AI Analysis
Final verdict: SAFE
The package exhibits low risk across multiple dimensions with no signs of malicious behavior. The metadata suggests it's from a new or less active account, but there are no other red flags.
- Low risk scores across network, shell, obfuscation, and credential risks.
- Maintainer has only one package listed.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: baseinformation.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Proteus Technology PVT. LTD." 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 aiemailautomationutility
Create a Python-based email automation tool named 'CatalogMailer' that leverages the 'aiemailautomationutility' package. This tool will be designed to streamline the process of extracting product catalog details from emails and sending targeted marketing emails based on these extracted details. Here’s a detailed step-by-step guide on how to develop this tool: 1. **Setup Project Environment**: Initialize a new Python environment and install the 'aiemailautomationutility' package. 2. **Configuration Setup**: Allow users to configure the email labels they want to monitor and the criteria for filtering emails. Use the updated configuration feature of 'aiemailautomationutility' to specify which product catalogs to extract details from. 3. **Email Extraction**: Implement a function that uses 'aiemailautomationutility' to only process emails that match the configured labels and criteria. Extract relevant product details such as name, price, description, etc., from these emails. 4. **Data Storage**: Store the extracted product details in a structured format (e.g., CSV, JSON). Consider adding functionality to update existing records if a product detail changes. 5. **Targeted Email Generation**: Develop a module that generates personalized marketing emails using the extracted product data. Users should be able to customize the content and recipients. 6. **Sending Emails**: Integrate the ability to send these generated emails through SMTP or another email service provider. Ensure that the emails are sent only after user confirmation. 7. **User Interface**: Optionally, create a simple command-line interface (CLI) or a basic web interface for easy interaction with the tool. The UI should allow users to view extracted data, manage configurations, and send emails. 8. **Testing & Documentation**: Thoroughly test all functionalities and write comprehensive documentation including setup instructions, usage examples, and troubleshooting tips. Suggested Features: - Support for multiple email accounts and labels. - Advanced filtering options for email processing. - Integration with popular CRMs for customer segmentation. - Automated scheduling for regular data extraction and email sending. - Error handling and logging mechanisms for robustness. By utilizing 'aiemailautomationutility', you can focus more on the application logic and less on the intricacies of email processing, making CatalogMailer both powerful and easy to maintain.