aion-sdk

v0.16.0 suspicious
4.0
Medium Risk

Python SDK for AI Agent trading on prediction markets (Polymarket MVP)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential unreliability due to its minimal maintainer history and underdeveloped git repository, raising concerns about long-term support and updates. However, no immediate malicious activities were detected.

  • Minimal maintainer history and underdeveloped git repository
  • Legitimate network calls using urllib.request.urlopen but requires further investigation
Per-check LLM notes
  • Network: The use of urllib.request.urlopen suggests network calls which could be legitimate for SDK functionality, but requires further investigation into the URLs and data being transmitted.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has minimal maintainer history and an underdeveloped git repository, indicating potential unreliability.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • Test runner config found: pyproject.toml
  • 4 test file(s) detected (e.g. test_aiagent_contract.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.aionmarket.com
  • Detailed PyPI description (7999 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 78 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 23 commits in aionmarket/aion-sdk
  • Single author but highly active (23 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • lient_method) with patch("urllib.request.urlopen", side_effect=mock_urlopen): method(*args, *
  • xyz"}, } with patch("urllib.request.urlopen", return_value=_MockResponse(response)): out
  • } with patch( "urllib.request.urlopen", side_effect=_http_error( url="
  • "ok": True}) with patch("urllib.request.urlopen", side_effect=_assert_url): result = client.
  • t": "demo"}) with patch("urllib.request.urlopen", side_effect=_assert_url): out = client.cla
  • emo-agent"}) with patch("urllib.request.urlopen", side_effect=_assert_url): out = client.get
✓ 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: aionmarket.com>

✓ 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 aion-sdk 
Create a predictive trading bot using the 'aion-sdk' Python package, which is designed for AI Agent trading on prediction markets like Polymarket MVP. Your task is to develop a fully-functional mini-app that allows users to interact with the prediction market by making informed trades based on AI predictions. Here’s a detailed guide on what your application should achieve and how to utilize the 'aion-sdk' package effectively:

1. **Setup Environment**: Start by setting up a Python environment. Ensure you have Python installed and create a virtual environment for your project. Install the 'aion-sdk' package via pip.
2. **Market Data Retrieval**: Use 'aion-sdk' to retrieve real-time or historical data from the Polymarket platform. This includes market conditions, available contracts, and current predictions.
3. **AI Prediction Integration**: Integrate an external AI model or use one provided by the 'aion-sdk' to analyze the retrieved data and predict outcomes of specific events or contracts. Your app should display these predictions alongside the market data.
4. **Trading Strategy Development**: Based on the AI predictions, develop a simple trading strategy. For instance, if the AI predicts a high probability of an event occurring, the bot should consider buying the corresponding contract.
5. **Automated Trading Execution**: Implement functionality within your app to automatically execute trades according to the developed strategy. Ensure there are safeguards in place to prevent excessive trading or losses.
6. **User Interface**: Develop a basic web interface using Flask or another lightweight framework to allow users to view market data, AI predictions, and trade history. Users should be able to monitor their portfolio and adjust trading parameters if necessary.
7. **Security and Compliance**: Address security concerns by ensuring sensitive information such as API keys and user credentials are stored securely. Also, ensure compliance with Polymarket’s terms of service and any relevant regulations.
8. **Testing and Optimization**: Before deploying your bot, thoroughly test its performance under different market conditions. Optimize the AI model and trading strategy based on testing results.
9. **Documentation and Deployment**: Document your project clearly, explaining each component and how it interacts with the 'aion-sdk'. Deploy your application to a cloud service provider such as AWS or Heroku for others to use.

By following these steps, you will create a robust and functional predictive trading bot that leverages the power of AI and the 'aion-sdk' to make informed decisions in the world of prediction markets.

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