aws-resource-validator-compute-optimizer

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package presents minimal risks based on the analysis notes provided. It has no network calls, shell executions, obfuscations, or credential harvesting activities.

  • No network calls detected
  • Sparse author details
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, suggesting a potentially new or less active 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-compute-optimizer 
Create a Python-based utility named 'ComputeOptiAnalyzer' that leverages the 'aws-resource-validator-compute-optimizer' package to analyze and optimize AWS EC2 instance usage based on historical utilization data. This tool will help users understand if their current EC2 instances are over-provisioned or under-provisioned, suggesting more cost-effective alternatives where applicable. Here’s a detailed breakdown of the project requirements:

1. **Setup**: Ensure your environment has Python 3.8+ installed. Install the necessary dependencies including 'boto3', 'pandas', and 'aws-resource-validator-compute-optimizer'. Use pip for dependency management.

2. **Authentication**: Implement a secure method for authenticating with AWS services. This could involve using IAM roles, AWS CLI credentials, or directly inputting access keys via a configuration file.

3. **Data Collection**: Utilize the 'aws-resource-validator-compute-optimizer' package to fetch historical utilization data for EC2 instances. Your script should be able to filter and retrieve data based on user-specified time periods.

4. **Analysis**: Analyze the collected data to determine the average CPU, memory, and storage usage of each EC2 instance. Based on these metrics, suggest optimal instance types that better match the actual workload while minimizing costs.

5. **Visualization**: Integrate a simple visualization component (using libraries like matplotlib or seaborn) to graphically represent the utilization data and proposed optimizations.

6. **Reporting**: Generate a detailed report summarizing the findings. Include recommendations for instance type changes, estimated cost savings, and potential performance improvements.

7. **User Interface**: Develop a basic command-line interface (CLI) for interacting with the tool. Users should be able to specify parameters such as start and end dates for data collection, filtering criteria, and output preferences.

8. **Testing**: Write unit tests for critical functionalities like data fetching, analysis, and reporting. Ensure your tests cover various scenarios, including edge cases and error handling.

The 'aws-resource-validator-compute-optimizer' package will primarily be used for validating the structure and format of AWS Compute Optimizer data against predefined Pydantic models. This validation ensures that the data retrieved from AWS services is consistent and usable for further analysis.

💬 Discussion Feed

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