auspicium

v0.9.1 suspicious
5.0
Medium Risk

Auspicium DaaS SDK — market data for crypto and prediction market quants

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk due to sparse metadata and lack of associated repositories, raising concerns about its origin and maintenance.

  • Sparse author details and lack of associated GitHub repository.
  • Metadata risk score of 4 out of 10.
Per-check LLM notes
  • Network: Network calls to a configured gateway URL are expected if the package is designed to interact with a service or API.
  • Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, and there's no associated GitHub repository, which raises some suspicion but not enough to conclusively identify it as malicious.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

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

Some documentation present

  • Detailed PyPI description (2710 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

  • 176 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 score 3.0

Found 2 network call pattern(s)

  • g() cls._client = httpx.Client( base_url=cfg.gateway_url, h
  • cfg = get_config() resp = httpx.get(f"{cfg.gateway_url}/v1/health", timeout=10) assert resp.
✓ 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: auspicium.io>

✓ 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 auspicium 
Your task is to develop a Python-based mini-application that serves as a dashboard for analyzing crypto market trends using the 'auspicium' package. This application will provide real-time market data for various cryptocurrencies, allowing users to monitor price movements, trading volumes, and other key indicators. Additionally, it will offer predictive analytics based on historical data, enabling users to make informed decisions about their investments.

Key Features:
1. Real-Time Data Fetching: Implement a feature that fetches live market data for multiple cryptocurrencies using the 'auspicium' package. Display the latest prices, trading volumes, and other relevant metrics.
2. Historical Data Analysis: Allow users to input a specific cryptocurrency and date range to retrieve historical data. Use this data to generate charts showing price trends over time.
3. Predictive Analytics: Utilize machine learning models trained on historical data to predict future price movements. Display these predictions alongside the real-time data.
4. Customizable Alerts: Enable users to set up alerts for significant changes in market conditions such as sudden drops or spikes in prices.
5. User Interface: Develop a simple yet intuitive user interface using Python's Tkinter library or any other preferred framework. Ensure the interface is responsive and easy to navigate.

Steps to Build the Application:
1. Install the 'auspicium' package and any other necessary dependencies.
2. Create a main function that initializes the application and sets up the user interface.
3. Implement a class or module that handles the fetching of real-time and historical data using the 'auspicium' package.
4. Integrate a charting library (such as matplotlib) to visualize the data effectively.
5. Develop the predictive analytics component using scikit-learn or any other suitable library. Train your model on historical data fetched from 'auspicium'.
6. Add functionality for setting up alerts based on user-defined criteria.
7. Test the application thoroughly to ensure all features work as expected.
8. Deploy the application locally or on a server if possible.

By completing this project, you will gain valuable experience working with real-time data APIs, integrating third-party packages, and building applications with predictive capabilities.

💬 Discussion Feed

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