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feat: add Agent Skills and restructure documentation
- Add 47 Agent Skills across 14 plugins following Anthropic's specification - Python (5): async patterns, testing, packaging, performance, UV package manager - JavaScript/TypeScript (4): advanced types, Node.js patterns, testing, modern JS - Kubernetes (4): manifests, Helm charts, GitOps, security policies - Cloud Infrastructure (4): Terraform, multi-cloud, hybrid networking, cost optimization - CI/CD (4): pipeline design, GitHub Actions, GitLab CI, secrets management - Backend (3): API design, architecture patterns, microservices - LLM Applications (4): LangChain, prompt engineering, RAG, evaluation - Blockchain/Web3 (4): DeFi protocols, NFT standards, Solidity security, Web3 testing - Framework Migration (4): React, Angular, database, dependency upgrades - Observability (4): Prometheus, Grafana, distributed tracing, SLO - Payment Processing (4): Stripe, PayPal, PCI compliance, billing - API Scaffolding (1): FastAPI templates - ML Operations (1): ML pipeline workflow - Security (1): SAST configuration - Restructure documentation into /docs directory - agent-skills.md: Complete guide to all 47 skills - agents.md: All 85 agents with model configuration - plugins.md: Complete catalog of 63 plugins - usage.md: Commands, workflows, and best practices - architecture.md: Design principles and patterns - Update README.md - Add Agent Skills banner announcement - Reduce length by ~75% with links to detailed docs - Add What's New section showcasing Agent Skills - Add Popular Use Cases with real examples - Improve navigation with Core Guides and Quick Links - Update marketplace.json with skills arrays for 14 plugins All 47 skills follow Agent Skills Specification: - Required YAML frontmatter (name, description) - Use when activation clauses - Progressive disclosure architecture - Under 1024 character descriptions
This commit is contained in:
Seth Hobson
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plugins/cloud-infrastructure/skills/cost-optimization/SKILL.md
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plugins/cloud-infrastructure/skills/cost-optimization/SKILL.md
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---
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name: cost-optimization
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description: Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
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---
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# Cloud Cost Optimization
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Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP.
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## Purpose
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Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.
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## When to Use
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- Reduce cloud spending
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- Right-size resources
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- Implement cost governance
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- Optimize multi-cloud costs
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- Meet budget constraints
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## Cost Optimization Framework
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### 1. Visibility
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- Implement cost allocation tags
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- Use cloud cost management tools
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- Set up budget alerts
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- Create cost dashboards
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### 2. Right-Sizing
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- Analyze resource utilization
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- Downsize over-provisioned resources
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- Use auto-scaling
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- Remove idle resources
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### 3. Pricing Models
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- Use reserved capacity
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- Leverage spot/preemptible instances
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- Implement savings plans
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- Use committed use discounts
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### 4. Architecture Optimization
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- Use managed services
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- Implement caching
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- Optimize data transfer
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- Use lifecycle policies
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## AWS Cost Optimization
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### Reserved Instances
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```
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Savings: 30-72% vs On-Demand
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Term: 1 or 3 years
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Payment: All/Partial/No upfront
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Flexibility: Standard or Convertible
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```
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### Savings Plans
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```
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Compute Savings Plans: 66% savings
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EC2 Instance Savings Plans: 72% savings
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Applies to: EC2, Fargate, Lambda
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Flexible across: Instance families, regions, OS
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```
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### Spot Instances
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```
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Savings: Up to 90% vs On-Demand
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Best for: Batch jobs, CI/CD, stateless workloads
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Risk: 2-minute interruption notice
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Strategy: Mix with On-Demand for resilience
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```
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### S3 Cost Optimization
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```hcl
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resource "aws_s3_bucket_lifecycle_configuration" "example" {
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bucket = aws_s3_bucket.example.id
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rule {
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id = "transition-to-ia"
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status = "Enabled"
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transition {
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days = 30
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storage_class = "STANDARD_IA"
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}
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transition {
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days = 90
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storage_class = "GLACIER"
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}
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expiration {
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days = 365
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}
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}
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}
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```
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## Azure Cost Optimization
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### Reserved VM Instances
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- 1 or 3 year terms
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- Up to 72% savings
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- Flexible sizing
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- Exchangeable
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### Azure Hybrid Benefit
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- Use existing Windows Server licenses
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- Up to 80% savings with RI
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- Available for Windows and SQL Server
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### Azure Advisor Recommendations
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- Right-size VMs
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- Delete unused resources
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- Use reserved capacity
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- Optimize storage
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## GCP Cost Optimization
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### Committed Use Discounts
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- 1 or 3 year commitment
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- Up to 57% savings
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- Applies to vCPUs and memory
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- Resource-based or spend-based
