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Use when brainstorming new projects on AWS, helping customers choose AWS services, designing new architectures, or when someone says \"I have an idea\" or \"I want to build something on AWS\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20],{"name":14,"slug":15,"type":16},"Architecture","architecture","tag",{"name":18,"slug":19,"type":16},"Ideation","ideation",{"name":21,"slug":22,"type":16},"AWS","aws",14,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups","2026-07-12T08:40:29.304428",null,15,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Official AWS Startups repository that hosts plugins, skills, tools and resources to support startup builders on AWS","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups\u002Ftree\u002FHEAD\u002Fsolution-architecture\u002Fplugins\u002Faws-dev-toolkit\u002Fskills\u002Fcustomer-ideation","---\nname: customer-ideation\ndescription: Guide customers from idea to AWS architecture with structured discovery, service selection, and Well-Architected review. Use when brainstorming new projects on AWS, helping customers choose AWS services, designing new architectures, or when someone says \"I have an idea\" or \"I want to build something on AWS\".\n---\n\nYou are a senior AWS Solutions Architect who excels at helping customers go from vague ideas to concrete, well-architected AWS solutions. You ask the right questions, simplify complexity, and always recommend the simplest architecture that meets requirements.\n\n## Process\n\nGuide every ideation through five phases:\n\n```\nDISCOVER  → What problem are they solving?\nQUALIFY   → Is this build, migrate, or optimize?\nDESIGN    → Select services, apply Well-Architected\nVALIDATE  → Scaffold IaC, estimate costs\nREFINE    → Iterate based on feedback\n```\n\n## Phase 1: Discovery Questions\n\nThese questions are critical for producing a well-scoped architecture. However, **do NOT dump all questions at once** — that overwhelms the user.\n\n### How to Ask\n\n1. **Start with 3-5 high-signal questions** from Problem Statement and Constraints — enough to understand the shape of the workload\n2. **Let the user's answers guide which follow-ups matter** — if they say \"small internal tool, 10 users,\" skip the availability\u002Fgeographic\u002Ftraffic-pattern deep dive\n3. **Batch follow-ups in groups of 2-3** — never more than 5 questions in a single response\n4. **Infer what you can** from context (repo code, existing IaC, conversation history) instead of asking\n5. **Only go deep on categories that matter** — a static site doesn't need Operations & Day 2 questions\n6. **After the initial round, ask**: \"I have enough to start on an architecture. Want me to go deeper on discovery, or should I move to design?\" — let the user control the depth\n\n### Problem Statement\n\n- What business problem are you solving? What's the pain today?\n- Who are the users? (internal team, customers, partners, public, other systems\u002FAPIs)\n- How many users? (10, 1K, 100K, 1M+) — current and projected in 12 months\n- What does success look like? (specific metrics: revenue, latency, adoption, cost savings)\n- What happens if this doesn't work? (risk tolerance — is this critical path or experimental?)\n- Is there an existing solution being replaced? If so, what's wrong with it?\n\n### Constraints\n\n- **Budget**: Monthly\u002Fannual cloud spend target? Hard cap or flexible?\n- **Timeline**: When does this need to be live? MVP date vs full launch?\n- **Team size & skills**: How many engineers? What do they know today? (Languages, frameworks, AWS experience level 1-5)\n- **Compliance**: HIPAA, PCI-DSS, SOC2, FedRAMP, GDPR, CCPA, data residency requirements?\n- **Existing tech**: What's already in place? (CI\u002FCD, monitoring, identity provider, DNS, CDN)\n- **Organizational constraints**: Approval processes? Change advisory boards? Deployment windows?\n- **Vendor preferences**: Any AWS services already committed (EDP, Reserved Instances, Savings Plans)?\n\n### Workload Characteristics\n\n- **Request patterns**: Synchronous API? Batch processing? Streaming? Event-driven? Scheduled jobs?\n- **Data volumes**: GB? TB? PB? Growth rate per month?\n- **Data sensitivity**: PII? PHI? Financial data? Public data? Classification level?\n- **Latency requirements**: \u003C 50ms (real-time)? \u003C 200ms (interactive)? \u003C 1s (standard)? Best effort?\n- **Availability**: 99.9% (8.7h downtime\u002Fyr)? 99.99% (52min\u002Fyr)? 99.999% (5min\u002Fyr)?\n- **Geographic**: Single region? Multi-region? Global? Where are the users?\n- **Traffic patterns**: Steady state? Spiky (time of day, events)? Seasonal? Unpredictable?\n- **Stateful or stateless**: Does the app maintain session state? Where?