🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no detected shell execution, obfuscation, or credential mishandling. The moderate metadata risk is not indicative of malicious activity.

Low network risk
No shell execution patterns
No obfuscation detected
Safe handling of credentials
Moderate metadata risk

Per-check LLM notes

Network: The network call patterns are typical for an asynchronous HTTP client setup in Python using aiohttp.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: Low activity and metadata quality suggest potential low effort or new maintainer, but no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.6/10)

◈ 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 (6609 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

84 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 100 commits in psilabs-dev/aio-lanraragi
Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

self.client_session = aiohttp.ClientSession(connector=self.connector, connector_owner=False)
self.client_session = aiohttp.ClientSession(connector=aiohttp.TCPConnector(ssl=self.ssl)) el
self.client_session = aiohttp.ClientSession() return self.client_session async def close(se

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "psilabs-dev" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aio-lanraragi

Create a fully-functional mini-application called 'RarViewer' using the Python package 'aio-lanraragi'. RarViewer is a command-line tool designed to interact with a LANraragi server, allowing users to manage their manga archive collections more efficiently. Your task is to build a feature-rich application that showcases the capabilities of 'aio-lanraragi', including but not limited to listing all available volumes, searching for specific volumes, downloading volumes, and updating volume metadata.

### Key Features:
1. **Volume Listing**: Display all volumes available on the LANraragi server in a user-friendly format.
2. **Search Functionality**: Allow users to search for volumes by title, author, or tags.
3. **Download Mechanism**: Implement a feature to download selected volumes directly from the LANraragi server.
4. **Metadata Update**: Provide functionality to update volume metadata such as title, author, tags, etc.
5. **Error Handling**: Ensure robust error handling to gracefully manage any issues encountered during API interactions.
6. **User Authentication**: Incorporate basic authentication mechanisms to secure interactions with the LANraragi server.

### Utilization of 'aio-lanraragi':
- Use 'aio-lanraragi' to asynchronously communicate with the LANraragi server, leveraging its aiohttp and pydantic integration for efficient and type-safe API interactions.
- For each feature, demonstrate how you utilize 'aio-lanraragi' methods to achieve the desired outcome, ensuring that the code is clean, modular, and well-documented.
- Focus on showcasing the power of asynchronous programming with 'aio-lanraragi' by optimizing the performance of your application.

### Deliverables:
- A complete, functional Python script implementing RarViewer.
- Detailed documentation explaining the implementation of each feature, the usage of 'aio-lanraragi', and any design decisions made.
- Examples of running the application and screenshots or console outputs demonstrating the functionality.