[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M783RKVFRNC464MSQFCTNETH":3,"skill-same:01M783RKVFRNC464MSQFCTNETH":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":14,"repositoryHtmlUrl":27,"repositoryHomepageUrl":6,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":29,"install":30,"resources":43},"01M783RKVFRNC464MSQFCTNETH","aws-solution-architect",null,"Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI\u002FCD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.","motivatedc-creator\u002FSaafy","motivatedc-creator","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F250662783?v=4","01M782H2D8C7XJGXMJC3YHQAWK","Happy Haris","01M782H2ERMBYEM1NSPPPC06S0",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fengineering-team\u002Fskills\u002Faws-solution-architect\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"aws-solution-architect\"\ndescription: Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI\u002FCD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.\n---\n\n# AWS Solution Architect\n\nDesign scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.\n\n---\n\n## Workflow\n\n### Step 1: Gather Requirements\n\nCollect application specifications:\n\n```\n- Application type (web app, mobile backend, data pipeline, SaaS)\n- Expected users and requests per second\n- Budget constraints (monthly spend limit)\n- Team size and AWS experience level\n- Compliance requirements (GDPR, HIPAA, SOC 2)\n- Availability requirements (SLA, RPO\u002FRTO)\n```\n\n### Step 2: Design Architecture\n\nRun the architecture designer to get pattern recommendations:\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json\n```\n\n**Example output:**\n\n```json\n{\n  \"recommended_pattern\": \"serverless_web\",\n  \"service_stack\": [\"S3\", \"CloudFront\", \"API Gateway\", \"Lambda\", \"DynamoDB\", \"Cognito\"],\n  \"estimated_monthly_cost_usd\": 35,\n  \"pros\": [\"Low ops overhead\", \"Pay-per-use\", \"Auto-scaling\"],\n  \"cons\": [\"Cold starts\", \"15-min Lambda limit\", \"Eventual consistency\"]\n}\n```\n\nSelect from recommended patterns:\n- **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB\n- **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions\n- **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache\n- **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito\n\nSee `references\u002Farchitecture_patterns.md` for detailed pattern specifications.\n\n**Validation checkpoint:** Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.\n\n### Step 3: Generate IaC Templates\n\nCreate infrastructure-as-code for the selected pattern:\n\n```bash\n# Serverless stack (CloudFormation)\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Example CloudFormation YAML output (core serverless resources):**\n\n```yaml\nAWSTemplateFormatVersion: '2010-09-09'\nTransform: AWS::Serverless-2016-10-31\n\nParameters:\n  AppName:\n    Type: String\n    Default: my-app\n\nResources:\n  ApiFunction:\n    Type: AWS::Serverless::Function\n    Properties:\n      Handler: index.handler\n      Runtime: nodejs20.x\n      MemorySize: 512\n      Timeout: 30\n      Environment:\n        Variables:\n          TABLE_NAME: !Ref DataTable\n      Policies:\n        - DynamoDBCrudPolicy:\n            TableName: !Ref DataTable\n      Events:\n        ApiEvent:\n          Type: Api\n          Properties:\n            Path: \u002F{proxy+}\n            Method: ANY\n\n  DataTable:\n    Type: AWS::DynamoDB::Table\n    Properties:\n      BillingMode: PAY_PER_REQUEST\n      AttributeDefinitions:\n        - AttributeName: pk\n          AttributeType: S\n        - AttributeName: sk\n          AttributeType: S\n      KeySchema:\n        - AttributeName: pk\n          KeyType: HASH\n        - AttributeName: sk\n          KeyType: RANGE\n```\n\n> Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by `serverless_stack.py` and also available in `references\u002Farchitecture_patterns.md`.