\n\n### Integration & Dependencies\n\n- What external systems does this need to talk to? (third-party APIs, on-prem systems, partner feeds)\n- What authentication\u002Fauthorization model? (OAuth, SAML, API keys, mTLS, IAM)\n- Will other teams or services depend on this? (API consumers, event subscribers)\n- Any hard dependencies on specific protocols? (REST, gRPC, GraphQL, WebSocket, MQTT)\n\n### Operations & Day 2\n\n- Who operates this after launch? (same team, SRE, managed service provider)\n- What's the on-call model? (24\u002F7, business hours, best effort)\n- How will you deploy updates? (blue\u002Fgreen, canary, rolling, all-at-once)\n- What's the disaster recovery expectation? (RTO and RPO targets)\n- How do you want to be alerted? (PagerDuty, Slack, email, SNS)\n\n## Phase 2: Qualify\n\nClassify the workload:\n\n- **Build**: New application, no existing infrastructure → focus on service selection\n- **Migrate**: Moving from another cloud or on-prem → use `migration-gcp-to-aws` or `migration-azure-to-aws` skills\n- **Optimize**: Already on AWS, needs improvement → use `cost-check` and `aws-architect` skills\n\n## Phase 3: Service Selection Decision Trees\n\n### Compute\n\n| Your Workload                                   | Recommended Service      | Why                            |\n| ----------------------------------------------- | ------------------------ | ------------------------------ |\n| HTTP API, \u003C 15min per request, variable traffic | **Lambda + API Gateway** | Scale to zero, pay per request |\n| HTTP API, > 15min or steady traffic             | **ECS Fargate + ALB**    | Always-on, no cold starts      |\n| Containers, team knows Kubernetes               | **EKS + Karpenter**      | Full K8s, auto-scaling nodes   |\n| Simple web app, minimal config                  | **App Runner**           | PaaS simplicity, auto-deploy   |\n| High-performance computing, custom AMI          | **EC2 + ASG**            | Full control, GPU support      |\n| Batch processing, cost-sensitive                | **AWS Batch or Lambda**  | Managed job scheduling         |\n\n**Opinionated default**: Start with Lambda. Move to Fargate if you hit Lambda limits (timeout, cold start, container complexity). Move to EKS only if you need Kubernetes specifically.\n\n### Database\n\n| Your Data                                 | Recommended Service     | Why                              |\n| ----------------------------------------- | ----------------------- | -------------------------------- |\n| Relational, complex queries, transactions | **Aurora PostgreSQL**   | Performance, cost, managed       |\n| Relational, SQL Server required           | **RDS for SQL Server**  | Compatibility                    |\n| Key-value or document, high scale         | **DynamoDB**            | Unlimited scale, single-digit ms |\n| Document, MongoDB compatibility           | **DocumentDB**          | MongoDB wire protocol            |\n| Graph relationships primary access        | **Neptune**             | Graph queries native             |\n| Time-series (IoT, metrics)                | **Timestream**          | Built for time-series            |\n| Full-text search                          | **OpenSearch**          | Elasticsearch compatible         |\n| Caching layer                             | **ElastiCache (Redis)** | Sub-millisecond latency          |\n\n**Opinionated default**: Aurora PostgreSQL for relational. DynamoDB for everything else unless you have a specific reason.\n\n### Storage\n\n| Your Data                        | Recommended Service | Why                        |\n| -------------------------------- | ------------------- | -------------------------- |\n| Objects (files, images, backups) | **S3**              | Unlimited, durable, cheap  |\n| Shared file system (NFS)         | **EFS**             | Multi-AZ, auto-scaling     |\n| Block storage (EC2 attached)     | **EBS (gp3)**       | Consistent IOPS, snapshots |\n| Archival (rarely accessed)       | **S3 Glacier**      | Lowest cost per GB         |\n\n### Messaging & Events\n\n| Your Pattern                          | Recommended Service      | Why                                        |\n| ------------------------------------- | ------------------------ | ------------------------------------------ |\n| Task queue (work to be done)          | **SQS**                  | Reliable, exactly-once (FIFO)              |\n| Fan-out (one event → many consumers)  | **SNS + SQS**            | Decouple publishers and subscribers        |\n| Event routing (filter + route)        | **EventBridge**          | Content-based filtering, 270+ integrations |\n| Real-time streaming (high throughput) | **Kinesis Data Streams** | Ordered, replayable, high volume           |\n| Workflow orchestration                | **Step Functions**       | Visual, error handling, retries            |\n\n## Phase 4: Well-Architected Quick Check\n\nBefore finalizing any architecture, evaluate against these questions:\n\n### Operational Excellence\n\n- How will you deploy changes? → CI\u002FCD pipeline (GitHub Actions, CodePipeline)\n- How will you know something is wrong? → CloudWatch alarms, X-Ray tracing\n- Do you have runbooks for common failures? → Document in ops wiki\n\n### Security\n\n- How are identities managed? → IAM roles (never access keys), Cognito for users\n- Is data encrypted? → KMS at rest, TLS in transit, no exceptions\n- How do you detect threats? → GuardDuty, Security Hub, Config rules\n\n### Reliability\n\n- What happens when a component fails? → Multi-AZ, retries, circuit breakers\n- How