ai-parrot-advisors

v0.1.0 suspicious
4.0
Medium Risk

Product Advisor and selection matching components for AI-Parrot

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity, but its novelty and lack of maintainer history raise concerns about potential supply-chain risks.

  • New package with unknown maintainers
  • Lack of maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new and lacks maintainer history, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_advisor.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/phenobarbital/ai-parrot/
  • Detailed PyPI description (859 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 109 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in phenobarbital/ai-parrot
  • 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: phenobarbital.info>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository phenobarbital/ai-parrot appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 ai-parrot-advisors 
Create a personalized product recommendation system using the 'ai-parrot-advisors' package. This mini-app will serve as a user-friendly interface where users can input their preferences and receive tailored product suggestions based on their needs. The app will integrate seamlessly with existing e-commerce platforms or can stand alone as a standalone application.

### Step-by-Step Guide:
1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed along with necessary libraries such as Flask for web development and the 'ai-parrot-advisors' package for product advisory functionalities.
2. **Design User Interface**: Design a simple yet effective user interface where users can input their preferences, such as product type, budget, and specific requirements.
3. **Integrate ai-parrot-advisors**: Utilize the 'ai-parrot-advisors' package to process user inputs and generate recommendations. This includes leveraging its product advisor and selection matching components to provide accurate and relevant suggestions.
4. **Implement Backend Logic**: Develop backend logic to handle user data securely and efficiently. Use Flask routes to connect frontend user inputs to backend processing through the 'ai-parrot-advisors' package.
5. **Testing and Deployment**: Thoroughly test the application for accuracy, reliability, and user-friendliness. Deploy the application either as a standalone web service or integrate it into an existing e-commerce platform.

### Suggested Features:
- **User Preferences Input**: Allow users to specify product types, budgets, and any other relevant criteria.
- **Real-time Recommendations**: Provide instant feedback based on user inputs.
- **Detailed Product Information**: Offer comprehensive details about recommended products, including images, descriptions, and links to purchase.
- **User Feedback Loop**: Implement a mechanism for users to rate the relevance of the recommendations, which can improve future suggestions.
- **Integration with Payment Gateways**: For seamless purchasing, integrate payment gateway options.

This project aims to demonstrate the practical application of the 'ai-parrot-advisors' package in enhancing user experience and personalizing product discovery processes.