azure-resource-health-diagnose

Name: azure-resource-health-diagnose
Author: github

8.6kinstalls4.6kforks

How to install azure-resource-health-diagnose?

github/awesome-copilot/azure-resource-health-diagnose

$npx -y skills add github/awesome-copilot --skill azure-resource-health-diagnose

Installs into the current project.

Summary

Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems.

Files · 1

View on GitHub

SKILL.md

1	# Azure Resource Health & Issue Diagnosis
2
3	This workflow analyzes a specific Azure resource to assess its health status, diagnose potential issues using logs and telemetry data, and develop a comprehensive remediation plan for any problems discovered.
4
5	## Prerequisites
6	- Azure MCP server configured and authenticated
7	- Target Azure resource identified (name and optionally resource group/subscription)
8	- Resource must be deployed and running to generate logs/telemetry
9	- Prefer Azure MCP tools (`azmcp-*`) over direct Azure CLI when available
10
11	## Workflow Steps
12
13	### Step 1: Get Azure Best Practices
14	Action: Retrieve diagnostic and troubleshooting best practices
15	Tools: Azure MCP best practices tool
16	Process:
17	1. Load Best Practices:
18	- Execute Azure best practices tool to get diagnostic guidelines
19	- Focus on health monitoring, log analysis, and issue resolution patterns
20	- Use these practices to inform diagnostic approach and remediation recommendations
21
22	### Step 2: Resource Discovery & Identification
23	Action: Locate and identify the target Azure resource
24	Tools: Azure MCP tools + Azure CLI fallback
25	Process:
26	1. Resource Lookup:
27	- If only resource name provided: Search across subscriptions using `azmcp-subscription-list`
28	- Use `az resource list --name <resource-name>` to find matching resources
29	- If multiple matches found, prompt user to specify subscription/resource group
30	- Gather detailed resource information:
31	- Resource type and current status
32	- Location, tags, and configuration
33	- Associated services and dependencies
34
35	2. Resource Type Detection:
36	- Identify resource type to determine appropriate diagnostic approach:
37	- Web Apps/Function Apps: Application logs, performance metrics, dependency tracking
38	- Virtual Machines: System logs, performance counters, boot diagnostics
39	- Cosmos DB: Request metrics, throttling, partition statistics
40	- Storage Accounts: Access logs, performance metrics, availability
41	- SQL Database: Query performance, connection logs, resource utilization
42	- Application Insights: Application telemetry, exceptions, dependencies
43	- Key Vault: Access logs, certificate status, secret usage
44	- Service Bus: Message metrics, dead letter queues, throughput
45
46	### Step 3: Health Status Assessment
47	Action: Evaluate current resource health and availability
48	Tools: Azure MCP monitoring tools + Azure CLI
49	Process:
50	1. Basic Health Check:
51	- Check resource provisioning state and operational status
52	- Verify service availability and responsiveness
53	- Review recent deployment or configuration changes
54	- Assess current resource utilization (CPU, memory, storage, etc.)
55
56	2. Service-Specific Health Indicators:
57	- Web Apps: HTTP response codes, response times, uptime
58	- Databases: Connection success rate, query performance, deadlocks
59	- Storage: Availability percentage, request success rate, latency
60	- VMs: Boot diagnostics, guest OS metrics, network connectivity
61	- Functions: Execution success rate, duration, error frequency
62
63	### Step 4: Log & Telemetry Analysis
64	Action: Analyze logs and telemetry to identify issues and patterns
65	Tools: Azure MCP monitoring tools for Log Analytics queries
66	Process:
67	1. Find Monitoring Sources:
68	- Use `azmcp-monitor-workspace-list` to identify Log Analytics workspaces
69	- Locate Application Insights instances associated with the resource
70	- Identify relevant log tables using `azmcp-monitor-table-list`
71
72	2. Execute Diagnostic Queries:
73	Use `azmcp-monitor-log-query` with targeted KQL queries based on resource type:
74
75	General Error Analysis:
76	```kql
77	// Recent errors and exceptions
78	union isfuzzy=true
79	AzureDiagnostics,
80	AppServiceHTTPLogs,
81	AppServiceAppLogs,
82	AzureActivity
83	\| where TimeGenerated > ago(24h)
84	\| where Level == "Error" or ResultType != "Success"
85	\| summarize ErrorCount=count() by Resource, ResultType, bin(TimeGenerated, 1h)
86	\| order by TimeGenerated desc
87	```
88
89	Performance Analysis:
90	```kql
91	// Performance degradation patterns
92	Perf
93	\| where TimeGenerated > ago(7d)
94	\| where ObjectName == "Processor" and CounterName == "% Processor Time"
95	\| summarize avg(CounterValue) by Computer, bin(TimeGenerated, 1h)
96	\| where avg_CounterValue > 80
97	```
98
99	Application-Specific Queries:
100	```kql
101	// Application Insights - Failed requests
102	requests
103	\| where timestamp > ago(24h)
104	\| where success == false
105	\| summarize FailureCount=count() by resultCode, bin(timestamp, 1h)
106	\| order by timestamp desc
107
108	// Database - Connection failures
109	AzureDiagnostics
110	\| where ResourceProvider == "MICROSOFT.SQL"
111	\| where C

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azure-resource-health-diagnose

8.6kinstalls4.6kforks

View on GitHub

How to install azure-resource-health-diagnose?

github/awesome-copilot/azure-resource-health-diagnose

$npx -y skills add github/awesome-copilot --skill azure-resource-health-diagnose

Installs into the current project.

