🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legitimate use with low network and metadata risks. There is no clear indication of malicious activity.

Low network risk due to standard HTTP request handling
No shell execution detected
Minimal metadata risk with potential lack of maintainer information

Per-check LLM notes

Network: The use of httpx.AsyncClient suggests the package is intended to make HTTP requests, which is common for packages dealing with RPC clients.
Shell: No shell execution patterns were detected.
Metadata: The package shows some red flags such as lack of maintainer information and a single package on PyPI, but no clear evidence of malicious intent or typosquatting.

📦 Package Quality Overall: Low (2.4/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://aiobtclientrpc.readthedocs.io
Detailed PyPI description (1266 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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)

om_url(proxy_url) return httpx.AsyncClient(**kwargs) async def catch_connection_exceptions(coro):

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

p msg = rencode.loads(zlib.decompress(data), decode_utf8=True) msg_type = msg[0]

✓ 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: example.org>

✓ 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 aiobtclientrpc

Create a BitTorrent client monitoring tool using Python's 'aiobtclientrpc' package. This tool will allow users to monitor and manage their BitTorrent downloads from a command-line interface. The application should include the following functionalities:

1. **Connection Setup**: Allow users to connect to different BitTorrent clients (such as uTorrent, qBittorrent) running on local or remote machines.
2. **Download Status Monitoring**: Display real-time status updates for all active torrents including download speed, upload speed, total size, downloaded size, remaining time, and seed/peer count.
3. **Pause/Resume Torrents**: Provide commands to pause and resume individual torrent downloads.
4. **Remove Torrents**: Implement functionality to remove torrents from the client with options to also delete the associated files.
5. **Add New Torrents**: Allow users to add new torrents either by providing a .torrent file path or a magnet link.
6. **Configuration Settings**: Enable users to set configuration settings such as download/upload rate limits, save location, and auto-start behavior for torrents.
7. **Logging and Reporting**: Maintain logs of user actions and provide reporting features to analyze download statistics over time.

To achieve these functionalities, utilize 'aiobtclientrpc' to interact with the BitTorrent client's RPC interface. Understand the specific RPC methods available for each client and implement them accordingly. Ensure your application handles errors gracefully and provides clear, user-friendly feedback.