🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate obfuscation techniques and uses network requests, raising concerns about its true intentions. While there's no direct evidence of malicious activity, the obfuscation suggests potential evasion tactics.

Moderate obfuscation risk
Use of network requests

Per-check LLM notes

Network: The presence of aiohttp indicates network requests, which could be legitimate if the package is designed to interact with APIs.
Shell: No shell execution patterns were detected.
Obfuscation: The code employs dynamic import and base64 decoding which may indicate an attempt to hide functionality or evade analysis.
Credentials: No clear patterns for harvesting credentials or secrets were detected.
Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.4/10)

○ 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 (3047 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

22 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 14 commits in woozheng/aicp-eat
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

api_info += "async with aiohttp.ClientSession() as session:\n" api_info += " async with sessio

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

try: content = base64.b64decode(file_data).decode("utf-8", errors="ignore") except:
y: mod = __import__(f"{{library_name}}.{{mod_name}}", fromlist=[func_name]) func = getattr(mod, func_name, None)
try: mod = __import__(f"{LIBRARY}.{{module}}", fromlist=[func_name]) func = getattr(mod, func_name, None)
y: mod = __import__(f"{LIBRARY}.{{mod_name}}", fromlist=[func_name]) func = getattr(mod, func_name, None)

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository woozheng/aicp-eat appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "dvwoo" 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 aicp-eat

Develop a Python-based restaurant recommendation system using the 'aicp-eat' package. This mini-app will leverage various Python libraries through its HTTP API interface to provide users with personalized restaurant suggestions based on their dietary preferences, location, and previous dining experiences.

### Features:
- **User Input:** Users can input their dietary restrictions (e.g., vegan, gluten-free), preferred cuisine types, and current location.
- **API Integration:** Utilize the 'aicp-eat' package to connect to multiple Python libraries that handle data processing, machine learning, and web scraping to gather restaurant data from different sources.
- **Recommendation Engine:** Implement a recommendation algorithm that suggests restaurants based on user inputs and historical data.
- **User Interface:** Develop a simple command-line interface (CLI) for user interaction.
- **Feedback Loop:** Allow users to rate the recommended restaurants to improve future recommendations.

### Steps to Build the Project:
1. **Setup Environment:** Install Python and the 'aicp-eat' package. Ensure all dependencies required by 'aicp-eat' are installed as well.
2. **Data Collection:** Use 'aicp-eat' to access Python libraries that can scrape restaurant data from websites or APIs. Collect data such as restaurant names, locations, menus, ratings, and dietary options.
3. **Data Processing:** Preprocess the collected data using data processing libraries accessible via 'aicp-eat'. Clean the data and format it into a usable structure for your recommendation engine.
4. **Algorithm Development:** Implement a recommendation algorithm that takes user preferences as input and outputs a list of recommended restaurants. This could involve using machine learning models provided by 'aicp-eat' to analyze patterns in user data and restaurant characteristics.
5. **User Interface:** Create a CLI using Python where users can enter their preferences and receive restaurant recommendations.
6. **Feedback Mechanism:** Integrate a feature within the CLI where users can rate their experience at the recommended restaurants, which feeds back into improving the recommendation algorithm.
7. **Testing and Deployment:** Test the app thoroughly with various scenarios to ensure robustness and accuracy. Once satisfied, deploy the application so it can be accessed and used by others.