aws-resource-validator-connect-contact-lens

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all categories except metadata, which has a moderate score due to incomplete author information. There are no indications of malicious behavior or supply-chain attacks.

  • Low risk scores in network, shell, and obfuscation categories.
  • Incomplete author metadata increases suspicion but lacks evidence of malintent.
Per-check LLM notes
  • Network: No network calls suggest normal operation if the package does not require external communication.
  • Shell: No shell execution suggests the package is not performing system-level operations.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
  • Metadata: The author's information is incomplete, suggesting potential unreliability.

📦 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 (339 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-connect-contact-lens 
Create a mini-application called 'ContactLensAnalyzer' that leverages the 'aws-resource-validator-connect-contact-lens' package to analyze and validate AWS Connect Contact Lens data. This application will serve as a tool for AWS Connect users to ensure their recorded calls meet specific quality standards before they are stored or archived. Here’s how you can approach building this application:

1. **Setup Environment**: Start by setting up a Python virtual environment and installing necessary packages including 'aws-resource-validator-connect-contact-lens', boto3 for AWS SDK, and pydantic for model validation.

2. **Data Fetching**: Implement a feature within 'ContactLensAnalyzer' that fetches call recordings metadata from AWS Connect Contact Lens using boto3. Ensure the application can handle pagination if there are more than 1000 call recordings.

3. **Validation Logic**: Utilize the 'aws-resource-validator-connect-contact-lens' package to define and apply validation rules on the fetched call recording metadata. For instance, check if all required tags are present, if the recording duration is within expected limits, and if the agent's performance metrics (like talk time vs hold time) align with company standards.

4. **Reporting**: Develop a reporting module that generates a summary report based on the validation results. The report should include details such as total number of validated recordings, number of recordings that failed validation, and reasons for failure.

5. **User Interface**: Create a simple command-line interface (CLI) for interacting with 'ContactLensAnalyzer'. Users should be able to initiate data fetching, view validation rules, run validations, and generate reports through the CLI.

6. **Configuration Management**: Allow users to configure validation rules and AWS credentials through a configuration file (e.g., YAML). This ensures flexibility and ease of use without hardcoding sensitive information.

7. **Error Handling**: Implement robust error handling to manage issues like invalid AWS credentials, missing required fields in call metadata, or network connectivity problems.

8. **Testing**: Write unit tests for your validation logic and integration tests to ensure the application works seamlessly with AWS services.

This project aims to provide a practical example of how to leverage AWS Connect Contact Lens data along with Pydantic models for validation purposes, making it a valuable tool for organizations looking to maintain high standards in customer service interactions.

💬 Discussion Feed

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