AI Analysis
Final verdict: SAFE
The package has minimal risks across all categories, with no detected network calls, shell executions, or obfuscation. However, the incomplete author metadata slightly increases the risk score.
- Incomplete author metadata
- New or inactive author
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, indicating no direct system command execution is performed.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's details are incomplete, and the author seems to be new or inactive, which raises some concern but not enough to suggest 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 (321 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-bcm-dashboards
Create a mini-application called 'Dashboard Validator' that leverages the 'aws-resource-validator-bcm-dashboards' Python package to validate AWS BCM Dashboards resources. This application should serve as a tool for developers and DevOps engineers to ensure their AWS BCM Dashboard configurations are valid according to AWS standards and best practices. Step-by-Step Guide: 1. Set up a basic Python environment with necessary dependencies including 'aws-resource-validator-bcm-dashboards'. 2. Develop a function that accepts an AWS BCM Dashboard configuration file as input. 3. Use the Pydantic models from 'aws-resource-validator-bcm-dashboards' to validate the configuration file against the AWS BCM Dashboard schema. 4. Implement error handling to provide clear feedback if the configuration file does not meet the required standards. 5. Extend the functionality to support command-line interaction, allowing users to run the validator on their dashboard configurations directly from the terminal. 6. Optionally, integrate logging to keep track of validation results and any errors encountered. 7. Write comprehensive documentation for the application, detailing how to install it, use it, and interpret its output. 8. Test the application thoroughly using various AWS BCM Dashboard configurations, including edge cases and invalid inputs. Suggested Features: - Support for multiple input formats (e.g., JSON, YAML). - Ability to validate configurations against different versions of the AWS BCM Dashboard API. - Option to automatically correct minor issues in the configuration files, such as missing fields or incorrect data types. - Integration with popular CI/CD pipelines like Jenkins or GitHub Actions for automated validation during the development process. How 'aws-resource-validator-bcm-dashboards' is Utilized: - The package provides Pydantic v2 models which define the structure and rules for AWS BCM Dashboard configurations. These models will be used to validate the input configurations, ensuring they adhere to the expected format and constraints set by AWS. - By leveraging these models, your application can perform robust validation checks, catching potential misconfigurations before they cause issues in production environments.
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