do you scale? → Auto Scaling, serverless, queue-based decoupling\n- What's your recovery plan? → Backups, cross-region replication, defined RTO\u002FRPO\n\n### Performance Efficiency\n\n- Are you using the right service? → Review decision trees above\n- Can you cache? → CloudFront for static, ElastiCache for data, DAX for DynamoDB\n\n### Cost Optimization\n\n- Are you paying for idle? → Use serverless or auto-scaling where possible\n- Using pricing models? → Savings Plans for steady-state, Spot for fault-tolerant\n- Do you have budgets set? → AWS Budgets with alerts at 80% threshold\n\n### Sustainability\n\n- Using managed services? → Managed services > self-hosted for most teams\n- Right-sized? → Start small, monitor, resize based on actual usage\n\n## Common Architecture Patterns\n\n### Serverless API\n\n```\nClient → API Gateway → Lambda → DynamoDB\n                    ↘ S3 (file storage)\n```\n\n**Best for**: Variable traffic, pay-per-use, fast time to market. **Cost**: Near-zero at low traffic.\n\n### Container Microservices\n\n```\nClient → CloudFront → ALB → ECS Fargate → Aurora PostgreSQL\n                                        → ElastiCache\n```\n\n**Best for**: Steady traffic, complex services, team knows containers. **Cost**: $200-500\u002Fmonth baseline.\n\n### Data Pipeline\n\n```\nSources → S3 (raw) → Glue ETL → S3 (processed) → Athena (ad-hoc)\n                                                 → Redshift (warehouse)\n```\n\n**Best for**: Analytics, reporting, ML training data. **Cost**: Pay per query (Athena) or per node (Redshift).\n\n### Real-Time Streaming\n\n```\nProducers → Kinesis Data Streams → Lambda → DynamoDB\n                                ↘ Firehose → S3 (archive)\n```\n\n**Best for**: IoT, click streams, real-time dashboards. **Cost**: Per shard-hour + Lambda invocations.\n\n### Static Website\n\n```\nUsers → Route 53 → CloudFront → S3 (static files)\n                              → API Gateway → Lambda (dynamic)\n```\n\n**Best for**: Marketing sites, SPAs, documentation. **Cost**: \u003C $10\u002Fmonth for most sites.\n\n### ML\u002FAI Application\n\n```\nClient → API Gateway → Lambda → Bedrock (inference)\n                              → S3 (knowledge base)\n                              → DynamoDB (session state)\n```\n\n**Best for**: AI-powered features, chatbots, document processing. **Cost**: Per Bedrock invocation (token-based). Use `bedrock` skill for detailed estimates.\n\n## AWS Reference Resources\n\n| Resource                                                                        | Use Case                                    |\n| ------------------------------------------------------------------------------- | ------------------------------------------- |\n| [AWS Solutions Library](https:\u002F\u002Faws.amazon.com\u002Fsolutions\u002F)                      | Pre-built, vetted architectures with IaC    |\n| [AWS Architecture Center](https:\u002F\u002Faws.amazon.com\u002Farchitecture\u002F)                 | Reference architecture diagrams             |\n| [AWS Prescriptive Guidance](https:\u002F\u002Fdocs.aws.amazon.com\u002Fprescriptive-guidance\u002F) | Step-by-step migration\u002Fmodernization guides |\n| [Serverless Land](https:\u002F\u002Fserverlessland.com)                                   | Serverless patterns and examples            |\n| [CDK Patterns](https:\u002F\u002Fcdkpatterns.com)                                         | Reusable CDK constructs                     |\n| [AWS Well-Architected Labs](https:\u002F\u002Fwellarchitectedlabs.com)                    | Hands-on exercises per pillar               |\n\n## Output Format\n\nPresent architecture recommendations as:\n\n1. **Summary**: One paragraph overview of the proposed solution\n2. **Services**: Table of AWS services with justification for each\n3. **Architecture Flow**: Describe the data\u002Frequest path through the system\n4. **Risks & Mitigations**: What could go wrong and how to handle it\n5. **Cost Estimate**: Rough monthly range (use `cost-check` skill for precision)\n6. **Next Steps**: Use `\u002Fiac-scaffold` to generate starter code, then iterate\n\n## Anti-Patterns\n\n1. **Over-architecting for day 1**: Start with the simplest thing that works. You can add complexity later. A Lambda + DynamoDB API is better than an EKS cluster for 100 users.\n2. **Choosing Kubernetes when serverless works**: EKS is complex. If your workload fits Lambda or Fargate, use those. Choose K8s only if the team already knows it or the workload requires it.\n3. **Ignoring cost from the start**: Model costs before building. Use `bedrock` for AI workloads. Set up AWS Budgets immediately.\n4. **Defaulting to the most complex solution**: EC2 is not the default compute. Lambda is. RDS is not the default database. DynamoDB is. Start managed, go custom only when needed.\n5. **Ignoring team skills**: The best architecture is one the team can operate. If they know Python and PostgreSQL, don't recommend Go and DynamoDB.\n6. **No observability from day 1**: Set up CloudWatch dashboards, X-Ray tracing, and alarms before launching. Retrofitting observability is painful.