aranet-cloud

v0.1.0 suspicious
4.0
Medium Risk

Async Python client for the Aranet Cloud REST API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is relatively new with limited maintainer information, raising concerns about its origin and intentions. However, it does not exhibit any malicious patterns such as shell execution or credential harvesting.

  • New package with limited maintainer details
  • No malicious patterns detected
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession suggests the package performs network operations which is expected for cloud-related packages.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package appears suspicious due to its newness and lack of maintainer information, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6102 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 62 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 4 commits in jasonjhofmann/aranet-cloud
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • : self._session = aiohttp.ClientSession(timeout=self._timeout) self._owns_session = True
  • import aiohttp session = aiohttp.ClientSession() try: with aioresponses() as m: m.g
✓ 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: jasonhofmann.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jasonjhofmann/aranet-cloud appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aranet-cloud 
Create a real-time indoor air quality monitoring dashboard using the 'aranet-cloud' Python package. This application will fetch and display live data from multiple Aranet sensors connected to your Aranet Cloud account. Here’s a step-by-step guide on how to build this mini-app:

1. **Setup**: Begin by setting up your development environment with Python installed. Use pip to install the 'aranet-cloud' package.
2. **Authentication**: Implement user authentication to securely connect to the Aranet Cloud API. This involves retrieving access tokens using OAuth 2.0.
3. **Data Fetching**: Utilize the 'aranet-cloud' package to periodically fetch sensor data from your Aranet Cloud account. Focus on key metrics such as temperature, humidity, CO2 levels, and battery status.
4. **Data Visualization**: Integrate a charting library like Plotly or Matplotlib to visualize the fetched data in real-time. Create dynamic graphs that update every minute to show trends over time.
5. **Alert System**: Implement an alert system that notifies users via email or SMS if any of the monitored metrics exceed predefined thresholds.
6. **User Interface**: Develop a simple yet effective web interface using Flask or Django. The UI should allow users to view their sensor data, set alert thresholds, and manage their accounts.
7. **Testing & Deployment**: Thoroughly test your application to ensure it works as expected. Once tested, deploy your app to a cloud service provider like Heroku or AWS.

The 'aranet-cloud' package will be used extensively throughout this project to handle all interactions with the Aranet Cloud API. It simplifies the process of fetching sensor data, making it easier to focus on building a robust and user-friendly application.

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