arwn-client

v0.2.1 safe
4.0
Medium Risk

Python client library for parsing ARWN weather station MQTT messages

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network calls, shell execution, and obfuscation. While metadata analysis reveals some concerns about the author and activity level, these alone do not constitute sufficient evidence of malicious intent or a supply-chain attack.

  • Low risk scores across multiple categories.
  • No direct evidence of malicious behavior.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is intended to interact with remote services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: The package shows some red flags such as an unknown author and low activity, but there's no clear evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (1514 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

  • 8 type-annotated function signatures (partial)
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 17 commits in sdague/arwn-client
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: dague.net>

✓ 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 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 arwn-client 
Create a real-time weather monitoring dashboard using the 'arwn-client' Python package. This dashboard will allow users to visualize and analyze data from ARWN weather stations in a user-friendly manner. The app should connect to an MQTT broker, subscribe to ARWN weather station topics, and display key weather metrics such as temperature, humidity, wind speed, and atmospheric pressure in real-time.

Key Features:
1. Real-time Data Display: Continuously update the displayed weather metrics as new data is received via MQTT.
2. Historical Data Storage: Implement a feature to store collected data in a local SQLite database for historical analysis.
3. User Interface: Develop a simple web-based UI using Flask that allows users to view current conditions and access historical data.
4. Alerts System: Configure alerts based on user-defined thresholds for any of the weather metrics. For example, notify users if the temperature exceeds a certain value.
5. Data Visualization: Integrate a lightweight charting library like Plotly to graphically represent the historical data.

Steps to Build the Application:
1. Install the 'arwn-client' package and set up a connection to your MQTT broker.
2. Subscribe to the appropriate ARWN weather station topics and parse incoming messages using 'arwn-client'.
3. Use Flask to create a web server that serves as the UI for your application.
4. Store incoming weather data in a SQLite database.
5. Implement an API endpoint to fetch historical data from the database.
6. Develop a frontend that displays real-time weather conditions and allows users to configure alert settings.
7. Add functionality to plot historical data using Plotly and serve these visualizations through the Flask app.
8. Test the application thoroughly to ensure all features work as expected.

💬 Discussion Feed

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