Identify and quantify Azure cost savings through resource analysis, utilization metrics, and actionable optimization recommendations.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add microsoft/github-copilot-for-azure --skill azure-cost-optimizationCompatible with
Supported agents
Discovers orphaned resources (unattached disks, unused NICs, idle gateways) and over-provisioned services using Azure Quick Review scans
Queries actual costs from Azure Cost Management API and utilization data from Azure Monitor to support rightsizing recommendations
Generates prioritized optimization reports with estimated savings, validated pricing,
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionazure-cost-optimizationExecute the skills CLI command in your project's root directory to begin installation:
epx add microsoft/github-copilot-for-azure --skill azure-cost-optimizationFetches azure-cost-optimization from microsoft/github-copilot-for-azure and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/azure-cost-optimization
Restart Cursor to activate azure-cost-optimization. Access via /azure-cost-optimizationin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
microsoft-foundry
microsoft/GitHub-Copilot-for-Azure · Cloud · tag: azure
azure-diagnostics
microsoft/GitHub-Copilot-for-Azure · Cloud · tag: azure
performance-optimization
supercent-io/skills-template · Productivity · tag: optimization
godot-optimization
zate/cc-godot · Backend · tag: optimization
web-performance-optimization
bobmatnyc/claude-mpm-skills · Productivity · tag: optimization
physical-ai-infrastructure-setup-and-resilient-scaling
nvidia/skills · physical-ai · tag: azure
azure-cost-optimization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added azure-cost-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend azure-cost-optimization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for azure-cost-optimization matched our evaluation — installs cleanly and behaves as described in the markdown.
azure-cost-optimization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: azure-cost-optimization is focused, and the summary matches what you get after install.
We added azure-cost-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
azure-cost-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added azure-cost-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
azure-cost-optimization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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