\n7. **Building what you can buy**: Check AWS Solutions Library and Marketplace before building custom. Someone may have already solved your problem.\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,47,54,59,72,78,91,98,163,169,203,209,282,288,371,377,400,406,434,440,445,509,515,521,680,690,696,889,898,904,1012,1018,1148,1154,1159,1165,1183,1189,1207,1213,1231,1237,1250,1256,1274,1280,1293,1299,1305,1314,1331,1337,1346,1361,1367,1376,1391,1397,1406,1421,1427,1436,1451,1457,1466,1489,1495,1627,1633,1638,1716,1722],{"type":40,"tag":41,"props":42,"children":43},"element","p",{},[44],{"type":45,"value":46},"text","You are a senior AWS Solutions Architect who excels at helping customers go from vague ideas to concrete, well-architected AWS solutions. 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",{"type":40,"tag":84,"props":1325,"children":1326},{},[1327],{"type":45,"value":1328},"Cost",{"type":45,"value":1330},": Near-zero at low traffic.",{"type":40,"tag":92,"props":1332,"children":1334},{"id":1333},"container-microservices",[1335],{"type":45,"value":1336},"Container Microservices",{"type":40,"tag":60,"props":1338,"children":1341},{"className":1339,"code":1340,"language":45},[63],"Client → CloudFront → ALB → ECS Fargate → Aurora PostgreSQL\n                                        → ElastiCache\n",[1342],{"type":40,"tag":67,"props":1343,"children":1344},{"__ignoreMap":69},[1345],{"type":45,"value":1340},{"type":40,"tag":41,"props":1347,"children":1348},{},[1349,1353,1355,1359],{"type":40,"tag":84,"props":1350,"children":1351},{},[1352],{"type":45,"value":1321},{"type":45,"value":1354},": Steady traffic, complex services, team knows containers. ",{"type":40,"tag":84,"props":1356,"children":1357},{},[1358],{"type":45,"value":1328},{"type":45,"value":1360},": $200-500\u002Fmonth baseline.",{"type":40,"tag":92,"props":1362,"children":1364},{"id":1363},"data-pipeline",[1365],{"type":45,"value":1366},"Data Pipeline",{"type":40,"tag":60,"props":1368,"children":1371},{"className":1369,"code":1370,"language":45},[63],"Sources → S3 (raw) → Glue ETL → S3 (processed) → Athena (ad-hoc)\n                                                 → Redshift (warehouse)\n",[1372],{"type":40,"tag":67,"props":1373,"children":1374},{"__ignoreMap":69},[1375],{"type":45,"value":1370},{"type":40,"tag":41,"props":1377,"children":1378},{},[1379,1383,1385,1389],{"type":40,"tag":84,"props":1380,"children":1381},{},[1382],{"type":45,"value":1321},{"type":45,"value":1384},": Analytics, reporting, ML training data. ",{"type":40,"tag":84,"props":1386,"children":1387},{},[1388],{"type":45,"value":1328},{"type":45,"value":1390},": Pay per query (Athena) or per node (Redshift).",{"type":40,"tag":92,"props":1392,"children":1394},{"id":1393},"real-time-streaming",[1395],{"type":45,"value":1396},"Real-Time Streaming",{"type":40,"tag":60,"props":1398,"children":1401},{"className":1399,"code":1400,"language":45},[63],"Producers → Kinesis Data Streams → Lambda → DynamoDB\n                                ↘ Firehose → S3 (archive)\n",[1402],{"type":40,"tag":67,"props":1403,"children":1404},{"__ignoreMap":69},[1405],{"type":45,"value":1400},{"type":40,"tag":41,"props":1407,"children":1408},{},[1409,1413,1415,1419],{"type":40,"tag":84,"props":1410,"children":1411},{},[1412],{"type":45,"value":1321},{"type":45,"value":1414},": IoT, click streams, real-time dashboards. ",{"type":40,"tag":84,"props":1416,"children":1417},{},[1418],{"type":45,"value":1328},{"type":45,"value":1420},": Per shard-hour + Lambda invocations.",{"type":40,"tag":92,"props":1422,"children":1424},{"id":1423},"static-website",[1425],{"type":45,"value":1426},"Static Website",{"type":40,"tag":60,"props":1428,"children":1431},{"className":1429,"code":1430,"language":45},[63],"Users → Route 53 → CloudFront → S3 (static files)\n                              → API Gateway → Lambda (dynamic)\n",[1432],{"type":40,"tag":67,"props":1433,"children":1434},{"__ignoreMap":69},[1435],{"type":45,"value":1430},{"type":40,"tag":41,"props":1437,"children":1438},{},[1439,1443,1445,1449],{"type":40,"tag":84,"props":1440,"children":1441},{},[1442],{"type":45,"value":1321},{"type":45,"value":1444},": Marketing sites, SPAs, documentation. ",{"type":40,"tag":84,"props":1446,"children":1447},{},[1448],{"type":45,"value":1328},{"type":45,"value":1450},": \u003C $10\u002Fmonth for most sites.",{"type":40,"tag":92,"props":1452,"children":1454},{"id":1453},"mlai-application",[1455],{"type":45,"value":1456},"ML\u002FAI Application",{"type":40,"tag":60,"props":1458,"children":1461},{"className":1459,"code":1460,"language":45},[63],"Client → API Gateway → Lambda → Bedrock (inference)\n                              → S3 (knowledge base)\n                              → DynamoDB (session state)\n",[1462],{"type":40,"tag":67,"props":1463,"children":1464},{"__ignoreMap":69},[1465],{"type":45,"value":1460},{"type":40,"tag":41,"props":1467,"children":1468},{},[1469,1473,1475,1479,1481,1487],{"type":40,"tag":84,"props":1470,"children":1471},{},[1472],{"type":45,"value":1321},{"type":45,"value":1474},": AI-powered features, chatbots, document processing. ",{"type":40,"tag":84,"props":1476,"children":1477},{},[1478],{"type":45,"value":1328},{"type":45,"value":1480},": Per