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Use when the user wants to visualize an architecture.\nargument-hint: [description or \"from-iac\"]\n---\n\nYou are generating an AWS architecture diagram. Produce clear, readable diagrams that show the request\u002Fdata flow through the system.\n\n## Process\n\n1. Determine the source:\n   - If `$ARGUMENTS` contains \"from-iac\": scan the repo for IaC files (CDK, Terraform, CloudFormation, SAM) and reverse-engineer the architecture\n   - Otherwise: use the description from `$ARGUMENTS` or conversation context\n2. Generate a Mermaid diagram (primary) and an ASCII fallback\n3. Include a legend for any non-obvious notation\n\n## Mermaid Diagram Style\n\nUse `graph LR` (left-to-right) for request flows, `graph TD` (top-down) for hierarchical architectures.\n\n### Conventions\n\n- **Users\u002FClients**: Stadium shape `([User])`\n- **AWS Services**: Rectangle `[Service Name]`\n- **Databases**: Cylinder `[(Database)]`\n- **Queues**: Parallelogram `[\u002FQueue\u002F]`\n- **External Services**: Double-bordered `[[External API]]`\n- **Subgraphs**: Group by VPC, subnet, or logical boundary\n- **Arrows**: Label with protocol\u002Faction (e.g., `-->|HTTPS|`, `-->|async|`)\n\n### Example\n\n```mermaid\ngraph LR\n    User([User]) -->|HTTPS| CF[CloudFront]\n    CF -->|HTTPS| ALB[ALB]\n\n    subgraph VPC\n        subgraph Public Subnet\n            ALB\n        end\n        subgraph Private Subnet\n            ECS[ECS Fargate]\n            ECS -->|TCP 5432| RDS[(Aurora PostgreSQL)]\n            ECS -->|TCP 6379| Redis[(ElastiCache Redis)]\n        end\n    end\n\n    ECS -->|HTTPS| S3[S3 Bucket]\n    ECS -->|SQS| Queue[\u002FProcessing Queue\u002F]\n    Queue --> Lambda[Lambda Processor]\n    Lambda --> S3\n```\n\n## ASCII Fallback\n\nFor environments that don't render Mermaid:\n\n```\n┌──────┐     ┌────────────┐     ┌─────┐\n│ User │────>│ CloudFront │────>│ ALB │\n└──────┘     └────────────┘     └──┬──┘\n                                   │\n                    ┌──────────────┴──────────────┐\n                    │         VPC                  │\n                    │  ┌─────────────┐             │\n                    │  │ ECS Fargate │──> Aurora   │\n                    │  │             │──> Redis    │\n                    │  └──────┬──────┘             │\n                    └─────────┼────────────────────┘\n                              │\n                         ┌────┴────┐\n                         │   SQS   │──> Lambda ──> S3\n                         └─────────┘\n```\n\n## From IaC Reverse Engineering\n\nWhen `from-iac` is specified:\n\n1. Glob for `*.tf`, `*.ts` (CDK), `template.yaml` (SAM), `*.template.json` (CFN)\n2. Extract resources, their relationships, and networking config\n3. Map to diagram nodes and edges\n4. Highlight any security concerns (public subnets, open SGs) with a warning marker\n\n## Output\n\nAlways provide:\n\n1. **Mermaid diagram** (in a ```mermaid code block)\n2. **ASCII fallback** (in a ``` code block)\n3. **Flow description** (1-2 sentences explaining the request\u002Fdata path)\n4. **Notes** (any assumptions made, security observations)\n",{"data":38,"body":41},{"name":4,"description":6,"argument-hint":39},[40],"description or \"from-iac\"",{"type":42,"children":43},"root",[44,52,59,109,115,136,143,263,269,450,456,461,471,477,490,544,550,555,598],{"type":45,"tag":46,"props":47,"children":48},"element","p",{},[49],{"type":50,"value":51},"text","You are generating an AWS architecture diagram. 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└─────────┘\n",[467],{"type":45,"tag":79,"props":468,"children":469},{"__ignoreMap":275},[470],{"type":50,"value":465},{"type":45,"tag":53,"props":472,"children":474},{"id":473},"from-iac-reverse-engineering",[475],{"type":50,"value":476},"From IaC Reverse Engineering",{"type":45,"tag":46,"props":478,"children":479},{},[480,482,488],{"type":50,"value":481},"When ",{"type":45,"tag":79,"props":483,"children":485},{"className":484},[],[486],{"type":50,"value":487},"from-iac",{"type":50,"value":489}," is specified:",{"type":45,"tag":60,"props":491,"children":492},{},[493,529,534,539],{"type":45,"tag":64,"props":494,"children":495},{},[496,498,504,505,511,513,519,521,527],{"type":50,"value":497},"Glob for ",{"type":45,"tag":79,"props":499,"children":501},{"className":500},[],[502],{"type":50,"value":503},"*.tf",{"type":50,"value":254},{"type":45,"tag":79,"props":506,"children":508},{"className":507},[],[509],{"type":50,"value":510},"*.ts",{"type":50,"value":512}," (CDK), 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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},[639,640,641],{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":642,"slug":643,"type":16},"Strategy","strategy","2026-07-12T08:41:33.557354",{"slug":646,"name":646,"fn":647,"description":648,"org":649,"tags":650,"stars":26,"repoUrl":27,"updatedAt":656},"aws-architect","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},[651,652,653],{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":654,"slug":655,"type":16},"Infrastructure","infrastructure","2026-07-12T08:40:57.630086",{"slug":658,"name":658,"fn":659,"description":660,"org":661,"tags":662,"stars":26,"repoUrl":27,"updatedAt":671},"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},[663,664,665,668],{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":666,"slug":667,"type":16},"Cost