aws-resource-validator-wisdom

v2.0.3 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with low risks across all categories except metadata, where the maintainer's information is lacking.

  • No network calls detected
  • No shell execution patterns detected
  • Missing maintainer's author name
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The maintainer's author name is missing and they seem to be new or inactive, which raises some concern but not enough to strongly suggest 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 (297 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-wisdom 
Create a Python-based command-line tool named 'WisdomAudit' that leverages the 'aws-resource-validator-wisdom' package to audit AWS Wisdom resources. This tool should provide developers and system administrators with a comprehensive way to validate the configuration and structure of their AWS Wisdom assets such as knowledge bases, knowledge documents, and FAQs. The tool should be able to connect to AWS using Boto3 and the AWS CLI for authentication purposes. Here are the steps and features you need to implement:

1. **Setup**: Begin by installing the required packages including 'aws-resource-validator-wisdom', 'boto3', and 'click' for command-line interface operations.
2. **Authentication**: Implement a mechanism for users to authenticate their AWS credentials either via environment variables, AWS CLI profile, or directly through command-line input.
3. **Resource Validation**: Utilize the Pydantic v2 models provided by 'aws-resource-validator-wisdom' to define schemas for validating different types of AWS Wisdom resources. Ensure these models accurately reflect the expected structure and attributes of each resource type.
4. **Command-Line Interface**: Develop a CLI with commands like 'validate', 'list', and 'info'. 
   - 'validate': Validates the configuration of specified AWS Wisdom resources against the defined schemas.
   - 'list': Lists all available resources within a given knowledge base.
   - 'info': Provides detailed information about a specific resource.
5. **Output**: The output from each command should be clear and informative, ideally in a structured format like JSON or YAML.
6. **Error Handling**: Implement robust error handling to manage issues such as invalid inputs, missing credentials, or connection errors.
7. **Documentation**: Provide thorough documentation on how to install and use 'WisdomAudit', including examples of how to use each command.
8. **Testing**: Write tests to ensure that the validation logic works correctly for various scenarios.

This project aims to simplify the process of maintaining and auditing AWS Wisdom resources, ensuring they adhere to best practices and standards.

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