asynchronously

v1.0.0 suspicious
5.0
Medium Risk

Tools to control async code execution

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, and obfuscation activities, but the metadata suggests it might be newly created with little community support, raising suspicion about its legitimacy.

  • metadata risk due to new creation and minimal activity
  • incomplete maintainer profile
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is designed to interact with external services.
  • Shell: No shell execution detected, indicating the package does not attempt to execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code being intentionally obscured.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The package shows signs of being newly created with minimal activity and an incomplete maintainer profile, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

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

Some documentation present

  • Detailed PyPI description (12682 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 13 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 2 commits in miriada-io/asynchronously
  • Two distinct contributors found

🔬 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: miriada.io>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Very few commits: 2 total

  • Very few commits: 2 total
⚠ 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 asynchronously 
Create a real-time stock market data analysis tool using Python. This tool will fetch live stock prices from multiple exchanges and perform basic statistical analysis on the fly. The application should allow users to input a list of stock symbols and select which statistical metrics they wish to compute in real-time, such as moving averages, standard deviation, and volume trends.

### Core Features:
- **Stock Price Fetching**: Use asynchronous requests to pull live stock price data from financial APIs like Alpha Vantage or Yahoo Finance.
- **Real-Time Analysis**: Implement real-time computation of selected statistical metrics based on incoming stock price data.
- **User Interface**: Develop a simple web-based interface where users can input stock symbols and choose which metrics to track.
- **Data Visualization**: Provide visual representations of the analyzed data through charts and graphs.

### Utilization of 'asynchronously' Package:
- **Control Async Code Execution**: Leverage the 'asynchronously' package to manage the concurrent fetching of stock prices from different exchanges without blocking the main thread.
- **Efficient Data Processing**: Use the package to handle the asynchronous processing of incoming stock data streams, ensuring smooth real-time analysis even under high load conditions.
- **Enhanced User Experience**: Ensure that the application remains responsive while performing heavy computations by controlling the execution of asynchronous tasks effectively.

### Development Steps:
1. Set up the project environment and install necessary packages including 'asynchronously', pandas, matplotlib, and any required financial data API client libraries.
2. Design the user interface using a web framework like Flask or Django, allowing users to input stock symbols and select analysis metrics.
3. Implement asynchronous functions to fetch live stock price data from financial APIs.
4. Create asynchronous functions to process incoming stock data and calculate the selected statistical metrics.
5. Integrate data visualization components to display the analyzed data in real-time.
6. Test the application thoroughly to ensure it performs well under various conditions and handles errors gracefully.
7. Deploy the application to a cloud platform for easy access.

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