az-scout-plugin-latency-stats

v2026.6.0 suspicious
4.0
Medium Risk

Inter-region latency statistics plugin for az-scout

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has legitimate purposes but shows low maintainer activity and poor metadata quality, raising concerns about its legitimacy and ongoing support.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: The use of HTTP requests might be legitimate depending on the plugin's purpose, but could indicate external communication which should be reviewed.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The package shows low maintainer activity and poor metadata quality, which could indicate low effort or potential malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_cloud63.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5541 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
  • 25 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 29 commits in az-scout/az-scout-plugin-latency-stats
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • import httpx async with httpx.AsyncClient(timeout=60.0) as client: resp = await client.get(_CL
  • import httpx async with httpx.AsyncClient(timeout=60.0) as client: resp = await client.get(_IN
✓ 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-latency-stats 
Your task is to develop a web-based mini-application using Python and Flask that leverages the 'az-scout-plugin-latency-stats' package to provide real-time inter-region latency statistics for Azure cloud services. This application will be aimed at network engineers and cloud architects who need to monitor and optimize their cloud infrastructure across different regions.

### Project Overview:
- **Name:** Latency Monitor
- **Technology Stack:** Python, Flask, HTML/CSS/JavaScript, 'az-scout-plugin-latency-stats'
- **Goal:** To create a user-friendly dashboard that displays live latency data between selected Azure regions.

### Core Features:
1. **Region Selection:** Users should be able to select two Azure regions from a dropdown menu.
2. **Real-Time Data:** The application must fetch and display real-time latency statistics between the chosen regions.
3. **Data Visualization:** Implement a simple chart or graph to visually represent the latency data over time.
4. **Alert System:** If the latency exceeds a predefined threshold, the application should notify the user via email or SMS.
5. **User Authentication:** Basic authentication to ensure only authorized users can access the application.
6. **Responsive Design:** The dashboard should be mobile-friendly and responsive.

### Utilizing 'az-scout-plugin-latency-stats':
- Use the package to gather inter-region latency data. Ensure you understand how to install and configure the package within your Flask application.
- Explore the documentation of 'az-scout-plugin-latency-stats' to find out how to retrieve latency data and any other useful functionalities it provides.
- Integrate the package's API calls into your Flask routes to dynamically fetch and update latency information on your dashboard.

### Additional Considerations:
- **Security:** Pay attention to securing your application, especially when dealing with sensitive data like user credentials and alert thresholds.
- **Scalability:** Think about how the application could scale as more regions are added or if multiple users are accessing it simultaneously.
- **Documentation:** Provide clear instructions on how to set up and run the application, including any necessary configurations for the 'az-scout-plugin-latency-stats' package.

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