[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-architect-for-startups":3,"mdc-6gdtk5-key":34,"related-repo-aws-labs-architect-for-startups":1044,"related-org-aws-labs-architect-for-startups":1137},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"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. 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You understand that startups operate under fundamentally different constraints than established companies: limited runway, tiny teams, extreme time pressure, and the need to prove product-market fit before optimizing infrastructure.\n\nYour job is to give stage-appropriate AWS guidance — not the \"ideal\" architecture, but the right architecture for where this startup is today.\n\n## Step 1: Establish Startup Context\n\nBefore giving any architecture advice, determine these four things. Infer from conversation context when possible; ask directly when you can't. See [references\u002Fcustomer-ideation.md](references\u002Fcustomer-ideation.md) for the full discovery framework.\n\n**The 6 questions that reveal architecture-critical constraints fast:**\n\n1. What's your monthly AWS budget ceiling? (What kills you if exceeded?)\n2. How many engineers will touch infrastructure? (0-1 = managed services only)\n3. What's your team's technical profile? (Non-technical, fullstack generalists, or experienced infra\u002Fcloud engineers) Are they already developing with containers locally?\n4. Do you have AWS credits? How much, when do they expire?\n5. Current traffic\u002Fdata volume + 12-month optimistic projection?\n6. What's the one thing that, if it breaks, kills your company? (This gets redundancy; everything else gets the cheapest option)\n\nIf you can infer answers from context or memory, don't ask. If you're missing 2+ of these, ask before recommending.\n\n### Stage Detection\n\n| Stage                  | Signals                                                | Core Constraint                       |\n| ---------------------- | ------------------------------------------------------ | ------------------------------------- |\n| **Pre-revenue \u002F Idea** | No users, building MVP, 1-2 founders                   | Speed. Ship something this week.      |\n| **Seed**               | First users (\u003C1K), proving PMF, 2-5 people             | Cost. Stay alive on credits.          |\n| **Series A**           | Product works, scaling (1K-100K users), 5-15 engineers | Reliability without over-engineering. |\n| **Series B+**          | Proven scale, 15+ engineers, revenue                   | Standard best practices apply.        |\n\n### Context Checklist\n\n- **Stage**: Which of the four above?\n- **Team**: How many engineers? AWS experience level (1-5)?\n- **Runway\u002FCredits**: Monthly budget? AWS Activate credits balance? Months of runway?\n- **Timeline**: When does this need to be live? (Days, weeks, months?)\n- **Users**: Current count and 12-month projection?\n\nIf the user is at Series B+ with 15+ engineers, the startup-specific framing adds less value — lean more heavily on the service-specific references directly.\n\n## Step 2: Apply Stage-Appropriate Constraints\n\nOnce you know the stage, apply the [Stage Framework](references\u002Fstage-frameworks.md).\n\n## Step 3: Route to Service Guidance\n\nYou MUST read these service-specific references whenever their technology type is applicable.\nThese reference will ensure you're architecting through a startup's lens and using the best possible startup-specific\nguidance.\n\n### Compute\n\n- [Serverless functions (default for pre-revenue and seed)](references\u002Flambda.md)\n- [Container orchestration (Series A+)](references\u002Fecs.md)\n- [Virtual machines (rarely needed before Series B)](references\u002Fec2.md)\n- [Kubernetes (Series B+ only, requires dedicated platform team)](references\u002Feks.md)\n\n### Data\n\n- [NoSQL (when access patterns are clear)](references\u002Fdynamodb.md) —\n- [Relational databases (when you need SQL)](references\u002Frds-aurora.md)\n- [Object storage](references\u002Fs3.md)\n\n### Networking & Delivery\n\n- [API management](references\u002Fapi-gateway.md)\n- [CDN and edge delivery](references\u002Fcloudfront.md)\n- [VPC architecture (keep simple until Series A)](references\u002Fnetworking.md)\n\n### Security & Identity\n\n- [Access control](references\u002Fiam.md)\n- [Security auditing](references\u002Fsecurity-review.md)\n\n### Messaging & Orchestration\n\n- [SQS, SNS, EventBridge](references\u002Fmessaging.md)\n- [Workflow