🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate metadata risk due to incomplete maintainer information and lack of a GitHub repository, suggesting potential unreliability. However, the low scores in network, shell, obfuscation, and credential risks indicate it's not conclusively malicious.

Moderate metadata risk
No clear evidence of malicious activities

Per-check LLM notes

Network: The use of an HTTP client suggests network interaction, which is not inherently malicious but requires further investigation to understand its purpose.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has no associated GitHub repository and the maintainer information is incomplete, raising some concerns but not definitive proof of malice.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (9963 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

19 type-annotated function signatures detected in source

○ 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 score 1.5

Found 1 network call pattern(s)

hrase) self._client = httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) async def __aenter__(self):

✓ 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: aissociate.at>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 aissociate

Create a Python-based mini-application that leverages the 'aissociate' package to enhance user engagement through personalized content recommendation. This application will serve as a simple web interface where users can input their interests or preferences, and the system will recommend relevant articles, videos, or podcasts based on those inputs. The core functionality of the application involves using the AIssociate API to fetch and filter content according to user preferences. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Ensure you have Python installed along with necessary packages like Flask for the web framework, requests for HTTP requests, and 'aissociate' for interfacing with the AIssociate API.
2. **API Integration**: Use 'aissociate' to authenticate your application with the AIssociate service and set up routes to fetch content data.
3. **User Interface**: Develop a simple HTML form for users to enter their interests. Use Flask to handle form submissions and pass the data to the backend.
4. **Content Recommendation Engine**: Implement logic using 'aissociate' to process user inputs and return a curated list of recommended content items. Consider factors such as relevance, popularity, and recency.
5. **Display Recommendations**: On the frontend, display the recommended content items in an appealing format. Include options for users to provide feedback on the recommendations.
6. **Feedback Loop**: Incorporate a mechanism to capture user feedback on the recommendations to improve future suggestions.
7. **Testing & Deployment**: Thoroughly test the application locally before deploying it to a public server. Ensure all functionalities work seamlessly.

Suggested Features:
- User-friendly interface for easy navigation.
- Personalized content based on user preferences.
- Option for users to rate or comment on the recommendations.
- Real-time updates for new content based on user interests.

The 'aissociate' package plays a crucial role in fetching and filtering content data from various sources, ensuring that the recommendations are accurate and relevant to each user's interests.