AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, obfuscation, or credential harvesting attempts. However, the metadata risk score is elevated due to the maintainer's new or inactive account.
- No network calls detected
- Maintainer has a new or inactive account
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution detected, which is expected as typical Python packages do not execute shell commands.
- Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The maintainer has a new or inactive account and lacks a full author name, which could indicate potential unreliability.
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 (369 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_validatorSmall 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 shortAuthor "" 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-featurestore-runtime
Create a Python-based utility that leverages the 'aws-resource-validator-sagemaker-featurestore-runtime' package to validate and manage resources for Amazon SageMaker Feature Store. This utility will serve as a robust tool for developers and data scientists working with SageMaker Feature Store, ensuring that their configurations adhere to best practices and AWS standards. ### Key Features: 1. **Resource Validation**: Implement a feature that validates various configurations for SageMaker Feature Store resources such as Feature Groups, Record Rows, and Feature Definitions using the Pydantic models provided by 'aws-resource-validator-sagemaker-featurestore-runtime'. 2. **Configuration Exporter**: Develop a functionality that exports validated configurations into a YAML file for easy sharing and deployment across different environments. 3. **Interactive CLI**: Build a command-line interface (CLI) that allows users to interactively validate their configurations, export them, and view detailed validation reports. 4. **Error Handling & Reporting**: Ensure that the utility provides clear, actionable error messages and detailed reports when configurations fail validation checks. 5. **Documentation & Examples**: Include comprehensive documentation and example configurations to help new users get started quickly. ### Steps to Create the Utility: 1. **Setup Project Structure**: Initialize a new Python project and install necessary dependencies including 'aws-resource-validator-sagemaker-featurestore-runtime'. 2. **Model Validation Logic**: Utilize the Pydantic models from 'aws-resource-validator-sagemaker-featurestore-runtime' to implement validation logic for different types of SageMaker Feature Store resources. 3. **CLI Development**: Use a library like Click or Argparse to develop a user-friendly CLI that supports the above-mentioned features. 4. **Export Configuration Functionality**: Implement a function that takes validated configurations and exports them into a YAML file. 5. **Testing & Documentation**: Write unit tests for your validation logic and ensure all features work as expected. Document the utility thoroughly, providing examples and explanations for each feature. 6. **Deployment**: Prepare the utility for deployment, ensuring it can be easily installed via pip and run on different systems. ### How 'aws-resource-validator-sagemaker-featurestore-runtime' is Utilized: - **Validation Models**: The package provides pre-defined Pydantic models that correspond to SageMaker Feature Store resource configurations. These models are used to validate user inputs against AWS standards and best practices. - **Namespace Extension**: By leveraging the PEP 420 namespace extension feature, the package integrates seamlessly into your project, allowing you to import and utilize its models directly without any additional setup.
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