aws-resource-validator-appflow

v2.0.3 safe
4.0
Medium Risk

Pydantic v2 models for AWS appflow, 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 calls, shell executions, or credential harvesting attempts. However, the sparse metadata and unusual absence of network activity for an AWS tool slightly increase its risk score.

  • No network calls detected, which is unusual for an AWS-related tool.
  • Sparse author information.
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious but unusual for an AWS-related tool.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of credential theft.
  • Metadata: The author information is sparse, indicating potential lack of transparency.

📦 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 (300 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-appflow 
Create a Python-based CLI tool named 'AppFlowValidator' that leverages the 'aws-resource-validator-appflow' package to validate and manage AWS AppFlow resources efficiently. This tool will serve as a robust utility for developers and DevOps teams looking to ensure their AppFlow configurations adhere to best practices and standards.

Step 1: Initialize your project by setting up a virtual environment and installing necessary packages including 'aws-resource-validator-appflow'.

Step 2: Design the command-line interface (CLI) using a library like 'Click' to facilitate user interaction and input. Ensure the CLI supports subcommands such as 'validate', 'list', and 'describe' for different functionalities.

Step 3: Implement the 'validate' subcommand which takes an AppFlow configuration file (in JSON or YAML format) as input. Use the Pydantic models provided by 'aws-resource-validator-appflow' to parse and validate the configuration against AWS AppFlow's schema. Provide meaningful error messages if validation fails.

Step 4: Add functionality to the 'list' subcommand to fetch and display all AppFlow flows within a specified AWS account and region. Utilize the 'aws-resource-validator-appflow' package to filter and organize the data before presenting it to the user.

Step 5: Integrate the 'describe' subcommand to provide detailed information about a specific AppFlow flow. The output should include configuration details, status, and any other relevant metadata parsed through the Pydantic models.

Suggested Features:
- Support for both JSON and YAML configuration files.
- Detailed logging and reporting options.
- Integration with AWS CLI for seamless authentication and access.
- Customizable validation rules based on user-defined policies.
- Ability to export validation results and descriptions to CSV or JSON formats.

The 'aws-resource-validator-appflow' package is crucial in this project as it provides the necessary Pydantic models to accurately represent and validate AWS AppFlow resources. These models ensure that the configurations are not only syntactically correct but also adhere to the expected structure and constraints defined by AWS.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!