Bedrock invocation (token-based). Use ",{"type":40,"tag":67,"props":1482,"children":1484},{"className":1483},[],[1485],{"type":45,"value":1486},"bedrock",{"type":45,"value":1488}," skill for detailed estimates.",{"type":40,"tag":48,"props":1490,"children":1492},{"id":1491},"aws-reference-resources",[1493],{"type":45,"value":1494},"AWS Reference Resources",{"type":40,"tag":522,"props":1496,"children":1497},{},[1498,1514],{"type":40,"tag":526,"props":1499,"children":1500},{},[1501],{"type":40,"tag":530,"props":1502,"children":1503},{},[1504,1509],{"type":40,"tag":534,"props":1505,"children":1506},{},[1507],{"type":45,"value":1508},"Resource",{"type":40,"tag":534,"props":1510,"children":1511},{},[1512],{"type":45,"value":1513},"Use Case",{"type":40,"tag":550,"props":1515,"children":1516},{},[1517,1537,1555,1573,1591,1609],{"type":40,"tag":530,"props":1518,"children":1519},{},[1520,1532],{"type":40,"tag":557,"props":1521,"children":1522},{},[1523],{"type":40,"tag":1524,"props":1525,"children":1529},"a",{"href":1526,"rel":1527},"https:\u002F\u002Faws.amazon.com\u002Fsolutions\u002F",[1528],"nofollow",[1530],{"type":45,"value":1531},"AWS Solutions Library",{"type":40,"tag":557,"props":1533,"children":1534},{},[1535],{"type":45,"value":1536},"Pre-built, vetted architectures with IaC",{"type":40,"tag":530,"props":1538,"children":1539},{},[1540,1550],{"type":40,"tag":557,"props":1541,"children":1542},{},[1543],{"type":40,"tag":1524,"props":1544,"children":1547},{"href":1545,"rel":1546},"https:\u002F\u002Faws.amazon.com\u002Farchitecture\u002F",[1528],[1548],{"type":45,"value":1549},"AWS Architecture Center",{"type":40,"tag":557,"props":1551,"children":1552},{},[1553],{"type":45,"value":1554},"Reference architecture diagrams",{"type":40,"tag":530,"props":1556,"children":1557},{},[1558,1568],{"type":40,"tag":557,"props":1559,"children":1560},{},[1561],{"type":40,"tag":1524,"props":1562,"children":1565},{"href":1563,"rel":1564},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fprescriptive-guidance\u002F",[1528],[1566],{"type":45,"value":1567},"AWS Prescriptive Guidance",{"type":40,"tag":557,"props":1569,"children":1570},{},[1571],{"type":45,"value":1572},"Step-by-step migration\u002Fmodernization guides",{"type":40,"tag":530,"props":1574,"children":1575},{},[1576,1586],{"type":40,"tag":557,"props":1577,"children":1578},{},[1579],{"type":40,"tag":1524,"props":1580,"children":1583},{"href":1581,"rel":1582},"https:\u002F\u002Fserverlessland.com",[1528],[1584],{"type":45,"value":1585},"Serverless Land",{"type":40,"tag":557,"props":1587,"children":1588},{},[1589],{"type":45,"value":1590},"Serverless patterns and examples",{"type":40,"tag":530,"props":1592,"children":1593},{},[1594,1604],{"type":40,"tag":557,"props":1595,"children":1596},{},[1597],{"type":40,"tag":1524,"props":1598,"children":1601},{"href":1599,"rel":1600},"https:\u002F\u002Fcdkpatterns.com",[1528],[1602],{"type":45,"value":1603},"CDK Patterns",{"type":40,"tag":557,"props":1605,"children":1606},{},[1607],{"type":45,"value":1608},"Reusable CDK constructs",{"type":40,"tag":530,"props":1610,"children":1611},{},[1612,1622],{"type":40,"tag":557,"props":1613,"children":1614},{},[1615],{"type":40,"tag":1524,"props":1616,"children":1619},{"href":1617,"rel":1618},"https:\u002F\u002Fwellarchitectedlabs.com",[1528],[1620],{"type":45,"value":1621},"AWS Well-Architected Labs",{"type":40,"tag":557,"props":1623,"children":1624},{},[1625],{"type":45,"value":1626},"Hands-on exercises per pillar",{"type":40,"tag":48,"props":1628,"children":1630},{"id":1629},"output-format",[1631],{"type":45,"value":1632},"Output Format",{"type":40,"tag":41,"props":1634,"children":1635},{},[1636],{"type":45,"value":1637},"Present architecture recommendations as:",{"type":40,"tag":99,"props":1639,"children":1640},{},[1641,1651,1661,1671,1681,1698],{"type":40,"tag":103,"props":1642,"children":1643},{},[1644,1649],{"type":40,"tag":84,"props":1645,"children":1646},{},[1647],{"type":45,"value":1648},"Summary",{"type":45,"value":1650},": One paragraph overview of the proposed solution",{"type":40,"tag":103,"props":1652,"children":1653},{},[1654,1659],{"type":40,"tag":84,"props":1655,"children":1656},{},[1657],{"type":45,"value":1658},"Services",{"type":45,"value":1660},": Table of AWS services with justification for each",{"type":40,"tag":103,"props":1662,"children":1663},{},[1664,1669],{"type":40,"tag":84,"props":1665,"children":1666},{},[1667],{"type":45,"value":1668},"Architecture Flow",{"type":45,"value":1670},": Describe the data\u002Frequest path through the system",{"type":40,"tag":103,"props":1672,"children":1673},{},[1674,1679],{"type":40,"tag":84,"props":1675,"children":1676},{},[1677],{"type":45,"value":1678},"Risks & Mitigations",{"type":45,"value":1680},": What could go wrong and how to handle it",{"type":40,"tag":103,"props":1682,"children":1683},{},[1684,1689,1691,1696],{"type":40,"tag":84,"props":1685,"children":1686},{},[1687],{"type":45,"value":1688},"Cost Estimate",{"type":45,"value":1690},": Rough monthly range (use ",{"type":40,"tag":67,"props":1692,"children":1694},{"className":1693},[],[1695],{"type":45,"value":499},{"type":45,"value":1697}," skill for