AI Analysis
Final verdict: SUSPICIOUS
The package has minimal direct risks associated with network calls, shell execution, obfuscation, and credential handling. However, the incomplete author information and possibly inactive account raise concerns about potential supply-chain risks.
- Incomplete author information
- Possibly inactive account
Per-check LLM notes
- Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity for a package focused on local validation tasks.
- Shell: No shell execution patterns detected, aligning with expectations for a package designed to validate AWS resources without requiring system-level permissions.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.
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 (327 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-lookoutequipment
Create a Python-based utility named 'EquipmentHealthChecker' that leverages the 'aws-resource-validator-lookoutequipment' package to validate and manage AWS Lookout for Equipment resources. This utility will serve as a robust tool for DevOps engineers and system administrators to ensure their AWS Lookout for Equipment configurations are accurate and adhere to best practices. The project aims to streamline the validation process and provide actionable insights into resource compliance. Step 1: Set up your development environment with Python 3.8+ and install the necessary packages including 'aws-resource-validator-lookoutequipment'. Step 2: Define a CLI interface using Click or argparse to allow users to interact with the utility easily. Step 3: Implement functionality to read AWS Lookout for Equipment configuration files (in JSON or YAML format) and use the 'aws-resource-validator-lookoutequipment' package to validate these configurations against Pydantic v2 models provided by the package. Ensure that the validation process checks for common errors and missing fields. Step 4: Add support for auto-correction of minor issues detected during validation. For instance, if a field is missing, the utility could automatically add it with default values where applicable. Step 5: Integrate logging and error handling to provide detailed feedback to the user about the validation process, including success messages, warnings, and errors. Step 6: Extend the utility to include a feature that generates a summary report after validation, highlighting any discrepancies between the current configuration and best practices. Suggested Features: - Support for multiple input file formats (JSON, YAML) - Option to validate configurations against specific versions of AWS Lookout for Equipment - Detailed documentation and examples for easy integration into CI/CD pipelines - Integration tests to ensure the utility works as expected across different scenarios How to Utilize 'aws-resource-validator-lookoutequipment': This package provides Pydantic v2 models that represent AWS Lookout for Equipment resources. These models are used within the utility to define the structure and constraints of valid AWS Lookout for Equipment configurations. By leveraging these models, the utility can perform comprehensive validation checks, ensuring that configurations meet the required standards. Additionally, the package simplifies the process of updating the utility to support new AWS Lookout for Equipment features by allowing you to update the Pydantic models without needing to modify extensive validation logic.
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