anosys-sdk-core

v1.0.13 suspicious
4.0
Medium Risk

Core utilities for AnoSys SDK - AI observability and monitoring

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of malicious intent or obfuscation but the incomplete author information and single package maintenance raise some concerns about the maintainer's experience and intentions.

  • Incomplete author information and single package maintenance suggest potential novice or suspicious activity.
  • No evidence of obfuscation or credential harvesting.
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is incomplete and the maintainer has only one package, which may indicate a less experienced or potentially suspicious actor.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_core.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.anosys.ai
  • Detailed PyPI description (906 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

  • 28 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 85 commits in anosys-ai/anosys-sdk
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • try: response = requests.post( self.api_url, json=data,
  • try: response = requests.get( f"{API_KEY_RESOLVER_URL}?apikey={api_key}",
  • g_indices) response = requests.post(_log_api_url, json=mapped_data, timeout=5) response.
✓ 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: anosys.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository anosys-ai/anosys-sdk appears legitimate

⚠ 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 anosys-sdk-core 
Create a mini-application named 'AnoSys Monitor' using the 'anosys-sdk-core' Python package. This application will serve as an AI observability tool, allowing users to monitor and analyze the performance of their machine learning models in real-time. Here’s a step-by-step guide on how to develop it:

1. **Setup Environment**: Begin by setting up your Python environment and installing the 'anosys-sdk-core' package.
2. **Model Integration**: Integrate a sample machine learning model (such as a simple classifier or regressor) into the application. Ensure that the model can be loaded and executed within the app.
3. **Real-Time Monitoring**: Implement real-time monitoring capabilities using the 'anosys-sdk-core'. This includes tracking key metrics such as accuracy, precision, recall, F1 score, etc., during the model's execution.
4. **Data Visualization**: Develop a user-friendly interface to visualize these metrics. Use libraries like Matplotlib or Plotly to create dynamic graphs and charts that update as the model processes data.
5. **Alert System**: Incorporate an alert system that notifies users when certain thresholds are breached. For instance, if the model's accuracy drops below a specified percentage, send an email or SMS alert.
6. **Report Generation**: Enable the generation of detailed reports summarizing the model's performance over time. These reports should include statistical analyses and visual representations of the model's behavior.
7. **Customization Options**: Allow users to customize which metrics they want to monitor and how often they receive alerts.
8. **Testing and Documentation**: Thoroughly test the application and document its functionalities, including installation instructions, configuration options, and usage examples.

By following these steps, you'll create a powerful yet user-friendly tool for monitoring and improving the performance of machine learning models in real-world applications.

💬 Discussion Feed

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