aws-resource-validator-forecast

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network, shell, and obfuscation activities, but the metadata risk due to the maintainer's new or inactive account and lack of proper identification warrants further scrutiny.

  • Metadata risk due to new/inactive maintainer account
  • Lack of proper author name
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages not requiring 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 obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of unauthorized credential access.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not definitive 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 (303 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-forecast 
Create a Python-based utility named 'ForecastValidator' that leverages the 'aws-resource-validator-forecast' package to validate AWS Forecast resources. This tool should enable users to input various AWS Forecast resource configurations and receive validation feedback based on the Pydantic v2 models provided by the package. The utility should support command-line interaction and provide detailed error messages for invalid configurations.

### Key Features:
1. **Resource Configuration Input:** Users should be able to input configurations for AWS Forecast resources such as datasets, dataset groups, forecasts, predictors, and ML models directly via the command line.
2. **Validation Output:** Upon submission of a configuration, the utility should validate it against the Pydantic models from 'aws-resource-validator-forecast'. If the configuration is valid, the utility should confirm this; if not, it should return specific error messages detailing why the configuration failed validation.
3. **Interactive Mode:** Include an interactive mode where users can iteratively test their configurations until they are validated. In this mode, the utility should offer suggestions for correcting errors based on common issues encountered with AWS Forecast configurations.
4. **Configuration File Support:** Users should also be able to load configurations from a YAML file, which the utility will validate using the same process as direct command-line input.
5. **Detailed Documentation:** Provide comprehensive documentation explaining how to use the utility, including examples of valid and invalid configurations.

### Utilization of 'aws-resource-validator-forecast':
- The utility will import Pydantic models from the 'aws-resource-validator-forecast' package to define the structure and rules for validating AWS Forecast resources.
- These models will be used both for validating user inputs and generating informative error messages when a configuration fails validation.
- By leveraging these pre-defined models, the utility aims to simplify the process of ensuring AWS Forecast resources adhere to best practices and AWS standards.

💬 Discussion Feed

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