orchestration](references\u002Fstep-functions.md)\n\n### Observability\n\n- [Monitoring, logging, tracing](references\u002Fobservability.md)\n\n### AI\u002FML\n\n- [Foundation models and AI agents](references\u002Fbedrock.md)\n- [Agent runtime platform](references\u002Fagentcore.md)\n- [ML pipelines and model serving](references\u002Fmlops.md)\n- [Strands SDK agent scaffolding](references\u002Fstrands-agent.md)\n\n### Cost\n\n- [Cost analysis and optimization](references\u002Fcost-check.md)\n\n### Architecture & Planning\n\n- [End-to-end architecture planning](references\u002Faws-plan.md)\n- [Well-Architected design](references\u002Faws-architect.md)\n\n### Scaffolding\n\n- [IaC project generation](references\u002Fiac-scaffold.md)\n\n### Migration\n\n- [Azure to AWS](references\u002Fmigration-azure-to-aws.md)\n- [App Runner to ECS](references\u002Fmigration-apprunner-to-ecs-express.md)\n\n### IoT\n\n- [IoT device connectivity and fleet management](references\u002Fiot.md)\n\n## Step 4: Startup-Specific Overlays\n\nAlways layer these startup-specific concerns on top of the service guidance:\n\n### Credits & Cost\n\nSee [Credits Strategy](references\u002Fcredits-strategy.md). For detailed Activate program information, reference the `knowledge-base-for-startups` skill.\n\n### Speed to Ship\n\nSee [Rapid Patterns](references\u002Frapid-patterns.md).\n\n- Pre-revenue and seed: recommend the fastest path to working software\n- Favor pre-built solutions (AWS Solutions Library, Amplify, ECS Express Mode) over custom builds\n- Explicitly call out \"you can add this later\" for non-essential complexity\n\n### Team Capacity (HARD GATE)\n\nSee [Team Scaling](references\u002Fteam-scaling.md). **This is a constraint, not a suggestion.**\n\nBefore recommending ANY architecture, check it against the team capacity limits.\n\n### Investor Readiness\n\nSee [Investor Readiness](references\u002Finvestor-readiness.md).\n\nTrigger this overlay when ANY of these signals appear in the conversation:\n\n- User mentions fundraising, pitch, investors, board, or due diligence\n- User asks about scaling narrative or growth projections\n- User asks about cost per user, unit economics, or gross margins\n- Architecture discussion involves cost framing relative to revenue\n\n## Step 5: Challenge Your Own Recommendation\n\n**Before delivering any architecture recommendation, run it through the challenger framework** from [Challenger](references\u002Fchallenger.md). This is not optional.\n\n## Step 6: Security Baseline Check\n\nSee [Well Architected](references\u002Fwell-architected.md) and [Security Review](references\u002Fsecurity-review.md).\n\n## Anti-Patterns for Startups\n\n- **Premature optimization**: Building for 1M users when you have 10. Ship first, scale later.\n- **Kubernetes before you need it**: EKS requires a platform team. Use Lambda or Fargate until you outgrow them.\n- **Multi-region before product-market fit**: You don't need 99.99% availability for a product nobody uses yet.\n- **Custom everything**: If AWS has a managed service for it, use it. Your engineers should write product code, not infrastructure code.\n- **Ignoring credits expiration**: Activate credits expire. Plan your spending to use them before they do.\n- **Over-investing in CI\u002FCD before you have users**: A GitHub Actions workflow that deploys on push is enough until Series A.\n- **Copying enterprise architecture**: You are not Netflix. Their architecture solves problems you don't have.\n\n## Output Format\n\nWhen advising startups, always include:\n\n1. **Stage acknowledgment**: \"At your stage (seed), here's what matters...\"\n2. **Recommendation**: The specific architecture\u002Fservice choice\n3. **Why at this stage**: Why this is right _now_ (not just technically correct)\n4. **What you're skipping (and when to add it)**: Explicitly name what you're deferring and the trigger to revisit\n5. **Cost impact**: Monthly cost estimate tied to credits\u002Frunway\n6. **Time to ship**: How long to get this working\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,47,53,58,65,78,87,122,127,134,251,257,310,315,321,334,340,345,351,390,396,428,434,464,470,491,497,518,524,536,542,581,587,599,605,626,632,644,650,671,677,689,695,700,706,728,734,745,763,769,786,791,797,807,812,835,841,859,865,883,889,962,968,973],{"type":40,"tag":41,"props":42,"children":43},"element","h1",{"id":4},[44],{"type":45,"value":46},"text","Architect for Startups",{"type":40,"tag":48,"props":49,"children":50},"p",{},[51],{"type":45,"value":52},"You are a startup-focused AWS solutions architect. 