aws-cost-operations▌
zxkane/aws-skills · updated Apr 8, 2026
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This skill provides comprehensive guidance for AWS cost optimization, monitoring, observability, and operational excellence with integrated MCP servers.
AWS Cost & Operations
This skill provides comprehensive guidance for AWS cost optimization, monitoring, observability, and operational excellence with integrated MCP servers.
AWS Documentation Requirement
Always verify AWS facts using MCP tools (mcp__aws-mcp__* or mcp__*awsdocs*__*) before answering. The aws-mcp-setup dependency is auto-loaded — if MCP tools are unavailable, guide the user through that skill's setup flow.
Integrated MCP Servers
This plugin provides 3 MCP servers:
Bundled Servers
1. AWS Pricing MCP Server (pricing)
Purpose: Pre-deployment cost estimation and optimization
- Estimate costs before deploying resources
- Compare pricing across regions
- Calculate Total Cost of Ownership (TCO)
- Evaluate different service options for cost efficiency
2. AWS Cost Explorer MCP Server (costexp)
Purpose: Detailed cost analysis and reporting
- Analyze historical spending patterns
- Identify cost anomalies and trends
- Forecast future costs
- Analyze cost by service, region, or tag
3. Amazon CloudWatch MCP Server (cw)
Purpose: Metrics, alarms, and logs analysis
- Query CloudWatch metrics and logs
- Create and manage CloudWatch alarms
- Troubleshoot operational issues
- Monitor resource utilization
Note: The following servers are available separately via the Full AWS MCP Server (see
aws-mcp-setupskill) and are not bundled with this plugin:
- AWS Billing and Cost Management MCP — Real-time billing details
- CloudWatch Application Signals MCP — APM and SLOs
- AWS Managed Prometheus MCP — PromQL queries for containers
- AWS CloudTrail MCP — API activity audit
- AWS Well-Architected Security Assessment MCP — Security posture assessment
When to Use This Skill
Use this skill when:
- Optimizing AWS costs and reducing spending
- Estimating costs before deployment
- Monitoring application and infrastructure performance
- Setting up observability and alerting
- Analyzing spending patterns and trends
- Investigating operational issues
- Auditing AWS activity and changes
- Assessing security posture
- Implementing operational excellence
Cost Optimization Best Practices
Pre-Deployment Cost Estimation
Always estimate costs before deploying:
- Use AWS Pricing MCP to estimate resource costs
- Compare pricing across different regions
- Evaluate alternative service options
- Calculate expected monthly costs
- Plan for scaling and growth
Example workflow:
"Estimate the monthly cost of running a Lambda function with
1 million invocations, 512MB memory, 3-second duration in us-east-1"
Cost Analysis and Optimization
Regular cost reviews:
- Use Cost Explorer MCP to analyze spending trends
- Identify cost anomalies and unexpected charges
- Review costs by service, region, and environment
- Compare actual vs. budgeted costs
- Generate cost optimization recommendations
Cost optimization strategies:
- Right-size over-provisioned resources
- Use appropriate storage classes (S3, EBS)
- Implement auto-scaling for dynamic workloads
- Leverage Savings Plans and Reserved Instances
- Delete unused resources and snapshots
- Use cost allocation tags effectively
Budget Monitoring
Track spending against budgets:
- Use Billing and Cost Management MCP to monitor budgets
- Set up budget alerts for threshold breaches
- Review budget utilization regularly
- Adjust budgets based on trends
- Implement cost controls and governance
Monitoring and Observability Best Practices
CloudWatch Metrics and Alarms
Implement comprehensive monitoring:
- Use CloudWatch MCP to query metrics and logs
- Set up alarms for critical metrics:
- CPU and memory utilization
- Error rates and latency
- Queue depths and processing times
- API gateway throttling
- Lambda errors and timeouts
- Create CloudWatch dashboards for visualization
- Use log insights for troubleshooting
Example alarm scenarios:
- Lambda error rate > 1%
- EC2 CPU utilization > 80%
- API Gateway 4xx/5xx error spike
- DynamoDB throttled requests
- ECS task failures
Application Performance Monitoring
Monitor application health:
- Use CloudWatch Application Signals MCP for APM
- Track service-level objectives (SLOs)
- Monitor application dependencies
- Identify performance bottlenecks
- Set up distributed tracing
Container and Kubernetes Monitoring
For containerized workloads:
- Use AWS Managed Prometheus MCP for metrics
- Monitor container resource utilization
- Track pod and node health
- Create PromQL queries for custom metrics
- Set up alerts for container anomalies
Audit and Security Best Practices
CloudTrail Activity Analysis
Audit AWS activity:
- Use CloudTrail MCP to analyze API activity
- Track who made changes to resources
- Investigate security incidents
- Monitor for suspicious activity patterns
- Audit compliance with policies
Common audit scenarios:
- "Who deleted this S3 bucket?"
