate these resources based on their configurations. The validation process will ensure that the provided configurations adhere to AWS standards and best practices.

The project should include the following key components:
1. **Resource Configuration Input**: Users should be able to input the configuration details of AWS Machine Learning resources either via command line arguments or a JSON file.
2. **Validation Logic**: Implement validation logic using the Pydantic models from the 'aws-resource-validator-machinelearning' package to check if the provided configurations are valid according to AWS specifications.
3. **Output Feedback**: Provide clear feedback to the user indicating whether the resource configurations are valid or not. If invalid, specify which fields or values are incorrect.
4. **Error Handling**: Gracefully handle errors such as missing required fields, invalid data types, and unsupported resource types.
5. **Documentation**: Include comprehensive documentation on how to use the CLI tool, including examples of valid and invalid configurations.

This project aims to simplify the process of validating AWS Machine Learning resources, ensuring they meet AWS standards before deployment, thus reducing potential errors and improving the reliability of ML workflows.

💬 Discussion Feed

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