AI Analysis
The package has low individual risk factors such as no network calls, shell executions, obfuscations, or credential risks. However, the incomplete author details and possibly inactive account raise concerns about its legitimacy.
- Incomplete author details
- Possibly inactive account
Per-check LLM notes
- Network: No network calls suggest the package is not attempting to communicate externally without reason.
- Shell: No shell execution patterns indicate the package does not attempt to run external commands.
- 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 account seems new or inactive, which raises some suspicion but not enough to conclusively identify it as malicious.
Package Quality Overall: Low (3.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Brief PyPI description (306 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Repository CoreOxide/aws_resource_validator appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a Python-based task scheduling application named 'SchedulerPro' that leverages the 'aws-resource-validator-scheduler' package to manage and validate AWS Scheduler tasks efficiently. The application should allow users to define, schedule, and monitor tasks using the AWS Scheduler service. Key functionalities include: 1. **Task Definition**: Users should be able to input task details such as target ARN, schedule expression, and input parameters. 2. **Validation**: Before scheduling, the application must validate the input against Pydantic models provided by 'aws-resource-validator-scheduler' to ensure all fields comply with AWS Scheduler requirements. 3. **Scheduling**: Upon successful validation, the application schedules the task using the AWS SDK (boto3). 4. **Monitoring**: Provide real-time monitoring capabilities to view the status of scheduled tasks including success/failure notifications. 5. **User Interface**: Implement a simple command-line interface (CLI) for ease of use. 6. **Documentation**: Include comprehensive documentation detailing setup, configuration, and usage of the application. The 'aws-resource-validator-scheduler' package plays a crucial role in ensuring data integrity and compliance with AWS Scheduler's specifications during the task definition and validation steps. This ensures that all scheduled tasks are correctly formatted and ready for execution without errors.
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