az-scout-plugin-aks-placement-advisor

v2026.6.0 safe
4.0
Medium Risk

AKS Placement Advisor plugin for az-scout — evaluates and recommends VM SKUs for AKS node pools

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all assessed categories, except for metadata quality, where it shows signs of potential neglect or lack of maintenance.

  • Low risk in network, shell, obfuscation, and credential aspects.
  • Moderate concern over metadata quality and maintainer activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
  • Metadata: The package shows low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

Some documentation present

  • Detailed PyPI description (12415 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
  • 27 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 16 commits in az-scout/az-scout-plugin-aks-placement-advisor
  • Small but multi-author team (3–4 contributors)

🔬 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

No author email provided

✓ 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with az-scout-plugin-aks-placement-advisor 
Create a Python-based utility named 'AKSPlacementAdvisor' that leverages the 'az-scout-plugin-aks-placement-advisor' package to provide recommendations for optimal Virtual Machine (VM) SKUs for Azure Kubernetes Service (AKS) node pools. Your application should have a user-friendly command-line interface (CLI) for interacting with users.

Step-by-step guide:
1. Start by setting up your development environment with Python and installing necessary packages including 'az-scout-plugin-aks-placement-advisor'.
2. Design a CLI that allows users to input details of their AKS cluster such as node pool specifications, workload requirements, and any constraints they might have on VM types.
3. Use 'az-scout-plugin-aks-placement-advisor' to evaluate these inputs and generate a list of recommended VM SKUs that best fit the provided criteria.
4. Display the results in a readable format, highlighting key attributes like cost-efficiency, performance metrics, and compatibility with the specified workloads.
5. Implement error handling to manage incorrect inputs and provide useful feedback to the user.
6. Optionally, extend the utility to save the recommendation report to a file or export it in a structured format like JSON or CSV.
7. Ensure your code is well-documented and includes a README file detailing installation steps, usage examples, and how to contribute to the project.

Features to consider:
- Support for multiple AKS clusters and node pools.
- Detailed performance metrics comparison for each recommended SKU.
- Cost estimation based on the chosen VM SKUs and expected usage.
- User authentication for accessing private AKS clusters.
- Integration with other Azure services for a more comprehensive analysis.

💬 Discussion Feed

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