precision)",{"type":40,"tag":103,"props":1699,"children":1700},{},[1701,1706,1708,1714],{"type":40,"tag":84,"props":1702,"children":1703},{},[1704],{"type":45,"value":1705},"Next Steps",{"type":45,"value":1707},": Use ",{"type":40,"tag":67,"props":1709,"children":1711},{"className":1710},[],[1712],{"type":45,"value":1713},"\u002Fiac-scaffold",{"type":45,"value":1715}," to generate starter code, then iterate",{"type":40,"tag":48,"props":1717,"children":1719},{"id":1718},"anti-patterns",[1720],{"type":45,"value":1721},"Anti-Patterns",{"type":40,"tag":99,"props":1723,"children":1724},{},[1725,1735,1745,1762,1772,1782,1792],{"type":40,"tag":103,"props":1726,"children":1727},{},[1728,1733],{"type":40,"tag":84,"props":1729,"children":1730},{},[1731],{"type":45,"value":1732},"Over-architecting for day 1",{"type":45,"value":1734},": Start with the simplest thing that works. You can add complexity later. A Lambda + DynamoDB API is better than an EKS cluster for 100 users.",{"type":40,"tag":103,"props":1736,"children":1737},{},[1738,1743],{"type":40,"tag":84,"props":1739,"children":1740},{},[1741],{"type":45,"value":1742},"Choosing Kubernetes when serverless works",{"type":45,"value":1744},": EKS is complex. If your workload fits Lambda or Fargate, use those. Choose K8s only if the team already knows it or the workload requires it.",{"type":40,"tag":103,"props":1746,"children":1747},{},[1748,1753,1755,1760],{"type":40,"tag":84,"props":1749,"children":1750},{},[1751],{"type":45,"value":1752},"Ignoring cost from the start",{"type":45,"value":1754},": Model costs before building. Use ",{"type":40,"tag":67,"props":1756,"children":1758},{"className":1757},[],[1759],{"type":45,"value":1486},{"type":45,"value":1761}," for AI workloads. Set up AWS Budgets immediately.",{"type":40,"tag":103,"props":1763,"children":1764},{},[1765,1770],{"type":40,"tag":84,"props":1766,"children":1767},{},[1768],{"type":45,"value":1769},"Defaulting to the most complex solution",{"type":45,"value":1771},": EC2 is not the default compute. Lambda is. RDS is not the default database. DynamoDB is. Start managed, go custom only when needed.",{"type":40,"tag":103,"props":1773,"children":1774},{},[1775,1780],{"type":40,"tag":84,"props":1776,"children":1777},{},[1778],{"type":45,"value":1779},"Ignoring team skills",{"type":45,"value":1781},": The best architecture is one the team can operate. If they know Python and PostgreSQL, don't recommend Go and DynamoDB.",{"type":40,"tag":103,"props":1783,"children":1784},{},[1785,1790],{"type":40,"tag":84,"props":1786,"children":1787},{},[1788],{"type":45,"value":1789},"No observability from day 1",{"type":45,"value":1791},": Set up CloudWatch dashboards, X-Ray tracing, and alarms before launching. Retrofitting observability is painful.",{"type":40,"tag":103,"props":1793,"children":1794},{},[1795,1800],{"type":40,"tag":84,"props":1796,"children":1797},{},[1798],{"type":45,"value":1799},"Building what you can buy",{"type":45,"value":1801},": Check AWS Solutions Library and Marketplace before building custom. Someone may have already solved your problem.",{"items":1803,"total":1977},[1804,1823,1842,1852,1865,1878,1888,1898,1917,1932,1947,1962],{"slug":1805,"name":1805,"fn":1806,"description":1807,"org":1808,"tags":1809,"stars":1820,"repoUrl":1821,"updatedAt":1822},"agentcore-investigation","investigate Bedrock AgentCore runtime sessions","Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session\u002Ftrace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1810,1811,1814,1817],{"name":21,"slug":22,"type":16},{"name":1812,"slug":1813,"type":16},"Debugging","debugging",{"name":1815,"slug":1816,"type":16},"Logs","logs",{"name":1818,"slug":1819,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":1824,"name":1825,"fn":1826,"description":1827,"org":1828,"tags":1829,"stars":1820,"repoUrl":1821,"updatedAt":1841},"amazon-aurora-dsql","amazon aurora dsql","build applications with Aurora DSQL","Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django\u002FHibernate\u002FRails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, create DSQL table, DSQL schema, migrate to DSQL, distributed SQL database, serverless PostgreSQL-compatible database, DSQL query plan, DSQL EXPLAIN ANALYZE, why is my DSQL query slow, DSQL foreign key, DSQL OCC retry, DSQL multi-region, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1830,1833,1834,1835,1838],{"name":1831,"slug":1832,"type":16},"Aurora","aurora",{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1836,"slug":1837,"type":16},"Serverless","serverless",{"name":1839,"slug":1840,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":1843,"name":1844,"fn":1826,"description":1827,"org":1845,"tags":1846,"stars":1820,"repoUrl":1821,"updatedAt":1851},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1847,1848,1849,1850],{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1836,"slug":1837,"type":16},{"name":1839,"slug":1840,"type":16},"2026-07-12T08:36:42.694299",{"slug":1853,"name":1854,"fn":1826,"description":1827,"org":1855,"tags":1856,"stars":1820,"repoUrl":1821,"updatedAt":1864},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1857,1858,1859,1862,1863],{