AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity, with low risks across all categories assessed. The only notable concern is the metadata risk due to the maintainer having only one package, suggesting they may be new or less active.
- No network calls or shell executions detected
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
- Metadata: The maintainer has only one package, indicating a new or less active account, but no other suspicious activities were flagged.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (8924 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Alexy Grabov" 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
Create a command-line utility named 'AWS Resource Validator' using Python that leverages the 'aws-resource-validator' package. This tool will help users validate AWS resource names and ensure they adhere to AWS naming conventions across various services. The application should offer several functionalities: 1. **Resource Validation**: Users can input an AWS resource name (e.g., S3 bucket name, RDS instance name), and the application will validate if it conforms to AWS naming rules. It should also provide feedback on which specific AWS service's naming convention the name fails to meet. 2. **Pattern Generation**: For any given AWS service, the application should be able to generate a pattern or regex that represents the valid naming format for resources of that service. 3. **Model Creation**: Given a specific AWS service, the application should generate Pydantic v2 models that mirror the typed dictionaries found in boto3 for that service, allowing for easier data handling and validation within Python applications. 4. **Interactive Mode**: Implement an interactive mode where the user can explore different AWS services and their respective naming conventions and models. 5. **Batch Processing**: Allow users to validate multiple resource names at once from a file or stdin. The 'aws-resource-validator' package should be used extensively throughout the application to handle all AWS-specific validations, patterns, and model generation tasks. Ensure the codebase is well-documented, modular, and follows best practices for Python development.
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