Optimization","cost-optimization",{"name":669,"slug":670,"type":16},"Security","security","2026-07-12T08:40:23.960287",{"slug":673,"name":673,"fn":674,"description":675,"org":676,"tags":677,"stars":26,"repoUrl":27,"updatedAt":688},"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},[678,679,682,685],{"name":24,"slug":25,"type":16},{"name":680,"slug":681,"type":16},"Debugging","debugging",{"name":683,"slug":684,"type":16},"Deployment","deployment",{"name":686,"slug":687,"type":16},"Observability","observability","2026-07-12T08:40:16.767171",{"slug":4,"name":4,"fn":5,"description":6,"org":690,"tags":691,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[692,693,694,695],{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},42,{"items":698,"total":870},[699,714,735,745,758,771,781,791,810,825,840,855],{"slug":700,"name":700,"fn":701,"description":702,"org":703,"tags":704,"stars":711,"repoUrl":712,"updatedAt":713},"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},[705,706,707,710],{"name":24,"slug":25,"type":16},{"name":680,"slug":681,"type":16},{"name":708,"slug":709,"type":16},"Logs","logs",{"name":686,"slug":687,"type":16},9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":715,"name":716,"fn":717,"description":718,"org":719,"tags":720,"stars":711,"repoUrl":712,"updatedAt":734},"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},[721,724,725,728,731],{"name":722,"slug":723,"type":16},"Aurora","aurora",{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},"Database","database",{"name":729,"slug":730,"type":16},"Serverless","serverless",{"name":732,"slug":733,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":736,"name":737,"fn":717,"description":718,"org":738,"tags":739,"stars":711,"repoUrl":712,"updatedAt":744},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[740,741,742,743],{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},{"name":729,"slug":730,"type":16},{"name":732,"slug":733,"type":16},"2026-07-12T08:36:42.694299",{"slug":746,"name":747,"fn":717,"description":718,"org":748,"tags":749,"stars":711,"repoUrl":712,"updatedAt":757},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[750,751,752,755,756],{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},{"name":753,"slug":754,"type":16},"Migration","migration",{"name":729,"slug":730,"type":16},{"name":732,"slug":733,"type":16},"2026-07-12T08:36:38.584057",{"slug":759,"name":760,"fn":717,"description":718,"org":761,"tags":762,"stars":711,"repoUrl":712,"updatedAt":770},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[763,764,765,768,769],{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},{"name":766,"slug":767,"type":16},"PostgreSQL","postgresql",{"name":729,"slug":730,"type":16},{"name":732,"slug":733,"type":16},"2026-07-12T08:36:46.530743",{"slug":772,"name":773,"fn":717,"description":718,"org":774,"tags":775,"stars":711,"repoUrl":712,"updatedAt":780},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[776,777,778,779],{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},{"name":729,"slug":730,"type":16},{"name":732,"slug":733,"type":16},"2026-07-12T08:36:48.104182",{"slug":782,"name":782,"fn":717,"description":718,"org":783,"tags":784,"stars":711,"repoUrl":712,"updatedAt":790},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[785,786,787,788,789],{"name":24,"slug":25,"type":16},{"name":726,"slug":727,"type":16},{"name":753,"slug":754,"type":16},{"name":729,"slug":730,"type":16},{"name":732,"slug":733,"type":16},"2026-07-12T08:36:36.374512",{"slug":792,"name":792,"fn":793,"description":794,"org":795,"tags":796,"stars":807,"repoUrl":808,"updatedAt":809},"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},[797,800,803,804],{"name":798,"slug":799,"type":16},"Accounting","accounting",{"name":801,"slug":802,"type":16},"Analytics","analytics",{"name":666,"slug":667,"type":16},{"name":805,"slug":806,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":811,"name":811,"fn":812,"description":813,"org":814,"tags":815,"stars":807,"repoUrl":808,"updatedAt":824},"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},[816,817,818,821],{"name":24,"slug":25,"type":16},{"name":805,"slug":806,"type":16},{"name":819,"slug":820,"type":16},"Management","management",{"name":822,"slug":823,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":826,"name":826,"fn":827,"description":828,"org":829,"tags":830,"stars":807,"repoUrl":808,"updatedAt":839},"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},[831,832,833,836],{"name":801,"slug":802,"type":16},{"name":805,"slug":806,"type":16},{"name":834,"slug":835,"type":16},"Financial Statements","financial-statements",{"name":837,"slug":838,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":841,"name":841,"fn":842,"description":843,"org":844,"tags":845,"stars":807,"repoUrl":808,"updatedAt":854},"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},[846,849,852],{"name":847,"slug":848,"type":16},"Automation","automation",{"name":850,"slug":851,"type":16},"Documents","documents",{"name":853,"slug":841,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":856,"name":856,"fn":857,"description":858,"org":859,"tags":860,"stars":807,"repoUrl":808,"updatedAt":869},"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},[861,862,865,866],{"name":798,"slug":799,"type":16},{"name":863,"slug":864,"type":16},"Data Analysis","data-analysis",{"name":805,"slug":806,"type":16},{"name":867,"slug":868,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150]