Install
$ agentstack add skill-github-awesome-copilot-aws-cost-optimize ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AWS Cost Optimize
This workflow analyzes Infrastructure-as-Code (IaC) files and AWS resources to generate cost optimization recommendations. It creates individual GitHub issues for each optimization opportunity plus one EPIC issue to coordinate implementation, enabling efficient tracking and execution of cost savings initiatives.
Prerequisites
- AWS CLI configured and authenticated (
aws sts get-caller-identitysucceeds) - GitHub MCP server configured and authenticated
- Target GitHub repository identified
- AWS resources deployed (IaC files optional but helpful)
Workflow Steps
Step 1: Get AWS Cost Optimization Best Practices
Action: Retrieve cost optimization best practices before analysis Tools: fetch to retrieve AWS documentation Process:
- Load Best Practices:
- Fetch
https://docs.aws.amazon.com/cost-management/latest/userguide/cost-optimization-best-practices.html - Fetch the AWS Well-Architected Cost Optimization pillar summary
- Use these practices to inform subsequent analysis and recommendations
Step 2: Discover AWS Infrastructure
Action: Dynamically discover and analyze AWS resources and configurations Tools: AWS CLI + Local file system access Process:
- Account & Region Discovery:
- Execute
aws sts get-caller-identityto confirm account - Execute
aws configure get regionto determine default region
- Resource Discovery (per region):
- EC2 instances:
aws ec2 describe-instances --query 'Reservations[].Instances[].[InstanceId,InstanceType,State.Name,Tags]' - RDS instances:
aws rds describe-db-instances --query 'DBInstances[].[DBInstanceIdentifier,DBInstanceClass,Engine,MultiAZ]' - Lambda functions:
aws lambda list-functions --query 'Functions[].[FunctionName,Runtime,MemorySize,Architectures]' - ECS clusters/services:
aws ecs list-clustersthenaws ecs describe-services - S3 buckets:
aws s3api list-buckets --query 'Buckets[].Name' - ElastiCache clusters:
aws elasticache describe-cache-clusters - NAT Gateways:
aws ec2 describe-nat-gateways - Load Balancers:
aws elbv2 describe-load-balancers
- IaC Detection:
- Scan for IaC files:
**/*.tf,**/*.yaml(CloudFormation/SAM),**/*.json(CloudFormation),**/cdk.json,lib/**/*.ts(CDK) - Parse resource definitions to understand intended configurations
- Do NOT use application code files — only IaC files as the source of truth
- If no IaC files found: STOP and report to user
Step 3: Collect Usage Metrics & Validate Current Costs
Action: Gather utilization data and verify actual resource costs Tools: AWS CLI (CloudWatch, Cost Explorer) Process:
- CloudWatch Metrics (last 7 days):
```bash # EC2 CPU utilization aws cloudwatch get-metric-statistics \ --namespace AWS/EC2 --metric-name CPUUtilization \ --dimensions Name=InstanceId,Value= \ --start-time $(date -u -d '7 days ago' +%Y-%m-%dT%H:%M:%SZ) \ --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \ --period 3600 --statistics Average
# Lambda duration aws cloudwatch get-metric-statistics \ --namespace AWS/Lambda --metric-name Duration \ --dimensions Name=FunctionName,Value= \ --start-time $(date -u -d '7 days ago' +%Y-%m-%dT%H:%M:%SZ) \ --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \ --period 86400 --statistics Average,Maximum ```
- AWS Cost Explorer:
``bash aws ce get-cost-and-usage \ --time-period Start=$(date -u -d '30 days ago' +%Y-%m-%d),End=$(date -u +%Y-%m-%d) \ --granularity MONTHLY --metrics BlendedCost \ --group-by Type=DIMENSION,Key=SERVICE ``
- Calculate Baseline Metrics: CPU/Memory averages, Lambda invocation rates, data transfer patterns, and a realistic current monthly total.
Step 4: Generate Cost Optimization Recommendations
Action: Analyze resources to identify optimization opportunities Process:
- Apply Optimization Patterns:
Compute:
- EC2: Right-size based on CPU/memory ( 20 | Medium: Score 5-20 | Low: Score 80% of estimated savings realized, no performance degradation).
Error Handling
- AWS Authentication Failure: Guide through
aws configure - No Resources Found: Create informational issue about AWS resource deployment
- Insufficient Permissions: List required IAM read-only permissions
- GitHub Creation Failure: Output formatted recommendations to console
- Cost Explorer Not Enabled: Guide user to enable in AWS Console
Success Criteria
- ✅ All cost estimates verified against actual configurations and AWS pricing
- ✅ Individual GitHub issues created for each optimization
- ✅ EPIC issue provides comprehensive coordination and tracking
- ✅ All recommendations include specific AWS CLI or IaC commands
- ✅ User confirmation obtained before creating issues
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: github
- Source: github/awesome-copilot
- License: MIT
- Homepage: https://awesome-copilot.github.com/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.