arrakis-server

v0.10.0 suspicious
4.0
Medium Risk

Server implementation of the Arrakis low-latency timeseries data distribution platform

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in terms of network, shell, obfuscation, and credential handling. However, the incomplete maintainer information and lack of a linked GitHub repository increase the metadata risk, making it suspicious.

  • Incomplete maintainer information
  • Lack of linked GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network operations.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The package lacks an associated GitHub repository and the maintainer's information is incomplete, raising some concerns but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • Test runner config found: conftest.py
  • 8 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://docs.ligo.org/ngdd/arrakis-server
  • 1 documentation file(s) (e.g. gen_ref_nav.py)
  • Detailed PyPI description (4325 chars)
○ 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

  • 123 type-annotated function signatures detected in source
○ 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

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: ligo.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 arrakis-server 
Your task is to develop a real-time financial market data visualization tool using the 'arrakis-server' package. This tool will enable users to subscribe to live stock price updates from various exchanges and visualize these prices on a dynamic graph. The goal is to create an intuitive and responsive user interface that allows users to select different stocks and view their price movements in real-time. Here’s a detailed breakdown of the project requirements:

1. **Setup**: Begin by installing the 'arrakis-server' package and setting up a basic server instance capable of handling real-time data streams.
2. **Data Subscription**: Implement functionality that allows users to subscribe to real-time stock price updates from multiple exchanges simultaneously. Users should be able to specify which stocks they want to track.
3. **Visualization**: Integrate a graphing library (such as Plotly or Matplotlib) to display the stock price movements over time. Ensure the graph is interactive and updates in real-time based on the data received from the 'arrakis-server'.
4. **User Interface**: Develop a simple yet effective web interface where users can input stock symbols and see the corresponding price charts. The UI should be responsive and easy to navigate.
5. **Performance Monitoring**: Include a feature that monitors the performance of the server and data stream. Display metrics such as latency and throughput in the UI.
6. **Error Handling**: Ensure robust error handling to manage issues like network disruptions or incorrect stock symbols entered by users.
7. **Security**: Since this tool deals with real-time financial data, implement basic security measures to protect the data stream and user interactions.

This project leverages the 'arrakis-server' package's capability to handle low-latency timeseries data distribution, making it ideal for real-time applications such as financial market data visualization.

💬 Discussion Feed

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