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AWS Resource Health Diagnose Agent Skill

by github

Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
37394
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/github/awesome-copilot --skill aws-resource-health-diagnose

The instruction itself

12 sections, as written by the author

AWS Resource Health & Issue Diagnosis

This workflow analyzes a specific AWS resource to assess its health status, diagnose potential issues using CloudWatch logs and metrics, and develop a comprehensive remediation plan for any problems discovered.

Prerequisites

  • AWS CLI configured and authenticated
  • Target AWS resource identified (name, type, and optionally region/account)
  • CloudWatch logging and metrics enabled on the target resource

Workflow Steps

Step 1: Get AWS Diagnostic Best Practices

Fetch https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/ for monitoring and troubleshooting guidance to inform the diagnostic approach.

Step 2: Resource Discovery & Identification

Locate the target resource using the appropriate AWS CLI command for its type:

# EC2
aws ec2 describe-instances --filters "Name=tag:Name,Values=<name>"
# Lambda
aws lambda get-function --function-name <name>
# RDS
aws rds describe-db-instances --db-instance-identifier <name>
# ECS
aws ecs describe-services --cluster <cluster> --services <name>
# ALB
aws elbv2 describe-load-balancers --names <name>
# DynamoDB
aws dynamodb describe-table --table-name <name>
# SQS
aws sqs get-queue-attributes --queue-url <url> --attribute-names All
# API Gateway
aws apigatewayv2 get-apis

If multiple matches are found, prompt the user to specify region/account.

Step 3: Health Status Assessment

Run service-specific health checks:

# EC2
aws ec2 describe-instance-status --instance-ids <id>

# RDS
aws rds describe-db-instances --db-instance-identifier <name> \
  --query 'DBInstances[0].DBInstanceStatus'

# Lambda - error rate over 24h
aws cloudwatch get-metric-statistics --namespace AWS/Lambda \
  --metric-name Errors --dimensions Name=FunctionName,Value=<name> \
  --start-time $(date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 3600 --statistics Sum

# ECS
aws ecs describe-services --cluster <cluster> --services <name> \
  --query 'services[0].[status,runningCount,desiredCount,pendingCount]'

Key health indicators by service type:

  • Lambda: Error rate, throttle rate, duration P99, concurrent executions
  • RDS: CPU utilization, FreeStorageSpace, DatabaseConnections, ReadLatency/WriteLatency
  • ECS: Running vs desired task count, task stop reason
  • ALB: TargetResponseTime, HTTPCode_ELB_5XX_Count, UnHealthyHostCount
  • SQS: ApproximateNumberOfMessagesNotVisible, ApproximateAgeOfOldestMessage
  • DynamoDB: ConsumedReadCapacityUnits, ThrottledRequests, SuccessfulRequestLatency

Step 4: Log & Metrics Analysis

Find log groups and run CloudWatch Logs Insights queries:

# Find log groups
aws logs describe-log-groups --log-group-name-prefix /aws/<service>/<name>

# Start a query (last 24h errors)
aws logs start-query \
  --log-group-name /aws/lambda/<name> \
  --start-time $(date -u -d '24 hours ago' +%s) \
  --end-time $(date -u +%s) \
  --query-string 'filter @message like /ERROR/ | stats count(*) as errorCount by bin(1h)'

# Get results
aws logs get-query-results --query-id <id>

# Lambda cold starts
aws logs start-query \
  --log-group-name /aws/lambda/<name> \
  --start-time $(date -u -d '24 hours ago' +%s) \
  --end-time $(date -u +%s) \
  --query-string 'filter @type = "REPORT" | filter @initDuration > 0 | stats count() as coldStarts by bin(1h)'

# RDS Performance Insights (if enabled)
aws pi get-resource-metrics \
  --service-type RDS --identifier db:<identifier> \
  --metric-queries '[{"Metric":"db.load.avg"}]' \
  --start-time $(date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period-in-seconds 3600

Identify: recurring error patterns, correlation with deployments (CloudTrail), performance trends, dependency failures.

Step 5: Issue Classification & Root Cause Analysis

Severity:

  • Critical: Service unavailable, data loss, security incidents
  • High: Performance degradation, error rates >5%, intermittent failures
  • Medium: Warnings, suboptimal configuration, minor performance issues
  • Low: Informational alerts, optimization opportunities

Root Cause Categories:

  • Configuration Issues: wrong settings, missing env vars, IAM permission denials
  • Resource Constraints: CPU/memory/disk limits, Lambda throttling, RDS connection exhaustion
  • Network Issues: security group rules, VPC routing, DNS, NACLs
  • Application Issues: code bugs, memory leaks, unhandled exceptions, slow queries
  • Dependency Issues: downstream timeouts, SQS/SNS failures, external API limits
  • Security Issues: KMS key issues, certificate expiration

Step 6: Generate Remediation Plan

Immediate Actions (Critical):

# Lambda throttling — increase reserved concurrency
aws lambda put-reserved-concurrency \
  --function-name <name> --reserved-concurrent-executions 100

# RDS connection exhaustion — reboot to reset connections
aws rds reboot-db-instance --db-instance-identifier <name>

Short-term Fixes (High/Medium): Configuration adjustments, right-sizing, CloudWatch alarm improvements, IAM corrections.

Long-term Improvements: Architectural changes for resilience, preventive monitoring, enable AWS Health Dashboard notifications via EventBridge.

Step 7: Report & User Confirmation

Present findings:

🏥 AWS Resource Health Assessment

📊 Resource Overview:
• Resource: [Name] ([Type])
• Status: [Healthy/Warning/Critical]
• Region: [Region] | Account: [Account ID]

🚨 Issues Identified:
• Critical: X | High: Y | Medium: Z | Low: N

🔍 Top Issues:
1. [Issue]: [Description] — Impact: [High/Medium/Low]
2. [Issue]: [Description] — Impact: [High/Medium/Low]

🛠️ Remediation: X immediate, Y short-term, Z long-term actions

❓ Proceed with detailed remediation plan? (y/n)

Then generate a full markdown report covering: health metrics, issues with root cause analysis, phased remediation steps with AWS CLI commands, CloudWatch alarm recommendations, and validation checklist.

Error Handling

  • Resource Not Found: Ask user to clarify name/region
  • Authentication Issues: Guide through aws configure
  • Insufficient Permissions: List required IAM actions (logs:*, cloudwatch:*, pi:*)
  • No Logs Available: Suggest enabling CloudWatch logging for the resource type
  • Query Timeouts: Use shorter time windows

Success Criteria

  • ✅ Resource health accurately assessed across all key metrics
  • ✅ All significant issues identified and classified by severity
  • ✅ Root cause analysis completed for major problems
  • ✅ Actionable remediation plan with AWS CLI commands
  • ✅ CloudWatch monitoring recommendations included
  • ✅ Implementation steps include validation and rollback procedures

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How to use it

Copy the folder

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