\n\n**Example CDK TypeScript snippet (three-tier pattern):**\n\n```typescript\nimport * as ecs from 'aws-cdk-lib\u002Faws-ecs';\nimport * as ec2 from 'aws-cdk-lib\u002Faws-ec2';\nimport * as rds from 'aws-cdk-lib\u002Faws-rds';\n\nconst vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });\n\nconst cluster = new ecs.Cluster(this, 'AppCluster', { vpc });\n\nconst db = new rds.ServerlessCluster(this, 'AppDb', {\n  engine: rds.DatabaseClusterEngine.auroraPostgres({\n    version: rds.AuroraPostgresEngineVersion.VER_15_2,\n  }),\n  vpc,\n  scaling: { minCapacity: 0.5, maxCapacity: 4 },\n});\n```\n\n### Step 4: Review Costs\n\nAnalyze estimated costs and optimization opportunities:\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources current_setup.json --monthly-spend 2000\n```\n\n**Example output:**\n\n```json\n{\n  \"current_monthly_usd\": 2000,\n  \"recommendations\": [\n    { \"action\": \"Right-size RDS db.r5.2xlarge → db.r5.large\", \"savings_usd\": 420, \"priority\": \"high\" },\n    { \"action\": \"Purchase 1-yr Compute Savings Plan at 40% utilization\", \"savings_usd\": 310, \"priority\": \"high\" },\n    { \"action\": \"Move S3 objects >90 days to Glacier Instant Retrieval\", \"savings_usd\": 85, \"priority\": \"medium\" }\n  ],\n  \"total_potential_savings_usd\": 815\n}\n```\n\nOutput includes:\n- Monthly cost breakdown by service\n- Right-sizing recommendations\n- Savings Plans opportunities\n- Potential monthly savings\n\n### Step 5: Deploy\n\nDeploy the generated infrastructure:\n\n```bash\n# CloudFormation\naws cloudformation create-stack \\\n  --stack-name my-app-stack \\\n  --template-body file:\u002F\u002Ftemplate.yaml \\\n  --capabilities CAPABILITY_IAM\n\n# CDK\ncdk deploy\n\n# Terraform\nterraform init && terraform apply\n```\n\n### Step 6: Validate and Handle Failures\n\nVerify deployment and set up monitoring:\n\n```bash\n# Check stack status\naws cloudformation describe-stacks --stack-name my-app-stack\n\n# Set up CloudWatch alarms\naws cloudwatch put-metric-alarm --alarm-name high-errors ...\n```\n\n**If stack creation fails:**\n\n1. Check the failure reason:\n   ```bash\n   aws cloudformation describe-stack-events \\\n     --stack-name my-app-stack \\\n     --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'\n   ```\n2. Review CloudWatch Logs for Lambda or ECS errors.\n3. Fix the template or resource configuration.\n4. Delete the failed stack before retrying:\n   ```bash\n   aws cloudformation delete-stack --stack-name my-app-stack\n   # Wait for deletion\n   aws cloudformation wait stack-delete-complete --stack-name my-app-stack\n   # Redeploy\n   aws cloudformation create-stack ...\n   ```\n\n**Common failure causes:**\n- IAM permission errors → verify `--capabilities CAPABILITY_IAM` and role trust policies\n- Resource limit exceeded → request quota increase via Service Quotas console\n- Invalid template syntax → run `aws cloudformation validate-template --template-body file:\u002F\u002Ftemplate.yaml` before deploying\n\n---\n\n## Tools\n\n### architecture_designer.py\n\nGenerates architecture patterns based on requirements.\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json --output design.json\n```\n\n**Input:** JSON with app type, scale, budget, compliance needs\n**Output:** Recommended pattern, service stack, cost estimate, pros\u002Fcons\n\n### serverless_stack.py\n\nCreates serverless CloudFormation templates.\n\n```bash\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Output:** Production-ready CloudFormation YAML with:\n- API Gateway + Lambda\n- DynamoDB table\n- Cognito user pool\n- IAM roles with least privilege\n- CloudWatch logging\n\n### cost_optimizer.py\n\nAnalyzes costs and recommends optimizations.