🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct malicious activities but raises some concerns due to the metadata risk, particularly the new or inactive PyPI account and the use of a common email domain.

metadata risk due to new/inactive PyPI account
common email domain usage

Per-check LLM notes

Network: No network calls detected, which is normal and does not indicate any risk.
Shell: Shell execution appears to be related to package development and deployment activities, which are expected behaviors and do not suggest malicious intent.
Obfuscation: No obfuscation patterns detected, suggesting low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has a new or inactive PyPI account and uses a very common email domain, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

144 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 51 commits in tagaryen/pyarchernet
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

sal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.exec
to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git t
'Pushing git tags…') os.system('git tag v{0}'.format(about['__version__'])) os.sys
out['__version__'])) os.system('git push --tags') sys.exit() # Where the ma

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com

Very short email domain: qq.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository tagaryen/pyarchernet appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Archer" 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 archernet

Create a real-time network monitoring tool using the 'archernet' Python package, which is built on top of a C library designed for efficient network operations. This tool will allow users to monitor network traffic in real-time, providing insights into data flow and helping diagnose potential network issues.

### Project Overview:
- **Name:** Network Traffic Monitor
- **Objective:** To develop a simple yet powerful tool that monitors network traffic in real-time, displaying key metrics such as bandwidth usage, packet count, and error rates.

### Core Features:
1. **Real-Time Data Collection:** Continuously collect network traffic data from the system's network interfaces.
2. **Data Visualization:** Display collected data in a user-friendly format, including graphs and charts for easy interpretation.
3. **Alert System:** Set up customizable alerts for when certain thresholds are exceeded, such as high error rates or excessive bandwidth usage.
4. **Configuration Management:** Allow users to configure which network interfaces to monitor and set alert thresholds.

### Utilizing 'archernet':
- Use 'archernet' to establish connections to network interfaces and gather raw data efficiently.
- Leverage 'archernet's capabilities for parsing and analyzing network packets.
- Implement 'archernet' functions to handle real-time data streaming and processing.

### Step-by-Step Development Guide:
1. **Setup Environment:** Install Python and the 'archernet' package. Ensure you have necessary permissions to access network interface data.
2. **Data Collection Module:** Develop a module that uses 'archernet' to collect network traffic data from specified interfaces.
3. **Data Processing Module:** Create a module that processes the raw data collected, calculating metrics like total bytes transferred, packet counts, etc.
4. **Visualization Module:** Integrate a visualization library (like Matplotlib or Plotly) to display the processed data in real-time.
5. **Alert System Implementation:** Design an alert system that triggers notifications based on predefined conditions.
6. **User Interface:** Build a simple GUI using Tkinter or PyQt to provide a user-friendly experience for configuring settings and viewing data.
7. **Testing & Optimization:** Test the tool thoroughly under various network conditions and optimize performance.
8. **Documentation:** Write comprehensive documentation detailing how to install, configure, and use the tool.

This project aims to demonstrate the power and efficiency of 'archernet' in handling complex network operations while providing practical value through real-world application.