Creates professional AWS architecture diagrams in draw.io XML format (.drawio files) using official AWS Architecture Icons (aws4 library). Use when the user asks for AWS diagrams, VPC layouts, multi-tier architectures, serverless designs, network topology, or draw.io exports involving Lambda, EC2, RDS, or other AWS services.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-drawio-architecture-diagrams
Creates pixel-perfect AWS architecture diagrams in draw.io XML format using official AWS4 shape library. Generates production-ready .drawio files for diagrams.net.
Every .drawio file follows this XML structure:
<mxfile host="app.diagrams.net" agent="Claude" version="24.7.17">
<diagram id="aws-arch-1" name="AWS Architecture">
<mxGraphModel dx="1434" dy="759" grid="1" gridSize="10" guides="1"
tooltips="1" connect="1" arrows="1" fold="1" page="1"
pageScale="1" pageWidth="1169" pageHeight="827" math="0" shadow="0">
<root>
<mxCell id="0" />
<mxCell id="1" parent="0" />
<!-- AWS shapes and connectors -->
</root>
</mxGraphModel>
</diagram>
</mxfile>
Key rules:
pageWidth="1169" pageHeight="827")Groups use container=1 with child shapes referencing via parent="groupId".
AWS Cloud (top-level boundary):
<mxCell id="2" value="AWS Cloud" style="points=[[0,0],[0.25,0],[0.5,0],[0.75,0],[1,0],[1,0.25],[1,0.5],[1,0.75],[1,1],[0.75,1],[0.5,1],[0.25,1],[0,1],[0,0.75],[0,0.5],[0,0.25]];outlineConnect=0;gradientColor=none;html=1;whiteSpace=wrap;fontSize=12;fontStyle=0;shape=mxgraph.aws4.group;grIcon=mxgraph.aws4.group_aws_cloud_alt;strokeColor=#232F3E;fillColor=none;verticalAlign=top;align=left;spacingLeft=30;fontColor=#232F3E;dashed=0;labelBackgroundColor=none;container=1;pointerEvents=0;collapsible=0;recursiveResize=0;" vertex="1" parent="1">
<mxGeometry x="100" y="40" width="1000" height="700" as="geometry" />
</mxCell>
Region:
<mxCell id="3" value="us-east-1" style="...grIcon=mxgraph.aws4.group_region;strokeColor=#00A4A6;fontColor=#147EBA;dashed=1;..." vertex="1" parent="2">
<mxGeometry x="20" y="40" width="960" height="640" as="geometry" />
</mxCell>
VPC:
<mxCell id="4" value="VPC (10.0.0.0/16)" style="...grIcon=mxgraph.aws4.group_vpc;strokeColor=#8C4FFF;fontColor=#AAB7B8;..." vertex="1" parent="3">
<mxGeometry x="20" y="40" width="920" height="580" as="geometry" />
</mxCell>
Subnet styles:
strokeColor=#7AA116;fillColor=#E9F3D2;fontColor=#248814strokeColor=#00A4A6;fillColor=#E6F6F7;fontColor=#147EBAService icons use shape=mxgraph.aws4.resourceIcon with resIcon property.
CRITICAL: strokeColor=#ffffff is required for resourceIcon shapes to render white icon glyphs on colored backgrounds.
Standard service icon:
<mxCell id="10" value="Amazon S3" style="...gradientColor=#60A337;gradientDirection=north;fillColor=#277116;strokeColor=#ffffff;...shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.s3;" vertex="1" parent="1">
<mxGeometry x="100" y="100" width="60" height="60" as="geometry" />
</mxCell>
Dedicated shapes (Lambda, ALB, Users) use strokeColor=none. See references/aws-shape-reference.md for complete shape catalog.
Each AWS service category uses official colors. All resourceIcon shapes must use strokeColor=#ffffff and gradientDirection=north. See references/aws-shape-reference.md for full color table.
Quick reference:
| Category | fillColor | gradientColor | Services |
|----------|-----------|---------------|----------|
| Compute | #D05C17 | #F78E04 | EC2, ECS, EKS, Fargate |
| Storage | #277116 | #60A337 | S3, EBS, EFS, Glacier |
| Database | #3334B9 | #4D72F3 | RDS, DynamoDB, Aurora, Redshift |
| Networking | #5A30B5 | #945DF2 | CloudFront, Route 53, API GW |
| Security | #C7131F | #F54749 | IAM, Cognito, KMS, WAF |
| App Integration | #BC1356 | #F54749 | SQS, SNS, EventBridge |
Standard data flow:
edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=open;endFill=0;strokeColor=#545B64;strokeWidth=2;
Encrypted connection:
edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=classic;endFill=1;strokeColor=#DD344C;strokeWidth=2;dashed=1;dashPattern=5 5;
Async/event flow:
edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=open;endFill=0;strokeColor=#E7157B;strokeWidth=2;dashed=1;
verticalLabelPosition=bottom)User Request: "Create AWS three-tier architecture with VPC, public ALB, private EC2, RDS across 2 AZs."
