anibridge-anilist-provider

v0.2.2 suspicious
4.0
Medium Risk

AniList provider for the AniBridge project.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of shell execution but has some concerns regarding metadata and network calls, suggesting potential risks that warrant further investigation.

  • Maintainer has only one package and lacks an associated GitHub repository.
  • Network calls suggest interaction with AniList API, which is expected but should be verified.
Per-check LLM notes
  • Network: The network call pattern suggests the package is likely making API requests to Anilist or similar services, which is expected for an Anilist provider.
  • Shell: No shell execution patterns detected, indicating no immediate risk related to shell command execution.
  • Metadata: The maintainer has a single package and no associated GitHub repository, which could indicate a less established or potentially suspicious presence.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (950 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 46 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)

  • self._session = aiohttp.ClientSession(headers=headers) return self._session async de
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Elias Benbourenane" 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 anibridge-anilist-provider 
Create a personalized anime recommendation engine using the 'anibridge-anilist-provider' Python package. Your task is to develop a web-based application that allows users to input their favorite anime titles and then recommends other anime they might enjoy based on similar themes, genres, and user ratings. Here’s a detailed breakdown of what your application should achieve:

1. **User Registration & Login**: Implement a simple registration and login system so users can save their preferences and recommendations.
2. **Anime Search Functionality**: Integrate the 'anibridge-anilist-provider' package to allow users to search for anime titles by name, genre, or season. Use this package to fetch details such as title, synopsis, images, and user ratings from AniList.
3. **Favorite Anime List**: Allow users to add their favorite anime titles to a list after searching. Ensure these favorites are saved when they log out and reappear upon logging back in.
4. **Recommendation Engine**: Develop a recommendation algorithm that suggests new anime based on the user's favorite titles. Consider factors like genre, theme, and user ratings to make accurate suggestions.
5. **Interactive User Interface**: Design a clean, interactive UI where users can easily navigate through search results, view details about anime, and manage their favorite list.
6. **Feedback System**: Enable users to rate recommended anime they've watched to help refine future recommendations.
7. **Data Visualization**: Incorporate charts or graphs to visually represent trends in the user's favorite genres or top-rated anime.

To utilize the 'anibridge-anilist-provider' package effectively, follow these steps:
- Install the package using pip.
- Authenticate with AniList using OAuth to access user data securely.
- Utilize the package's methods to query anime information and build dynamic content based on user interactions.
- Implement error handling to ensure smooth user experience even when API calls fail.

Your goal is to create a seamless, engaging experience for anime enthusiasts looking to discover new shows based on their tastes.

💬 Discussion Feed

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