aws-resource-validator-autoscaling-plans

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network, shell, or obfuscation risks and no evidence of credential mishandling. However, the metadata risk suggests caution due to sparse author details.

  • No network or shell execution detected
  • Sparse author details increasing metadata risk
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The author details are sparse, indicating a potentially less experienced or inactive maintainer.

📦 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 (330 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-autoscaling-plans 
Create a Python-based utility named 'AutoScaleChecker' that leverages the 'aws-resource-validator-autoscaling-plans' package to validate and manage AWS Auto Scaling Plans. This tool will be designed to help DevOps engineers ensure their Auto Scaling Plans configurations are correct before deploying them into production environments.

Step-by-Step Guide:
1. Setup the Project Environment: Initialize a new Python project with a virtual environment and install necessary packages including 'aws-resource-validator-autoscaling-plans', 'boto3' for AWS interactions, and 'click' for command-line interface.
2. Define Configuration Models: Use 'aws-resource-validator-autoscaling-plans' to define Pydantic models that represent valid AWS Auto Scaling Plans configurations. These models should enforce specific rules and constraints based on AWS documentation.
3. Implement Validation Logic: Develop functions within your utility that take in configuration files (in JSON or YAML format) and use the defined models to validate these configurations. Provide feedback to the user about any errors found during validation.
4. Add CLI Commands: Utilize 'click' to create a simple yet powerful command-line interface where users can specify input files and get validation results.
5. Enhance with AWS Interactions: Extend the functionality to not only validate local configurations but also fetch existing Auto Scaling Plans from an AWS account and validate those against the models. This will help ensure that current live configurations adhere to best practices.
6. Optional Features: Consider adding additional functionalities such as:
   - Automatic correction suggestions for common issues found during validation.
   - Integration with CI/CD pipelines through hooks or direct API calls.
   - Support for multiple AWS regions and accounts.
7. Documentation & Testing: Write comprehensive documentation explaining how to use each feature and include examples. Also, develop unit tests for your validation logic and integration tests with real AWS resources.

By following these steps, you'll create a valuable tool that can significantly improve the quality and reliability of AWS Auto Scaling Plans configurations.

💬 Discussion Feed

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