AGENT SKILL

aws-solution-architect

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/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

License 许可
MIT
最近更新
2026-07-04
Stars
★ 0
Forks
0

01 前言 Front Matter

name
aws-solution-architect
description
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/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

02 资源文件 8

03 SKILL.md 正文

AWS Solution Architect

Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.


Workflow

Step 1: Gather Requirements

Collect application specifications:

- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and AWS experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)

Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

python scripts/architecture_designer.py --input requirements.json

Example output:

{
  "recommended_pattern": "serverless_web",
  "service_stack": ["S3", "CloudFront", "API Gateway", "Lambda", "DynamoDB", "Cognito"],
  "estimated_monthly_cost_usd": 35,
  "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling"],
  "cons": ["Cold starts", "15-min Lambda limit", "Eventual consistency"]
}

Select from recommended patterns:

  • Serverless Web: S3 + CloudFront + API Gateway + Lambda + DynamoDB
  • Event-Driven Microservices: EventBridge + Lambda + SQS + Step Functions
  • Three-Tier: ALB + ECS Fargate + Aurora + ElastiCache
  • GraphQL Backend: AppSync + Lambda + DynamoDB + Cognito

See references/architecture_patterns.md for detailed pattern specifications.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Generate IaC Templates

Create infrastructure-as-code for the selected pattern:

# Serverless stack (CloudFormation)
python scripts/serverless_stack.py --app-name my-app --region us-east-1

Example CloudFormation YAML output (core serverless resources):

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Parameters:
  AppName:
    Type: String
    Default: my-app

Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs20.x
      MemorySize: 512
      Timeout: 30
      Environment:
        Variables:
          TABLE_NAME: !Ref DataTable
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref DataTable
      Events:
        ApiEvent:
          Type: Api
          Properties:
            Path: /{proxy+}
            Method: ANY

  DataTable:
    Type: AWS::DynamoDB::Table
    Properties:
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: pk
          AttributeType: S
        - AttributeName: sk
          AttributeType: S
      KeySchema:
        - AttributeName: pk
          KeyType: HASH
        - AttributeName: sk
          KeyType: RANGE

Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by serverless_stack.py and also available in references/architecture_patterns.md.

Example CDK TypeScript snippet (three-tier pattern):

import * as ecs from 'aws-cdk-lib/aws-ecs';
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as rds from 'aws-cdk-lib/aws-rds';

const vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });

const cluster = new ecs.Cluster(this, 'AppCluster', { vpc });

const db = new rds.ServerlessCluster(this, 'AppDb', {
  engine: rds.DatabaseClusterEngine.auroraPostgres({
    version: rds.AuroraPostgresEngineVersion.VER_15_2,
  }),
  vpc,
  scaling: { minCapacity: 0.5, maxCapacity: 4 },
});

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

Example output:

{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size RDS db.r5.2xlarge → db.r5.large", "savings_usd": 420, "priority": "high" },
    { "action": "Purchase 1-yr Compute Savings Plan at 40% utilization", "savings_usd": 310, "priority": "high" },
    { "action": "Move S3 objects >90 days to Glacier Instant Retrieval", "savings_usd": 85, "priority": "medium" }
  ],
  "total_potential_savings_usd": 815
}

Output includes:

  • Monthly cost breakdown by service
  • Right-sizing recommendations
  • Savings Plans opportunities
  • Potential monthly savings

Step 5: Deploy

Deploy the generated infrastructure:

# CloudFormation
aws cloudformation create-stack \
  --stack-name my-app-stack \
  --template-body file://template.yaml \
  --capabilities CAPABILITY_IAM

# CDK
cdk deploy

# Terraform
terraform init && terraform apply

Step 6: Validate and Handle Failures

Verify deployment and set up monitoring:

# Check stack status
aws cloudformation describe-stacks --stack-name my-app-stack

# Set up CloudWatch alarms
aws cloudwatch put-metric-alarm --alarm-name high-errors ...

If stack creation fails:

  1. Check the failure reason:
    aws cloudformation describe-stack-events \
      --stack-name my-app-stack \
      --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'
    
  2. Review CloudWatch Logs for Lambda or ECS errors.
  3. Fix the template or resource configuration.
  4. Delete the failed stack before retrying:
    aws cloudformation delete-stack --stack-name my-app-stack
    # Wait for deletion
    aws cloudformation wait stack-delete-complete --stack-name my-app-stack
    # Redeploy
    aws cloudformation create-stack ...
    