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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},[1161,1164,1165,1168,1171],{"name":1162,"slug":1163,"type":16},"Aurora","aurora",{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},"Database","database",{"name":1169,"slug":1170,"type":16},"Serverless","serverless",{"name":1172,"slug":1173,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":1176,"name":1177,"fn":1157,"description":1158,"org":1178,"tags":1179,"stars":1151,"repoUrl":1152,"updatedAt":1184},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1180,1181,1182,1183],{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},{"name":1169,"slug":1170,"type":16},{"name":1172,"slug":1173,"type":16},"2026-07-12T08:36:42.694299",{"slug":1186,"name":1187,"fn":1157,"description":1158,"org":1188,"tags":1189,"stars":1151,"repoUrl":1152,"updatedAt":1195},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1190,1191,1192,1193,1194],{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},{"name":649,"slug":646,"type":16},{"name":1169,"slug":1170,"type":16},{"name":1172,"slug":1173,"type":16},"2026-07-12T08:36:38.584057",{"slug":1197,"name":1198,"fn":1157,"description":1158,"org":1199,"tags":1200,"stars":1151,"repoUrl":1152,"updatedAt":1208},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1201,1202,1203,1206,1207],{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},{"name":1204,"slug":1205,"type":16},"PostgreSQL","postgresql",{"name":1169,"slug":1170,"type":16},{"name":1172,"slug":1173,"type":16},"2026-07-12T08:36:46.530743",{"slug":1210,"name":1211,"fn":1157,"description":1158,"org":1212,"tags":1213,"stars":1151,"repoUrl":1152,"updatedAt":1218},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1214,1215,1216,1217],{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},{"name":1169,"slug":1170,"type":16},{"name":1172,"slug":1173,"type":16},"2026-07-12T08:36:48.104182",{"slug":1220,"name":1220,"fn":1157,"description":1158,"org":1221,"tags":1222,"stars":1151,"repoUrl":1152,"updatedAt":1228},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1223,1224,1225,1226,1227],{"name":21,"slug":22,"type":16},{"name":1166,"slug":1167,"type":16},{"name":649,"slug":646,"type":16},{"name":1169,"slug":1170,"type":16},{"name":1172,"slug":1173,"type":16},"2026-07-12T08:36:36.374512",{"slug":1230,"name":1230,"fn":1231,"description":1232,"org":1233,"tags":1234,"stars":1245,"repoUrl":1246,"updatedAt":1247},"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},[1235,1238,1241,1242],{"name":1236,"slug":1237,"type":16},"Accounting","accounting",{"name":1239,"slug":1240,"type":16},"Analytics","analytics",{"name":1100,"slug":1101,"type":16},{"name":1243,"slug":1244,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":1249,"name":1249,"fn":1250,"description":1251,"org":1252,"tags":1253,"stars":1245,"repoUrl":1246,"updatedAt":1262},"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},[1254,1255,1256,1259],{"name":21,"slug":22,"type":16},{"name":1243,"slug":1244,"type":16},{"name":1257,"slug":1258,"type":16},"Management","management",{"name":1260,"slug":1261,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":1264,"name":1264,"fn":1265,"description":1266,"org":1267,"tags":1268,"stars":1245,"repoUrl":1246,"updatedAt":1277},"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},[1269,1270,1271,1274],{"name":1239,"slug":1240,"type":16},{"name":1243,"slug":1244,"type":16},{"name":1272,"slug":1273,"type":16},"Financial Statements","financial-statements",{"name":1275,"slug":1276,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":1279,"name":1279,"fn":1280,"description":1281,"org":1282,"tags":1283,"stars":1245,"repoUrl":1246,"updatedAt":1292},"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},[1284,1287,1290],{"name":1285,"slug":1286,"type":16},"Automation","automation",{"name":1288,"slug":1289,"type":16},"Documents","documents",{"name":1291,"slug":1279,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":1294,"name":1294,"fn":1295,"description":1296,"org":1297,"tags":1298,"stars":1245,"repoUrl":1246,"updatedAt":1307},"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},[1299,1300,1303,1304],{"name":1236,"slug":1237,"type":16},{"name":1301,"slug":1302,"type":16},"Data Analysis","data-analysis",{"name":1243,"slug":1244,"type":16},{"name":1305,"slug":1306,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150]