aws-resource-validator-timestream-query

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential handling. However, the incomplete author information suggests potential lack of transparency, raising suspicion.

  • Incomplete author information
  • No network calls detected, unusual for AWS interaction
Per-check LLM notes
  • Network: No network calls detected, which is not typical for a package interacting with AWS services but could be due to conditional logic or external dependencies.
  • Shell: No shell execution detected, which is expected as direct shell execution is uncommon in pure Python packages.
  • Obfuscation: No obfuscation patterns detected, suggesting normal code readability and functionality.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: The author information is incomplete, which raises some concern about the transparency and legitimacy of the package.

📦 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_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-timestream-query 
Create a mini-application called 'Timestream Query Analyzer' which will serve as a tool for developers and data analysts to query and analyze data stored in AWS Timestream using the 'aws-resource-validator-timestream-query' package. This application should be designed to simplify the process of querying time-series data and provide insights through visualizations. Here are the steps and features to include in your project:

1. **Setup**: Start by installing the necessary packages including 'aws-resource-validator-timestream-query', 'boto3' for AWS SDK, and 'matplotlib' for data visualization.
2. **Authentication**: Implement a secure way to authenticate users to their AWS account via AWS credentials or IAM roles. Ensure that only authorized users can access sensitive information.
3. **Data Querying**: Utilize the 'aws-resource-validator-timestream-query' package to define models for Timestream query requests. These models should validate and structure the queries according to the Timestream API specifications.
4. **Visualization**: Once the data is queried from Timestream, use 'matplotlib' to create visual representations such as line graphs, bar charts, etc., to help users understand trends over time.
5. **User Interface**: Develop a simple command-line interface (CLI) where users can input their query parameters, select visualization types, and view the results.
6. **Error Handling**: Implement robust error handling to manage issues like invalid queries, connectivity problems, and permission errors.
7. **Documentation**: Provide clear documentation on how to install the application, set up AWS credentials, and use the CLI effectively.

By following these steps, you'll create a powerful yet user-friendly tool that leverages the 'aws-resource-validator-timestream-query' package to make working with AWS Timestream more accessible and insightful.

💬 Discussion Feed

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