\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources inventory.json --monthly-spend 5000\n```\n\n**Output:** Recommendations for:\n- Idle resource removal\n- Instance right-sizing\n- Reserved capacity purchases\n- Storage tier transitions\n- NAT Gateway alternatives\n\n---\n\n## Quick Start\n\n### MVP Architecture (\u003C $100\u002Fmonth)\n\n```\nAsk: \"Design a serverless MVP backend for a mobile app with 1000 users\"\n\nResult:\n- Lambda + API Gateway for API\n- DynamoDB pay-per-request for data\n- Cognito for authentication\n- S3 + CloudFront for static assets\n- Estimated: $20-50\u002Fmonth\n```\n\n### Scaling Architecture ($500-2000\u002Fmonth)\n\n```\nAsk: \"Design a scalable architecture for a SaaS platform with 50k users\"\n\nResult:\n- ECS Fargate for containerized API\n- Aurora Serverless for relational data\n- ElastiCache for session caching\n- CloudFront for CDN\n- CodePipeline for CI\u002FCD\n- Multi-AZ deployment\n```\n\n### Cost Optimization\n\n```\nAsk: \"Optimize my AWS setup to reduce costs by 30%. Current spend: $3000\u002Fmonth\"\n\nProvide: Current resource inventory (EC2, RDS, S3, etc.)\n\nResult:\n- Idle resource identification\n- Right-sizing recommendations\n- Savings Plans analysis\n- Storage lifecycle policies\n- Target savings: $900\u002Fmonth\n```\n\n### IaC Generation\n\n```\nAsk: \"Generate CloudFormation for a three-tier web app with auto-scaling\"\n\nResult:\n- VPC with public\u002Fprivate subnets\n- ALB with HTTPS\n- ECS Fargate with auto-scaling\n- Aurora with read replicas\n- Security groups and IAM roles\n```\n\n---\n\n## Input Requirements\n\nProvide these details for architecture design:\n\n| Requirement | Description | Example |\n|-------------|-------------|---------|\n| Application type | What you're building | SaaS platform, mobile backend |\n| Expected scale | Users, requests\u002Fsec | 10k users, 100 RPS |\n| Budget | Monthly AWS limit | $500\u002Fmonth max |\n| Team context | Size, AWS experience | 3 devs, intermediate |\n| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |\n| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |\n\n**JSON Format:**\n\n```json\n{\n  \"application_type\": \"saas_platform\",\n  \"expected_users\": 10000,\n  \"requests_per_second\": 100,\n  \"budget_monthly_usd\": 500,\n  \"team_size\": 3,\n  \"aws_experience\": \"intermediate\",\n  \"compliance\": [\"SOC2\"],\n  \"availability_sla\": \"99.9%\"\n}\n```\n\n---\n\n## Output Formats\n\n### Architecture Design\n\n- Pattern recommendation with rationale\n- Service stack diagram (ASCII)\n- Monthly cost estimate and trade-offs\n\n### IaC Templates\n\n- **CloudFormation YAML**: Production-ready SAM\u002FCFN templates\n- **CDK TypeScript**: Type-safe infrastructure code\n- **Terraform HCL**: Multi-cloud compatible configs\n\n### Cost Analysis\n\n- Current spend breakdown with optimization recommendations\n- Priority action list (high\u002Fmedium\u002Flow) and implementation checklist\n\n---\n\n## Reference Documentation\n\n| Document | Contents |\n|----------|----------|\n| `references\u002Farchitecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region |\n| `references\u002Fservice_selection.md` | Decision matrices for compute, database, storage, messaging |\n| `references\u002Fbest_practices.md` | Serverless design, cost optimization, security hardening, scalability |\n","# AWS Solution Architect\n\nDesign scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.