az-scout-plugin-odcr-coverage

v2026.6.0 safe
3.0
Low Risk

az-scout plugin for On-Demand Capacity Reservation (ODCR) coverage analysis

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks across all categories assessed, with only metadata suggesting potential low trustworthiness. However, there's insufficient evidence to indicate any malicious activity.

  • Low risk scores in all categories
  • No signs of network, shell, or obfuscation risks
  • Metadata risk noted but not indicative of malicious behavior
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low activity and effort suggest potential low trustworthiness, but insufficient evidence for malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (5142 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
  • 22 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 27 commits in az-scout/az-scout-plugin-odcr-coverage
  • 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 4.0

2 maintainer concern(s) found

  • Author "Ludovic Rivallain" 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-odcr-coverage 
Create a mini-application named 'ODCRAnalyzer' that leverages the 'az-scout-plugin-odcr-coverage' Python package to analyze On-Demand Capacity Reservation (ODCR) usage within Azure environments. This tool will provide insights into the current state of ODCR utilization, helping users optimize their resource allocation and cost management strategies. Here are the key steps and features your application should include:

1. **Setup and Configuration**: Begin by setting up the environment where the application will run. Ensure you have Python installed along with the necessary packages including 'az-scout-plugin-odcr-coverage'. Configure the application to authenticate with Azure using Azure CLI or Service Principal credentials.
2. **Data Collection**: Utilize 'az-scout-plugin-odcr-coverage' to collect data on all ODCRs currently active in the user's Azure subscription(s). This includes details such as reservation ID, scope, start/end times, and associated costs.
3. **Analysis and Reporting**: Implement functionality to analyze the collected data. Identify underutilized or over-provisioned reservations, and calculate potential savings if reservations were adjusted based on actual usage patterns. Generate reports summarizing these findings.
4. **Visualization**: Develop a simple yet effective dashboard to visualize the ODCR utilization data. Use libraries like Plotly or Matplotlib to create charts and graphs showing reservation usage trends over time, alongside cost-saving recommendations.
5. **User Interface**: Create a command-line interface (CLI) for interacting with the application. Users should be able to specify which subscriptions to analyze, view detailed reports, and receive alerts about suboptimal reservation usage.
6. **Integration with Other Tools**: Consider integrating 'ODCRAnalyzer' with other tools commonly used in DevOps workflows, such as Azure DevOps or GitHub Actions, to automate the process of analyzing ODCRs periodically.
7. **Documentation and Support**: Provide comprehensive documentation explaining how to install and use 'ODCRAnalyzer', along with FAQs and troubleshooting tips. Also, set up a basic support system where users can report issues or request new features.

By following these guidelines, you'll develop a powerful tool that not only helps users understand their ODCR usage but also suggests actionable improvements to enhance cost efficiency in their Azure deployments.

💬 Discussion Feed

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