aws-resource-validator-forecastquery

v2.0.3 suspicious
4.0
Medium Risk

Pydantic v2 models for AWS forecastquery, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, and obfuscation activities. However, the incomplete author information and potential inactivity of the maintainer raise concerns about its legitimacy.

  • Incomplete author information
  • Potential inactivity of the maintainer
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 execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate usage without compromising secrets.
  • Metadata: The author information is incomplete and the maintainer seems to be 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 (318 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_validator
  • Small 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 short
  • Author "" 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-forecastquery 
Create a mini-application named 'ForecastQueryAnalyzer' using Python that leverages the 'aws-resource-validator-forecastquery' package to validate and query Amazon Forecast resources efficiently. This tool will serve as a bridge between developers and AWS Forecast, ensuring that the resources they manage adhere to best practices and standards. The application should include the following functionalities:

1. **Resource Validation**: Implement a feature where users can input details of their AWS Forecast resources (such as Dataset Groups, Predictors, etc.) through a command-line interface (CLI). Use the 'aws-resource-validator-forecastquery' package to validate these inputs against predefined Pydantic models, ensuring they meet the necessary criteria for successful deployment on AWS Forecast.

2. **Query Execution**: Once validated, allow users to execute queries on their Forecast resources directly from the CLI. These queries could range from simple status checks to more complex data retrieval tasks, all utilizing the ForecastQuery API provided by AWS.

3. **Result Presentation**: After executing queries, present the results in a user-friendly manner. This could involve formatting the output for better readability, such as displaying it in a tabular format or exporting it to a CSV file.

4. **Error Handling & Logging**: Integrate robust error handling mechanisms to catch and report any issues encountered during resource validation or query execution. Additionally, implement logging to track operations performed by the application, which can be useful for debugging and auditing purposes.

5. **Interactive Mode**: Offer an interactive mode where users can explore different validation and querying options without needing to provide full input parameters upfront. This could enhance usability, especially for those new to AWS Forecast.

6. **Configuration Management**: Allow users to save their preferred configurations (e.g., default region, output format) so they don't have to re-enter them each time they use the tool.

By utilizing the 'aws-resource-validator-forecastquery' package, you ensure that your application not only interacts seamlessly with AWS Forecast but also maintains high standards of data integrity and operational efficiency. Your task is to design and implement this application, adhering to Pythonic principles and best practices, making it both powerful and accessible.

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