\n\n---\n\n## Workflow\n\n### Step 1: Gather Requirements\n\nCollect application specifications:\n\n```\n- Application type (web app, mobile backend, data pipeline, SaaS)\n- Expected users and requests per second\n- Budget constraints (monthly spend limit)\n- Team size and AWS experience level\n- Compliance requirements (GDPR, HIPAA, SOC 2)\n- Availability requirements (SLA, RPO\u002FRTO)\n```\n\n### Step 2: Design Architecture\n\nRun the architecture designer to get pattern recommendations:\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json\n```\n\n**Example output:**\n\n```json\n{\n  \"recommended_pattern\": \"serverless_web\",\n  \"service_stack\": [\"S3\", \"CloudFront\", \"API Gateway\", \"Lambda\", \"DynamoDB\", \"Cognito\"],\n  \"estimated_monthly_cost_usd\": 35,\n  \"pros\": [\"Low ops overhead\", \"Pay-per-use\", \"Auto-scaling\"],\n  \"cons\": [\"Cold starts\", \"15-min Lambda limit\", \"Eventual consistency\"]\n}\n```\n\nSelect from recommended patterns:\n- **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB\n- **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions\n- **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache\n- **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito\n\nSee `references\u002Farchitecture_patterns.md` for detailed pattern specifications.\n\n**Validation checkpoint:** Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.\n\n### Step 3: Generate IaC Templates\n\nCreate infrastructure-as-code for the selected pattern:\n\n```bash\n# Serverless stack (CloudFormation)\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Example CloudFormation YAML output (core serverless resources):**\n\n```yaml\nAWSTemplateFormatVersion: '2010-09-09'\nTransform: AWS::Serverless-2016-10-31\n\nParameters:\n  AppName:\n    Type: String\n    Default: my-app\n\nResources:\n  ApiFunction:\n    Type: AWS::Serverless::Function\n    Properties:\n      Handler: index.handler\n      Runtime: nodejs20.x\n      MemorySize: 512\n      Timeout: 30\n      Environment:\n        Variables:\n          TABLE_NAME: !Ref DataTable\n      Policies:\n        - DynamoDBCrudPolicy:\n            TableName: !Ref DataTable\n      Events:\n        ApiEvent:\n          Type: Api\n          Properties:\n            Path: \u002F{proxy+}\n            Method: ANY\n\n  DataTable:\n    Type: AWS::DynamoDB::Table\n    Properties:\n      BillingMode: PAY_PER_REQUEST\n      AttributeDefinitions:\n        - AttributeName: pk\n          AttributeType: S\n        - AttributeName: sk\n          AttributeType: S\n      KeySchema:\n        - AttributeName: pk\n          KeyType: HASH\n        - AttributeName: sk\n          KeyType: RANGE\n```\n\n> Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by `serverless_stack.py` and also available in `references\u002Farchitecture_patterns.md`.\n\n**Example CDK TypeScript snippet (three-tier pattern):**\n\n```typescript\nimport * as ecs from 'aws-cdk-lib\u002Faws-ecs';\nimport * as ec2 from 'aws-cdk-lib\u002Faws-ec2';\nimport * as rds from 'aws-cdk-lib\u002Faws-rds';\n\nconst vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });\n\nconst cluster = new ecs.Cluster(this, 'AppCluster', { vpc });\n\nconst db = new rds.ServerlessCluster(this, 'AppDb', {\n  engine: rds.DatabaseClusterEngine.auroraPostgres({\n    version: rds.AuroraPostgresEngineVersion.VER_15_2,\n  }),\n  vpc,\n  scaling: { minCapacity: 0.5, maxCapacity: 4 },\n});\n```\n\n### Step 4: Review Costs\n\nAnalyze estimated costs and optimization opportunities:\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources current_setup.json --monthly-spend 2000\n```\n\n**Example output:**\n\n```json\n{\n  \"current_monthly_usd\": 2000,\n  \"recommendations\": [\n    { \"action\": \"Right-size RDS db.r5.2xlarge → db.r5.large\", \"savings_usd\": 420, \"priority\": \"high\" },\n    { \"action\": \"Purchase 1-yr Compute Savings Plan at 40% utilization\", \"savings_usd\": 310, \"priority\": \"high\" },\n    { \"action\": \"Move S3 objects >90 days to Glacier Instant Retrieval\", \"savings_usd\": 85, \"priority\": \"medium\" }\n  ],\n  \"total_potential_savings_usd\": 815\n}\n```\n\nOutput includes:\n- Monthly cost breakdown by service\n- Right-sizing recommendations\n- Savings Plans opportunities\n- Potential monthly savings\n\n### Step 5: Deploy\n\nDeploy the generated infrastructure:\n\n```bash\n# CloudFormation\naws cloudformation create-stack \\\n  --stack-name my-app-stack \\\n  --template-body file:\u002F\u002Ftemplate.yaml \\\n  --capabilities CAPABILITY_IAM\n\n# CDK\ncdk deploy\n\n# Terraform\nterraform init && terraform apply\n```\n\n### Step 6: Validate and Handle Failures\n\nVerify deployment and set up monitoring:\n\n```bash\n# Check stack status\naws cloudformation describe-stacks --stack-name my-app-stack\n\n# Set up CloudWatch alarms\naws cloudwatch put-metric-alarm --alarm-name high-errors ...\n```\n\n**If stack creation fails:**\n\n1. Check the failure reason:\n   ```bash\n   aws cloudformation describe-stack-events \\\n     --stack-name my-app-stack \\\n     --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'\n   ```\n2. Review CloudWatch Logs for Lambda or ECS errors.\n3. Fix the template or resource configuration.\n4. Delete the failed stack before retrying:\n   ```bash\n   aws cloudformation delete-stack --stack-name my-app-stack\n   # Wait for deletion\n   aws cloudformation wait stack-delete-complete --stack-name my-app-stack\n   # Redeploy\n   aws cloudformation create-stack ...\n   ```\n\n**Common failure causes:**\n- IAM permission errors → verify `--capabilities CAPABILITY_IAM` and role trust policies\n- Resource limit exceeded → request quota increase via Service Quotas console\n- Invalid template syntax → run `aws cloudformation validate-template --template-body file:\u002F\u002Ftemplate.yaml` before deploying\n\n---\n\n## Tools\n\n### architecture_designer.py\n\nGenerates architecture patterns based on requirements.\n\n```bash\npython scripts\u002Farchitecture_designer.py --input requirements.json --output design.json\n```\n\n**Input:** JSON with app type, scale, budget, compliance needs\n**Output:** Recommended pattern, service stack, cost estimate, pros\u002Fcons\n\n### serverless_stack.py\n\nCreates serverless CloudFormation templates.\n\n```bash\npython scripts\u002Fserverless_stack.py --app-name my-app --region us-east-1\n```\n\n**Output:** Production-ready CloudFormation YAML with:\n- API Gateway + Lambda\n- DynamoDB table\n- Cognito user pool\n- IAM roles with least privilege\n- CloudWatch logging\n\n### cost_optimizer.py\n\nAnalyzes costs and recommends optimizations.\n\n```bash\npython scripts\u002Fcost_optimizer.py --resources inventory.json --monthly-spend 5000\n```\n\n**Output:** Recommendations for:\n- Idle resource removal\n- Instance right-sizing\n- Reserved capacity purchases\n- Storage tier transitions\n- NAT Gateway alternatives\n\n---\n\n## Quick Start\n\n### MVP Architecture (\u003C $100\u002Fmonth)\n\n```\nAsk: \"Design a serverless MVP backend for a mobile app with 1000 users\"\n\nResult:\n- Lambda + API Gateway for API\n- DynamoDB pay-per-request for data\n- Cognito for authentication\n- S3 + CloudFront for static assets\n- Estimated: $20-50\u002Fmonth\n```\n\n### Scaling Architecture ($500-2000\u002Fmonth)\n\n```\nAsk: \"Design a scalable architecture for a SaaS platform with 50k users\"\n\nResult:\n- ECS Fargate for containerized API\n- Aurora Serverless for relational data\n- ElastiCache for session caching\n- CloudFront for CDN\n- CodePipeline for CI\u002FCD\n- Multi-AZ deployment\n```\n\n### Cost Optimization\n\n```\nAsk: \"Optimize my AWS setup to reduce costs by 30%. Current spend: $3000\u002Fmonth\"\n\nProvide: Current resource inventory (EC2, RDS, S3, etc.)