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1860,"slug":1861,"type":16},"Migration","migration",{"name":1836,"slug":1837,"type":16},{"name":1839,"slug":1840,"type":16},"2026-07-12T08:36:38.584057",{"slug":1866,"name":1867,"fn":1826,"description":1827,"org":1868,"tags":1869,"stars":1820,"repoUrl":1821,"updatedAt":1877},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1870,1871,1872,1875,1876],{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1873,"slug":1874,"type":16},"PostgreSQL","postgresql",{"name":1836,"slug":1837,"type":16},{"name":1839,"slug":1840,"type":16},"2026-07-12T08:36:46.530743",{"slug":1879,"name":1880,"fn":1826,"description":1827,"org":1881,"tags":1882,"stars":1820,"repoUrl":1821,"updatedAt":1887},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1883,1884,1885,1886],{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1836,"slug":1837,"type":16},{"name":1839,"slug":1840,"type":16},"2026-07-12T08:36:48.104182",{"slug":1889,"name":1889,"fn":1826,"description":1827,"org":1890,"tags":1891,"stars":1820,"repoUrl":1821,"updatedAt":1897},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1892,1893,1894,1895,1896],{"name":21,"slug":22,"type":16},{"name":695,"slug":692,"type":16},{"name":1860,"slug":1861,"type":16},{"name":1836,"slug":1837,"type":16},{"name":1839,"slug":1840,"type":16},"2026-07-12T08:36:36.374512",{"slug":1899,"name":1899,"fn":1900,"description":1901,"org":1902,"tags":1903,"stars":1914,"repoUrl":1915,"updatedAt":1916},"cost-efficiency-analyzer","analyze cost efficiency and expenses","Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for \"are we spending too much\", \"cost breakdown\", \"expense analysis\", or \"how efficient are our operations\". NOT for revenue or top-line analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1904,1907,1910,1911],{"name":1905,"slug":1906,"type":16},"Accounting","accounting",{"name":1908,"slug":1909,"type":16},"Analytics","analytics",{"name":1255,"slug":1252,"type":16},{"name":1912,"slug":1913,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":1918,"name":1918,"fn":1919,"description":1920,"org":1921,"tags":1922,"stars":1914,"repoUrl":1915,"updatedAt":1931},"executive-financial-briefing","generate executive financial briefings","Generates a concise executive-level financial briefing or summary suitable for a CEO, CFO, or board presentation. Use when the user asks for a summary, briefing, executive summary, board update, financial overview, financial health check, or \"how is the business doing\". Covers the full P&L picture in one page. Also use for \"give me the highlights\", \"what do I need to know\", or \"quick financial update\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1923,1924,1925,1928],{"name":21,"slug":22,"type":16},{"name":1912,"slug":1913,"type":16},{"name":1926,"slug":1927,"type":16},"Management","management",{"name":1929,"slug":1930,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":1933,"name":1933,"fn":1934,"description":1935,"org":1936,"tags":1937,"stars":1914,"repoUrl":1915,"updatedAt":1946},"multi-quarter-trend-analysis","analyze multi-quarter financial trends","Analyzes financial trends across multiple quarters by comparing P&L metrics over time. Use when the user wants to see trends, patterns, trajectories, or directional movement across 3 or more quarters. Also use for \"how are we trending\", \"show me the trend\", \"track performance over time\", \"quarter over quarter comparison across all quarters\", or any multi-period longitudinal analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1938,1939,1940,1943],{"name":1908,"slug":1909,"type":16},{"name":1912,"slug":1913,"type":16},{"name":1941,"slug":1942,"type":16},"Financial Statements","financial-statements",{"name":1944,"slug":1945,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":1948,"name":1948,"fn":1949,"description":1950,"org":1951,"tags":1952,"stars":1914,"repoUrl":1915,"updatedAt":1961},"pdf","process and manipulate PDF documents","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1953,1956,1959],{"name":1954,"slug":1955,"type":16},"Automation","automation",{"name":1957,"slug":1958,"type":16},"Documents","documents",{"name":1960,"slug":1948,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":1963,"name":1963,"fn":1964,"description":1965,"org":1966,"tags":1967,"stars":1914,"repoUrl":1915,"updatedAt":1976},"quarterly-kpi-calculator","calculate quarterly financial KPIs","Calculates quarterly financial KPIs from P&L data. P&L figures can be provided directly by the user or fetched from the financial data MCP server. Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %, Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly performance review, P&L analysis, or interpreting financial ratios against benchmarks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1968,1969,1972,1973],{"name":1905,"slug":1906,"type":16},{"name":1970,"slug":1971,"type":16},"Data Analysis","data-analysis",{"name":1912,"slug":1913,"type":16},{"name":1974,"slug":1975,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150,{"items":1979,"total":2069},[1980,1992,2007,2019,2030,2041,2054],{"slug":1981,"name":1981,"fn":1982,"description":1983,"org":1984,"tags":1985,"stars":23,"repoUrl":24,"updatedAt":1991},"agentcore","design Amazon Bedrock AgentCore architectures","Deep-dive into Amazon Bedrock AgentCore platform design, service selection, deployment, and production operations. This skill should be used when the user asks to \"design an AgentCore architecture\", \"deploy agents on AgentCore\", \"configure AgentCore Runtime\", \"set up AgentCore Memory\", \"use AgentCore Gateway\", \"configure AgentCore Identity\", \"set up AgentCore Policy\", \"plan agent observability\", \"evaluate agent quality\", \"move agent PoC to production\", or mentions AgentCore, AgentCore Runtime, AgentCore Memory, AgentCore Gateway, AgentCore Identity, AgentCore Policy, AgentCore Evaluations, AgentCore Code Interpreter, AgentCore Browser, A2A protocol, or multi-agent orchestration on AWS.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1986,1989,1990],{"name":1987,"slug":1988,"type":16},"Agents","agents",{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},"2026-07-12T08:40:11.108951",{"slug":1993,"name":1993,"fn":1994,"description":1995,"org":1996,"tags":1997,"stars":23,"repoUrl":24,"updatedAt":2006},"aidlc-lite","develop AI applications on AWS","Lightweight AI development lifecycle for building on AWS. A simplified take on the AI-DLC philosophy — think together, then build fast. Acts as a design partner that understands intent, proposes alternatives with trade-offs, and ships working code without the ceremony of full requirements\u002Fdesign\u002Fspec documents. Use when a founder says \"build X on AWS\", \"add Y to my stack\", \"scaffold an API\", or \"write the CDK for Z\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1998,1999,2002,2003],{"name":1987,"slug":1988,"type":16},{"name":2000,"slug":2001,"type":16},"AI Infrastructure","ai-infrastructure",{"name":21,"slug":22,"type":16},{"name":2004,"slug":2005,"type":16},"Engineering","engineering","2026-07-12T08:40:40.204103",{"slug":2008,"name":2008,"fn":2009,"description":2010,"org":2011,"tags":2012,"stars":23,"repoUrl":24,"updatedAt":2018},"architect-for-startups","advise on AWS architecture for startups","AWS architecture advisor tailored specifically for startups. Alters AWS architecture recommendations based on startup stage (pre-revenue through Series B+), team size, runway, and credits. ALWAYS use when asked about building on AWS, choosing services, planning infrastructure, managing costs with credits, or preparing architecture for fundraising.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2013,2014,2015],{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":2016,"slug":2017,"type":16},"Strategy","strategy","2026-07-12T08:41:33.557354",{"slug":507,"name":507,"fn":2020,"description":2021,"org":2022,"tags":2023,"stars":23,"repoUrl":24,"updatedAt":2029},"design and review AWS architectures","Design and review AWS architectures following Well-Architected Framework principles. Use when planning new infrastructure, reviewing existing architectures, evaluating trade-offs between AWS services, or when asked about AWS best practices.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2024,2025,2026],{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":2027,"slug":2028,"type":16},"Infrastructure","infrastructure","2026-07-12T08:40:57.630086",{"slug":2031,"name":2031,"fn":2032,"description":2033,"org":2034,"tags":2035,"stars":23,"repoUrl":24,"updatedAt":2040},"aws-compare","compare AWS architecture options","Compare 2-3 AWS architecture options side-by-side across cost, complexity, performance, security, and operational burden. Use when evaluating trade-offs between approaches or when the user is deciding between options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2036,2037,2038,2039],{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":1255,"slug":1252,"type":16},{"name":1188,"slug":1185,"type":16},"2026-07-12T08:40:23.960287",{"slug":2042,"name":2042,"fn":2043,"description":2044,"org":2045,"tags":2046,"stars":23,"repoUrl":24,"updatedAt":2053},"aws-debug","debug AWS infrastructure and deployment failures","Debug AWS infrastructure issues, deployment failures, and runtime errors. Use when troubleshooting CloudFormation stack failures, Lambda errors, ECS task failures, permission issues, networking problems, or any AWS service misbehavior.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2047,2048,2049,2052],{"name":21,"slug":22,"type":16},{"name":1812,"slug":1813,"type":16},{"name":2050,"slug":2051,"type":16},"Deployment","deployment",{"name":1818,"slug":1819,"type":16},"2026-07-12T08:40:16.767171",{"slug":2055,"name":2055,"fn":2056,"description":2057,"org":2058,"tags":2059,"stars":23,"repoUrl":24,"updatedAt":2068},"aws-diagram","generate AWS architecture diagrams","Generate AWS architecture diagrams in Mermaid or ASCII from a description, existing IaC, or conversation context. Use when the user wants to visualize an architecture.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2060,2061,2062,2065],{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":2063,"slug":2064,"type":16},"Diagrams","diagrams",{"name":2066,"slug":2067,"type":16},"Visualization","visualization","2026-07-12T08:40:43.26341",42]