Generated Output:
<mxfile host="app.diagrams.net" agent="Claude" version="24.7.17">
<diagram id="three-tier-1" name="Three-Tier Web App">
<mxGraphModel dx="1434" dy="759" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1169" pageHeight="827" math="0" shadow="0">
<root>
<mxCell id="0" />
<mxCell id="1" parent="0" />
<mxCell id="2" value="Users" style="...shape=mxgraph.aws4.users;fillColor=#232F3E;..." vertex="1" parent="1">
<mxGeometry x="40" y="340" width="60" height="60" as="geometry" />
</mxCell>
<mxCell id="3" value="AWS Cloud" style="...shape=mxgraph.aws4.group;grIcon=mxgraph.aws4.group_aws_cloud_alt;..." vertex="1" parent="1">
<mxGeometry x="160" y="40" width="960" height="720" as="geometry" />
</mxCell>
<mxCell id="4" value="us-east-1" style="...grIcon=mxgraph.aws4.group_region;strokeColor=#00A4A6;dashed=1;..." vertex="1" parent="3">
<mxGeometry x="20" y="40" width="920" height="660" as="geometry" />
</mxCell>
<mxCell id="5" value="VPC (10.0.0.0/16)" style="...grIcon=mxgraph.aws4.group_vpc;strokeColor=#8C4FFF;..." vertex="1" parent="4">
<mxGeometry x="20" y="40" width="880" height="600" as="geometry" />
</mxCell>
<mxCell id="6" value="Public Subnet" style="...grIcon=mxgraph.aws4.group_security_group;strokeColor=#7AA116;fillColor=#E9F3D2;..." vertex="1" parent="5">
<mxGeometry x="20" y="40" width="400" height="160" as="geometry" />
</mxCell>
<mxCell id="7" value="Private Subnet" style="...grIcon=mxgraph.aws4.group_security_group;strokeColor=#00A4A6;fillColor=#E6F6F7;..." vertex="1" parent="5">
<mxGeometry x="20" y="230" width="400" height="160" as="geometry" />
</mxCell>
<mxCell id="8" value="Data Subnet" style="...grIcon=mxgraph.aws4.group_security_group;strokeColor=#00A4A6;fillColor=#E6F6F7;..." vertex="1" parent="5">
<mxGeometry x="20" y="420" width="400" height="160" as="geometry" />
</mxCell>
<mxCell id="12" value="Application<br>Load Balancer" style="...fillColor=#8C4FFF;shape=mxgraph.aws4.applicationLoadBalancer;" vertex="1" parent="6">
<mxGeometry x="170" y="50" width="60" height="60" as="geometry" />
</mxCell>
<mxCell id="13" value="EC2 Instance" style="...gradientColor=#F78E04;fillColor=#D05C17;strokeColor=#ffffff;shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.ec2;" vertex="1" parent="7">
<mxGeometry x="170" y="50" width="60" height="60" as="geometry" />
</mxCell>
<mxCell id="15" value="RDS Primary" style="...gradientColor=#4D72F3;fillColor=#3334B9;strokeColor=#ffffff;shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.rds;" vertex="1" parent="8">
<mxGeometry x="170" y="50" width="60" height="60" as="geometry" />
</mxCell>
<mxCell id="20" style="...endArrow=open;strokeColor=#545B64;strokeWidth=2;" edge="1" parent="1" source="2" target="12">
<mxGeometry relative="1" as="geometry" />
</mxCell>
<mxCell id="21" value="HTTPS" style="...endArrow=open;strokeColor=#545B64;strokeWidth=2;fontSize=11;labelBackgroundColor=#FFFFFF;" edge="1" parent="1" source="12" target="13">
<mxGeometry relative="1" as="geometry" />
</mxCell>
<mxCell id="23" value="TCP 5432" style="...endArrow=open;strokeColor=#545B64;strokeWidth=2;fontSize=11;labelBackgroundColor=#FFFFFF;" edge="1" parent="1" source="13" target="15">
<mxGeometry relative="1" as="geometry" />
</mxCell>
</root>
</mxGraphModel>
</diagram>
</mxfile>
Opening Instructions:
Open in draw.io with AWS libraries enabled:
https://app.diagrams.net/?libs=aws4
User Request: "Create serverless architecture with API Gateway, Lambda, DynamoDB, S3 for REST API."
Generated Output: XML with API Gateway (violet), Lambda (orange), DynamoDB (blue), S3 (green). See references/aws-architecture-templates.md for complete template.
See references/ directory:
aws-shape-reference.md - Complete AWS4 shape catalog with styles for 50+ servicesaws-architecture-templates.md - Ready-to-use templates (3-tier, serverless, data pipeline)Always follow this validation checklist before saving:
.drawio file structureparent attributes reference existing cells&→&, <→<)https://app.diagrams.net/?libs=aws4<br> for line breaks.mxgraph.aws4.* shapes. Legacy mxgraph.aws3.* not supported.<diagram> elementsmxgraph.aws4.*)Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take giuseppe-trisciuoglio/aws-drawio-architecture-diagrams from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.