\n\nResult:\n- Idle resource identification\n- Right-sizing recommendations\n- Savings Plans analysis\n- Storage lifecycle policies\n- Target savings: $900\u002Fmonth\n```\n\n### IaC Generation\n\n```\nAsk: \"Generate CloudFormation for a three-tier web app with auto-scaling\"\n\nResult:\n- VPC with public\u002Fprivate subnets\n- ALB with HTTPS\n- ECS Fargate with auto-scaling\n- Aurora with read replicas\n- Security groups and IAM roles\n```\n\n---\n\n## Input Requirements\n\nProvide these details for architecture design:\n\n| Requirement | Description | Example |\n|-------------|-------------|---------|\n| Application type | What you're building | SaaS platform, mobile backend |\n| Expected scale | Users, requests\u002Fsec | 10k users, 100 RPS |\n| Budget | Monthly AWS limit | $500\u002Fmonth max |\n| Team context | Size, AWS experience | 3 devs, intermediate |\n| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |\n| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |\n\n**JSON Format:**\n\n```json\n{\n  \"application_type\": \"saas_platform\",\n  \"expected_users\": 10000,\n  \"requests_per_second\": 100,\n  \"budget_monthly_usd\": 500,\n  \"team_size\": 3,\n  \"aws_experience\": \"intermediate\",\n  \"compliance\": [\"SOC2\"],\n  \"availability_sla\": \"99.9%\"\n}\n```\n\n---\n\n## Output Formats\n\n### Architecture Design\n\n- Pattern recommendation with rationale\n- Service stack diagram (ASCII)\n- Monthly cost estimate and trade-offs\n\n### IaC Templates\n\n- **CloudFormation YAML**: Production-ready SAM\u002FCFN templates\n- **CDK TypeScript**: Type-safe infrastructure code\n- **Terraform HCL**: Multi-cloud compatible configs\n\n### Cost Analysis\n\n- Current spend breakdown with optimization recommendations\n- Priority action list (high\u002Fmedium\u002Flow) and implementation checklist\n\n---\n\n## Reference Documentation\n\n| Document | Contents |\n|----------|----------|\n| `references\u002Farchitecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region |\n| `references\u002Fservice_selection.md` | Decision matrices for compute, database, storage, messaging |\n| `references\u002Fbest_practices.md` | Serverless design, cost optimization, security hardening, scalability |\n",{"name":5,"description":7},"main","55ae24ca5523c0499dd70b2874ef674a0e66a85b","engineering-team\u002Fskills\u002Faws-solution-architect","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator","2026-07-04 02:45:55",{"source":8,"skillName":5,"commands":31,"prompt":41,"zipAvailable":42},[32,35,38],{"tool":33,"command":34},"npx","npx skills add motivatedc-creator\u002FSaafy --skill aws-solution-architect",{"tool":36,"command":37},"bunx","bunx skills add motivatedc-creator\u002FSaafy --skill 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a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M7854YEAAV883M2PKS04TQEK","research","Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends\u002Fsentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Use when the user makes any research request that doesn't obviously match a more-specific specialist skill (e.g., \"research [topic]\", \"look into [topic]\", \"what do we know about [topic]\", \"investigate [topic]\", \"find me information on [topic]\", \"do some research on [topic]\", \"I need to understand [topic]\"). Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Fresearch\u002Fskills\u002Fresearch\u002FSKILL.md",[],[],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":19},"01M7854NCGDBQ6Q0QG2YY4VHSX","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":19},"01M78549YZMPYGC1F6S7ZJ4VF8","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":19},"01M78541PWEQMBPN91PEZZK8H7","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":19},"01M7853P7CWJNW5G0Z99YB6H5D","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fmotivatedc-creator\u002FSaafy\u002Fblob\u002Fmain\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[]]