azure-mgmt-advisor

v9.0.1 safe
3.0
Low Risk

Microsoft Azure Advisor Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package appears legitimate with no indications of malicious behavior. However, the incomplete author information suggests potential new or less experienced maintainers.

  • Incomplete author information
  • No detected network calls or shell executions
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity; it may be designed to operate without external dependencies.
  • Shell: No shell execution patterns detected, which is expected and safe.
  • Obfuscation: The observed pattern is a common method for extending module search paths and does not indicate malicious intent.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The author information is incomplete, suggesting a potentially less experienced or new maintainer.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: conftest.py
  • 2 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9298 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

  • Type checker (mypy / pyright / pytype) referenced in project
  • 98 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # coding=u
✓ 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: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python 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 azure-mgmt-advisor 
Develop a Python-based utility named 'AzureAdvisorChecker' that leverages the 'azure-mgmt-advisor' package to monitor and manage recommendations from Microsoft Azure Advisor. This tool will provide insights into optimizing your Azure resources by fetching and displaying actionable advice tailored to improve performance, security, reliability, and cost-efficiency of your Azure environment.

Key Features:
1. Authenticate with Azure using Azure CLI or Service Principal credentials.
2. Fetch and display all available recommendations across different categories (Performance, Security, Reliability, Cost).
3. Implement filtering capabilities to focus on specific categories or resource types.
4. Allow users to mark recommendations as 'Accepted', 'Dismissed', or 'In Progress'.
5. Provide an option to export recommendations to a CSV file for offline analysis.
6. Integrate a simple UI (using a library like Tkinter) to make the application more user-friendly.
7. Include error handling and logging mechanisms to ensure robustness.

Steps to Build:
1. Set up your development environment with Python and install the necessary packages including 'azure-mgmt-advisor'.
2. Use Azure CLI or Service Principal credentials for authentication within your application.
3. Utilize the 'azure-mgmt-advisor' package to fetch recommendations from Azure Advisor.
4. Develop a filtering mechanism based on recommendation category and/or resource type.
5. Implement functionality to update the status of recommendations based on user input.
6. Create a feature to export fetched recommendations to a CSV file.
7. Design a basic UI to interact with the application and visualize fetched recommendations.
8. Test the application thoroughly to ensure it meets the requirements and is user-friendly.

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