- "Show all IAM role changes in the last 24 hours"
- "List failed login attempts"
- "Find all actions by a specific user"
- "Track modifications to security groups"
Security Assessment
Regular security reviews:
- Use Well-Architected Security Assessment MCP
- Assess security posture against best practices
- Identify security gaps and vulnerabilities
- Implement recommended security improvements
- Document security compliance
Security assessment areas:
- Identity and Access Management (IAM)
- Detective controls and monitoring
- Infrastructure protection
- Data protection and encryption
- Incident response preparedness
Using MCP Servers Effectively
Cost Analysis Workflow
- Pre-deployment: Use Pricing MCP to estimate costs
- Post-deployment: Use Billing MCP to track actual spending
- Analysis: Use Cost Explorer MCP for detailed cost analysis
- Optimization: Implement recommendations from Cost Explorer
Monitoring Workflow
- Setup: Configure CloudWatch metrics and alarms
- Monitor: Use CloudWatch MCP to track key metrics
- Analyze: Use Application Signals for APM insights
- Troubleshoot: Query CloudWatch Logs for issue resolution
Security Workflow
- Audit: Use CloudTrail MCP to review activity
- Assess: Use Well-Architected Security Assessment
- Remediate: Implement security recommendations
- Monitor: Track security events via CloudWatch
MCP Usage Best Practices
- Cost Awareness: Check pricing before deploying resources
- Proactive Monitoring: Set up alarms for critical metrics
- Regular Reviews: Analyze costs and performance weekly
- Audit Trails: Review CloudTrail logs for compliance
- Security First: Run security assessments regularly
- Optimize Continuously: Act on cost and performance recommendations
Operational Excellence Guidelines
Cost Optimization
- Tag Everything: Use consistent cost allocation tags
- Review Monthly: Analyze spending trends and anomalies
- Right-size: Match resources to actual usage
- Automate: Use auto-scaling and scheduling
- Monitor Budgets: Set alerts for cost overruns
Monitoring and Alerting
- Critical Metrics: Alert on business-critical metrics
- Noise Reduction: Fine-tune thresholds to reduce false positives
- Actionable Alerts: Ensure alerts have clear remediation steps
- Dashboard Visibility: Create dashboards for key stakeholders
- Log Retention: Balance cost and compliance needs
Security and Compliance
- Least Privilege: Grant minimum required permissions
- Audit Regularly: Review CloudTrail logs for anomalies
- Encrypt Data: Use encryption at rest and in transit
- Assess Continuously: Run security assessments frequently
- Incident Response: Have procedures for security events
Additional Resources
For detailed operational patterns and best practices, refer to the comprehensive reference:
File: references/operations-patterns.md
This reference includes:
- Cost optimization strategies
- Monitoring and alerting patterns
- Observability best practices
- Security and compliance guidelines
- Troubleshooting workflows
CloudWatch Alarms Reference
File: references/cloudwatch-alarms.md
Common alarm configurations for:
- Lambda functions
- EC2 instances
- RDS databases
- DynamoDB tables
- API Gateway
- ECS services
- Application Load Balancers
How to use aws-cost-operations on Cursor
AI-first code editor with Composer
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 aws-cost-operations
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches aws-cost-operations from GitHub repository zxkane/aws-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate aws-cost-operations. Access the skill through slash commands (e.g., /aws-cost-operations) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★29 reviews- ★★★★★Dhruvi Jain· Dec 12, 2024
aws-cost-operations is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Nov 23, 2024
Keeps context tight: aws-cost-operations is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Oshnikdeep· Nov 3, 2024
aws-cost-operations fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Oct 22, 2024
aws-cost-operations has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Oct 14, 2024
I recommend aws-cost-operations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ren Reddy· Sep 21, 2024
aws-cost-operations fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hiroshi Jackson· Sep 5, 2024
aws-cost-operations has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Sep 1, 2024
aws-cost-operations reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Evelyn Harris· Sep 1, 2024
I recommend aws-cost-operations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakura Agarwal· Aug 24, 2024
aws-cost-operations fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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