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### Sustained Use Discounts
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- Automatic discounts
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- Up to 30% for running instances
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- No commitment required
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- Applies to Compute Engine, GKE
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### Preemptible VMs
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- Up to 80% savings
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- 24-hour maximum runtime
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- Best for batch workloads
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## Tagging Strategy
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### AWS Tagging
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```hcl
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locals {
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common_tags = {
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Environment = "production"
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Project = "my-project"
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CostCenter = "engineering"
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Owner = "team@example.com"
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ManagedBy = "terraform"
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}
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}
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resource "aws_instance" "example" {
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ami = "ami-12345678"
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instance_type = "t3.medium"
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tags = merge(
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local.common_tags,
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{
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Name = "web-server"
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}
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)
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}
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```
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**Reference:** See `references/tagging-standards.md`
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## Cost Monitoring
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### Budget Alerts
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```hcl
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# AWS Budget
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resource "aws_budgets_budget" "monthly" {
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name = "monthly-budget"
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budget_type = "COST"
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limit_amount = "1000"
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limit_unit = "USD"
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time_period_start = "2024-01-01_00:00"
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time_unit = "MONTHLY"
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notification {
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comparison_operator = "GREATER_THAN"
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threshold = 80
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threshold_type = "PERCENTAGE"
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notification_type = "ACTUAL"
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subscriber_email_addresses = ["team@example.com"]
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}
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}
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```
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### Cost Anomaly Detection
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- AWS Cost Anomaly Detection
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- Azure Cost Management alerts
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- GCP Budget alerts
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## Architecture Patterns
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### Pattern 1: Serverless First
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- Use Lambda/Functions for event-driven
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- Pay only for execution time
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- Auto-scaling included
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- No idle costs
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### Pattern 2: Right-Sized Databases
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```
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Development: t3.small RDS
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Staging: t3.large RDS
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Production: r6g.2xlarge RDS with read replicas
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```
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### Pattern 3: Multi-Tier Storage
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```
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Hot data: S3 Standard
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Warm data: S3 Standard-IA (30 days)
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Cold data: S3 Glacier (90 days)
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Archive: S3 Deep Archive (365 days)
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```
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### Pattern 4: Auto-Scaling
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```hcl
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resource "aws_autoscaling_policy" "scale_up" {
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name = "scale-up"
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scaling_adjustment = 2
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adjustment_type = "ChangeInCapacity"
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cooldown = 300
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autoscaling_group_name = aws_autoscaling_group.main.name
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}
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resource "aws_cloudwatch_metric_alarm" "cpu_high" {
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alarm_name = "cpu-high"
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comparison_operator = "GreaterThanThreshold"
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evaluation_periods = "2"
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metric_name = "CPUUtilization"
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namespace = "AWS/EC2"
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period = "60"
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statistic = "Average"
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threshold = "80"
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alarm_actions = [aws_autoscaling_policy.scale_up.arn]
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}
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```
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## Cost Optimization Checklist
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- [ ] Implement cost allocation tags
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- [ ] Delete unused resources (EBS, EIPs, snapshots)
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- [ ] Right-size instances based on utilization
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- [ ] Use reserved capacity for steady workloads
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- [ ] Implement auto-scaling
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- [ ] Optimize storage classes
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- [ ] Use lifecycle policies
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- [ ] Enable cost anomaly detection
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- [ ] Set budget alerts
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- [ ] Review costs weekly
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- [ ] Use spot/preemptible instances
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- [ ] Optimize data transfer costs
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- [ ] Implement caching layers
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- [ ] Use managed services
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- [ ] Monitor and optimize continuously
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## Tools
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- **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer
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- **Azure:** Cost Management, Advisor
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- **GCP:** Cost Management, Recommender
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- **Multi-cloud:** CloudHealth, Cloudability, Kubecost
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## Reference Files
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- `references/tagging-standards.md` - Tagging conventions
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- `assets/cost-analysis-template.xlsx` - Cost analysis spreadsheet
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## Related Skills
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- `terraform-module-library` - For resource provisioning
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- `multi-cloud-architecture` - For cloud selection
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