Summary

Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems.

Files · 1

View on GitHub

SKILL.md

1	# Azure Resource Health & Issue Diagnosis
2
3	This workflow analyzes a specific Azure resource to assess its health status, diagnose potential issues using logs and telemetry data, and develop a comprehensive remediation plan for any problems discovered.
4
5	## Prerequisites
6	- Azure MCP server configured and authenticated
7	- Target Azure resource identified (name and optionally resource group/subscription)
8	- Resource must be deployed and running to generate logs/telemetry
9	- Prefer Azure MCP tools (`azmcp-*`) over direct Azure CLI when available
10
11	## Workflow Steps
12
13	### Step 1: Get Azure Best Practices
14	Action: Retrieve diagnostic and troubleshooting best practices
15	Tools: Azure MCP best practices tool
16	Process:
17	1. Load Best Practices:
18	- Execute Azure best practices tool to get diagnostic guidelines
19	- Focus on health monitoring, log analysis, and issue resolution patterns
20	- Use these practices to inform diagnostic approach and remediation recommendations
21
22	### Step 2: Resource Discovery & Identification
23	Action: Locate and identify the target Azure resource
24	Tools: Azure MCP tools + Azure CLI fallback
25	Process:
26	1. Resource Lookup:
27	- If only resource name provided: Search across subscriptions using `azmcp-subscription-list`
28	- Use `az resource list --name <resource-name>` to find matching resources
29	- If multiple matches found, prompt user to specify subscription/resource group
30	- Gather detailed resource information:
31	- Resource type and current status
32	- Location, tags, and configuration
33	- Associated services and dependencies
34
35	2. Resource Type Detection:
36	- Identify resource type to determine appropriate diagnostic approach:
37	- Web Apps/Function Apps: Application logs, performance metrics, dependency tracking
38	- Virtual Machines: System logs, performance counters, boot diagnostics
39	- Cosmos DB: Request metrics, throttling, partition statistics
40	- Storage Accounts: Access logs, performance metrics, availability
41	- SQL Database: Query performance, connection logs, resource utilization
42	- Application Insights: Application telemetry, exceptions, dependencies
43	- Key Vault: Access logs, certificate status, secret usage
44	- Service Bus: Message metrics, dead letter queues, throughput
45
46	### Step 3: Health Status Assessment
47	Action: Evaluate current resource health and availability
48	Tools: Azure MCP monitoring tools + Azure CLI
49	Process:
50	1. Basic Health Check:
51	- Check resource provisioning state and operational status
52	- Verify service availability and responsiveness
53	- Review recent deployment or configuration changes
54	- Assess current resource utilization (CPU, memory, storage, etc.)
55
56	2. Service-Specific Health Indicators:
57	- Web Apps: HTTP response codes, response times, uptime
58	- Databases: Connection success rate, query performance, deadlocks
59	- Storage: Availability percentage, request success rate, latency
60	- VMs: Boot diagnostics, guest OS metrics, network connectivity
61	- Functions: Execution success rate, duration, error frequency
62
63	### Step 4: Log & Telemetry Analysis
64	Action: Analyze logs and telemetry to identify issues and patterns
65	Tools: Azure MCP monitoring tools for Log Analytics queries
66	Process:
67	1. Find Monitoring Sources:
68	- Use `azmcp-monitor-workspace-list` to identify Log Analytics workspaces
69	- Locate Application Insights instances associated with the resource
70	- Identify relevant log tables using `azmcp-monitor-table-list`
71
72	2. Execute Diagnostic Queries:
73	Use `azmcp-monitor-log-query` with targeted KQL queries based on resource type:
74
75	General Error Analysis:
76	```kql
77	// Recent errors and exceptions
78	union isfuzzy=true
79	AzureDiagnostics,
80	AppServiceHTTPLogs,
81	AppServiceAppLogs,
82	AzureActivity
83	\| where TimeGenerated > ago(24h)
84	\| where Level == "Error" or ResultType != "Success"
85	\| summarize ErrorCount=count() by Resource, ResultType, bin(TimeGenerated, 1h)
86	\| order by TimeGenerated desc
87	```
88
89	Performance Analysis:
90	```kql
91	// Performance degradation patterns
92	Perf
93	\| where TimeGenerated > ago(7d)
94	\| where ObjectName == "Processor" and CounterName == "% Processor Time"
95	\| summarize avg(CounterValue) by Computer, bin(TimeGenerated, 1h)
96	\| where avg_CounterValue > 80
97	```
98
99	Application-Specific Queries:
100	```kql
101	// Application Insights - Failed requests
102	requests
103	\| where timestamp > ago(24h)
104	\| where success == false
105	\| summarize FailureCount=count() by resultCode, bin(timestamp, 1h)
106	\| order by timestamp desc
107
108	// Database - Connection failures
109	AzureDiagnostics
110	\| where ResourceProvider == "MICROSOFT.SQL"
111	\| where C

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