auralis-af

v1.0.1 safe
4.0
Medium Risk

SDK oficial para la API de Auralis Futures

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with minimal risks detected. It does not engage in any network calls, shell executions, or obfuscations that could indicate malicious activity.

  • Low metadata risk due to a single package from the maintainer.
  • No network calls, shell executions, obfuscation, or credential risks detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk from command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, indicating a potentially new or less active account.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2698 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

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: auralisfutures.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Auralis Futures" 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 auralis-af 
Your task is to develop a financial forecasting tool using the 'auralis-af' Python package, which is the official SDK for Auralis Futures API. This tool will help users predict future market trends based on historical data and real-time market conditions. Here are the key steps and features your application should include:

1. **Setup Environment**: Ensure you have Python installed along with the 'auralis-af' package. Use pip to install the package if it’s not already available.
2. **Data Retrieval**: Utilize 'auralis-af' to fetch historical financial data for various assets (e.g., stocks, commodities). Your application should allow users to specify the asset and the time period of interest.
3. **Real-Time Data Integration**: Integrate real-time market data into your tool. This feature will enable users to make more accurate predictions by incorporating current market conditions.
4. **Data Analysis & Visualization**: Implement statistical analysis techniques and machine learning models to analyze the fetched data. Visualize these analyses through charts and graphs to provide insights into potential market movements.
5. **Prediction Engine**: Develop a prediction engine that uses the analyzed data to forecast future trends. This engine could employ different algorithms such as linear regression, ARIMA, or neural networks depending on the complexity and accuracy desired.
6. **User Interface**: Create a user-friendly interface where users can input parameters like asset type, time frame, and model choice. Display results clearly and concisely.
7. **Documentation & Testing**: Write comprehensive documentation explaining each feature and functionality of your application. Conduct thorough testing to ensure reliability and accuracy of predictions.

The 'auralis-af' package will be crucial in fetching both historical and real-time data efficiently. Make sure to leverage its capabilities fully to enhance the functionality and performance of your financial forecasting tool.

💬 Discussion Feed

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