AI Analysis
Final verdict: SAFE
The package is considered safe with minimal risks identified. It primarily provides Pydantic v2 models for AWS batch without engaging in network calls, executing shell commands, or obfuscating code.
- No network calls detected.
- No shell execution patterns.
- No credential harvesting patterns.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API interactions.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The package has some minor red flags but no clear indicators 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 (294 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-batch
Your task is to create a Python-based command-line utility named 'BatchJobValidator' that leverages the 'aws-resource-validator-batch' package to validate AWS Batch job definitions against predefined schemas. This tool will help developers ensure their job definitions are correctly formatted before deploying them to AWS Batch environments, reducing errors and improving deployment reliability. ### Project Scope: - **Input Parsing**: Accept JSON input representing AWS Batch job definitions via command-line arguments or standard input. - **Validation Logic**: Use 'aws-resource-validator-batch' to validate the provided job definitions against Pydantic models specific to AWS Batch resources. - **Output Generation**: Provide human-readable validation results, indicating whether each job definition is valid or not, and if invalid, specify the reasons why. - **Error Handling**: Gracefully handle potential exceptions such as malformed JSON inputs or issues with the validation process. ### Suggested Features: - **Interactive Mode**: Allow users to interactively enter job definitions through the CLI. - **File Input/Output**: Support reading from and writing to files for job definitions. - **Verbose Output**: Offer a verbose mode that provides detailed information about each validation check performed. - **Custom Schema Support**: Enable users to provide custom schemas for more tailored validation scenarios. - **Integration Testing**: Include a set of test cases to verify the functionality of your utility. ### Utilizing 'aws-resource-validator-batch': - **Model Definition**: Use the provided Pydantic models from 'aws-resource-validator-batch' to define the structure of valid AWS Batch job definitions. - **Validation Process**: Implement the validation logic using these models to parse and validate the incoming job definitions. - **Error Reporting**: Leverage the error messages generated by Pydantic to report back to the user any discrepancies found in the job definitions. Your goal is to develop a robust, user-friendly tool that simplifies the validation of AWS Batch job definitions, ensuring they meet the required standards before actual deployment.
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