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azure-observability▌

microsoft/github-copilot-for-azure · updated Apr 8, 2026

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$npx skills add https://github.com/microsoft/github-copilot-for-azure --skill azure-observability
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summary

Query metrics, logs, and traces across Azure Monitor, Application Insights, and Log Analytics.

  • ›Access Azure Monitor metrics, Application Insights APM data, and Log Analytics KQL queries through MCP tools or Azure CLI commands
  • ›Supports distributed tracing, performance analysis, and infrastructure monitoring across applications and resources
  • ›Includes interactive workbooks for building custom dashboards and reports on observability data
  • ›Common use cases: error tracking, request perfo
skill.md

Azure Observability Services

Services

Service Use When MCP Tools CLI
Azure Monitor Metrics, alerts, dashboards azure__monitor az monitor
Application Insights APM, distributed tracing azure__applicationinsights az monitor app-insights
Log Analytics Log queries, KQL azure__kusto az monitor log-analytics
Alerts Notifications, actions - az monitor alert
Workbooks Interactive reports azure__workbooks -

MCP Server (Preferred)

When Azure MCP is enabled:

Monitor

  • azure__monitor with command monitor_metrics_query - Query metrics
  • azure__monitor with command monitor_logs_query - Query logs with KQL

Application Insights

  • azure__applicationinsights with command applicationinsights_component_list - List App Insights resources

Log Analytics

  • azure__kusto with command kusto_cluster_list - List clusters
  • azure__kusto with command kusto_query - Execute KQL queries

If Azure MCP is not enabled: Run /azure:setup or enable via /mcp.

CLI Reference

# List Log Analytics workspaces
az monitor log-analytics workspace list --output table

# Query logs with KQL
az monitor log-analytics query \
  --workspace WORKSPACE_ID \
  --analytics-query "AzureActivity | take 10"

# List Application Insights
az monitor app-insights component list --output table

# List alerts
az monitor alert list --output table

# Query metrics
az monitor metrics list \
  --resource RESOURCE_ID \
  --metric "Percentage CPU"

Common KQL Queries

// Recent errors
AppExceptions
| where TimeGenerated > ago(1h)
| project TimeGenerated, Message, StackTrace
| order by TimeGenerated desc

// Request performance
AppRequests
| where TimeGenerated > ago(1h)
| summarize avg(DurationMs), count() by Name
| order by avg_DurationMs desc

// Resource usage
AzureMetrics
| where TimeGenerated > ago(1h)
| where MetricName == "Percentage CPU"
| summarize avg(Average) by Resource

Monitoring Strategy

What to Monitor Service Metric/Log
Application errors App Insights Exceptions, failed requests
Performance App Insights Response time, dependencies
Infrastructure Azure Monitor CPU, memory, disk
Security Log Analytics Sign-ins, audit logs
Costs Cost Management Budget alerts

SDK Quick References

For programmatic access to monitoring services, see the condensed SDK guides:

Service Details

For deep documentation on specific services:

how to use azure-observability

How to use azure-observability on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add azure-observability
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/microsoft/github-copilot-for-azure --skill azure-observability

The skills CLI fetches azure-observability from GitHub repository microsoft/github-copilot-for-azure and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/azure-observability

Reload or restart Cursor to activate azure-observability. Access the skill through slash commands (e.g., /azure-observability) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ 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.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.5★★★★★68 reviews
  • ★★★★★Nia Zhang· Dec 28, 2024

    I recommend azure-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Aisha Shah· Dec 16, 2024

    Useful defaults in azure-observability — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Zaid Zhang· Dec 16, 2024

    azure-observability fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Kaira Iyer· Dec 12, 2024

    I recommend azure-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Dhruvi Jain· Dec 4, 2024

    azure-observability fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Zaid Liu· Dec 4, 2024

    Solid pick for teams standardizing on skills: azure-observability is focused, and the summary matches what you get after install.

  • ★★★★★Arjun Perez· Nov 27, 2024

    azure-observability reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Oshnikdeep· Nov 23, 2024

    azure-observability is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Evelyn Choi· Nov 23, 2024

    I recommend azure-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Aisha Garcia· Nov 19, 2024

    Solid pick for teams standardizing on skills: azure-observability is focused, and the summary matches what you get after install.

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