Common failure causes:

  • IAM permission errors → verify --capabilities CAPABILITY_IAM and role trust policies
  • Resource limit exceeded → request quota increase via Service Quotas console
  • Invalid template syntax → run aws cloudformation validate-template --template-body file://template.yaml before deploying

Tools

architecture_designer.py

Generates architecture patterns based on requirements.

python scripts/architecture_designer.py --input requirements.json --output design.json

Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons

serverless_stack.py

Creates serverless CloudFormation templates.

python scripts/serverless_stack.py --app-name my-app --region us-east-1

Output: Production-ready CloudFormation YAML with:

  • API Gateway + Lambda
  • DynamoDB table
  • Cognito user pool
  • IAM roles with least privilege
  • CloudWatch logging

cost_optimizer.py

Analyzes costs and recommends optimizations.

python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000

Output: Recommendations for:

  • Idle resource removal
  • Instance right-sizing
  • Reserved capacity purchases
  • Storage tier transitions
  • NAT Gateway alternatives

Quick Start

MVP Architecture (< $100/month)

Ask: "Design a serverless MVP backend for a mobile app with 1000 users"

Result:
- Lambda + API Gateway for API
- DynamoDB pay-per-request for data
- Cognito for authentication
- S3 + CloudFront for static assets
- Estimated: $20-50/month

Scaling Architecture ($500-2000/month)

Ask: "Design a scalable architecture for a SaaS platform with 50k users"

Result:
- ECS Fargate for containerized API
- Aurora Serverless for relational data
- ElastiCache for session caching
- CloudFront for CDN
- CodePipeline for CI/CD
- Multi-AZ deployment

Cost Optimization

Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month"

Provide: Current resource inventory (EC2, RDS, S3, etc.)

Result:
- Idle resource identification
- Right-sizing recommendations
- Savings Plans analysis
- Storage lifecycle policies
- Target savings: $900/month

IaC Generation

Ask: "Generate CloudFormation for a three-tier web app with auto-scaling"

Result:
- VPC with public/private subnets
- ALB with HTTPS
- ECS Fargate with auto-scaling
- Aurora with read replicas
- Security groups and IAM roles

Input Requirements

Provide these details for architecture design:

Requirement Description Example
Application type What you're building SaaS platform, mobile backend
Expected scale Users, requests/sec 10k users, 100 RPS
Budget Monthly AWS limit $500/month max
Team context Size, AWS experience 3 devs, intermediate
Compliance Regulatory needs HIPAA, GDPR, SOC 2
Availability Uptime requirements 99.9% SLA, 1hr RPO

JSON Format:

{
  "application_type": "saas_platform",
  "expected_users": 10000,
  "requests_per_second": 100,
  "budget_monthly_usd": 500,
  "team_size": 3,
  "aws_experience": "intermediate",
  "compliance": ["SOC2"],
  "availability_sla": "99.9%"
}

Output Formats

Architecture Design

  • Pattern recommendation with rationale
  • Service stack diagram (ASCII)
  • Monthly cost estimate and trade-offs

IaC Templates

  • CloudFormation YAML: Production-ready SAM/CFN templates
  • CDK TypeScript: Type-safe infrastructure code
  • Terraform HCL: Multi-cloud compatible configs

Cost Analysis

  • Current spend breakdown with optimization recommendations
  • Priority action list (high/medium/low) and implementation checklist

Reference Documentation

Document Contents
references/architecture_patterns.md 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region
references/service_selection.md Decision matrices for compute, database, storage, messaging
references/best_practices.md Serverless design, cost optimization, security hardening, scalability

04 同仓库更多 Skills

motivatedc-creator
motivatedc-creator/Saafy

syllabus

Generates 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.

★ 0
motivatedc-creator
motivatedc-creator/Saafy

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/sentiment, 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.

★ 0
motivatedc-creator
motivatedc-creator/Saafy

pulse

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), 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.

★ 0
motivatedc-creator
motivatedc-creator/Saafy

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 / freedom-to-operate / competitive landscape / acquisition diligence / 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.

★ 0
motivatedc-creator
motivatedc-creator/Saafy

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/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, 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.

★ 0
motivatedc-creator
motivatedc-creator/Saafy

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 / Decomposition / 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 (5/10/20 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/OpenAlex (or Consensus) query.

★ 0