[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-use-case-specification":3,"mdc-igeupq-key":42,"related-org-aws-labs-use-case-specification":337,"related-repo-aws-labs-use-case-specification":517},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":37,"sourceUrl":40,"mdContent":41},"use-case-specification","create use case specifications for AI","Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.",{"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",[13,17,20,23],{"name":14,"slug":15,"type":16},"Requirements","requirements","tag",{"name":18,"slug":19,"type":16},"Governance","governance",{"name":21,"slug":22,"type":16},"Product Management","product-management",{"name":24,"slug":25,"type":16},"AWS","aws",831,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagent-plugins","2026-07-12T08:39:29.312316",null,127,[32,33,34,25,35,36],"agent-plugins","agent-skills","agents","coding-agent-skills","coding-agents",{"repoUrl":27,"stars":26,"forks":30,"topics":38,"description":39},[32,33,34,25,35,36],"Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS.","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagent-plugins\u002Ftree\u002FHEAD\u002Fplugins\u002Fsagemaker-ai\u002Fskills\u002Fuse-case-specification","---\nname: use-case-specification\ndescription: Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.\nmetadata:\n  version: \"1.0.0\"\n---\n\n# Use Case Specification\n\nMulti-turn conversation to gather use case details and produce a use case specification document.\n\n## Principles\n\n1. **One thing at a time.** Each response advances exactly one decision or collects one piece of information.\n2. **Confirm before proceeding.** Wait for the user to approve the spec before considering this skill complete.\n3. **Infer, don't interrogate.** Use what's already known from the conversation. Only ask when you truly can't infer.\n4. **Do NOT ask about base model selection.** Model selection is handled exclusively by the model-selection skill.\n\n## Workflow\n\n### Step 0: Check for Existing Spec\n\nBefore starting discovery, check if a `*_use_case_spec.md` file already exists in the project. If it does, present it to the user and ask whether they want to reuse it, modify it, or start fresh.\n\n### Phase 1: Discovery (1–3 turns)\n\nReview what is already known from the conversation so far, then identify what is still missing. You need these three things:\n\n- **What** is the problem the user is trying to solve with model customization\n- **Who** will use the finetuned model and in what context\n- **Which** success criteria can be used to evaluate how well the custom model performs compared to the base model on a test set. Success criteria must be measurable by an LLM-as-a-Judge (e.g., response accuracy, tone adherence) — not things like latency or throughput.\n\n**Guidelines**:\n\n- Infer as much as possible from what the user has already said\n- If the user gave examples, use them to fill gaps rather than asking again\n- Only ask clarifying questions when you cannot infer the information needed for Phase 2\n- If everything is already clear, say \"You've given me a clear picture. I'll put together a use case specification now.\" and move to Phase 2.\n\n⏸ Wait for user after each clarifying question.\n\n### Phase 2: Producing a Use Case Specification Document\n\n1. Save all generated artifacts under the project directory structure defined by the directory-management skill, if available.\n2. Synthesize the information you collected from the user into a Markdown document called [relevant_title]_use_case_spec.md containing the following fields (and only these fields):\n\n```\nUse case description\n  - Concise problem statement + what the custom model will do\n  - Field name: “Business Problem”\n  - Type: String\n\nKey stakeholders\n  - Who uses the model and in what context\n  - Field name: “Primary Users”\n  - Type: String, comma separated if there are multiple \n\nSuccess criteria\n  - A list of 3 criteria (a short name and a description) with which the user measure the success of the custom model. \n  - Field name: “Success Tenets”\n  - Type: list of name-description pairs\n```\n\n1. Present the use case specification in a human-readable format as follows:\n\nI have put together a use case specification and saved it in [relevant_title]_use_case_spec.md.\n\nA use case specification is a design principle recommended by the [AWS Responsible AI Lens](https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fresponsible-ai-lens\u002Fdesign-principles.html).\n\n[use case in human-readable format]\n\nDoes this match your intent?\n\n⏸ Wait for user approval.\n\n## use_case_specification Edit Protocol\n\n- If the user requests changes pertaining to any information covered by use_case_spec.md, you must edit it accordingly and ask for confirmation again.\n- The user can edit use_case_spec.md directly if they want to. If the user says they've updated the file directly, read it to get the latest in your context.\n",{"data":43,"body":46},{"name":4,"description":6,"metadata":44},{"version":45},"1.0.0",{"type":47,"children":48},"root",[49,57,63,70,116,122,129,143,149,154,188,198,221,226,232,253,265,273,284,300,308,313,318,324],{"type":50,"tag":51,"props":52,"children":53},"element","h1",{"id":4},[54],{"type":55,"value":56},"text","Use Case Specification",{"type":50,"tag":58,"props":59,"children":60},"p",{},[61],{"type":55,"value":62},"Multi-turn conversation to gather use case details and produce a use case specification document.",{"type":50,"tag":64,"props":65,"children":67},"h2",{"id":66},"principles",[68],{"type":55,"value":69},"Principles",{"type":50,"tag":71,"props":72,"children":73},"ol",{},[74,86,96,106],{"type":50,"tag":75,"props":76,"children":77},"li",{},[78,84],{"type":50,"tag":79,"props":80,"children":81},"strong",{},[82],{"type":55,"value":83},"One thing at a time.",{"type":55,"value":85}," Each response advances exactly one decision or collects one piece of information.",{"type":50,"tag":75,"props":87,"children":88},{},[89,94],{"type":50,"tag":79,"props":90,"children":91},{},[92],{"type":55,"value":93},"Confirm before proceeding.",{"type":55,"value":95}," Wait for the user to approve the spec before considering this skill complete.",{"type":50,"tag":75,"props":97,"children":98},{},[99,104],{"type":50,"tag":79,"props":100,"children":101},{},[102],{"type":55,"value":103},"Infer, don't interrogate.",{"type":55,"value":105}," Use what's already known from the conversation. Only ask when you truly can't infer.",{"type":50,"tag":75,"props":107,"children":108},{},[109,114],{"type":50,"tag":79,"props":110,"children":111},{},[112],{"type":55,"value":113},"Do NOT ask about base model selection.",{"type":55,"value":115}," Model selection is handled exclusively by the model-selection skill.",{"type":50,"tag":64,"props":117,"children":119},{"id":118},"workflow",[120],{"type":55,"value":121},"Workflow",{"type":50,"tag":123,"props":124,"children":126},"h3",{"id":125},"step-0-check-for-existing-spec",[127],{"type":55,"value":128},"Step 0: Check for Existing Spec",{"type":50,"tag":58,"props":130,"children":131},{},[132,134,141],{"type":55,"value":133},"Before starting discovery, check if a ",{"type":50,"tag":135,"props":136,"children":138},"code",{"className":137},[],[139],{"type":55,"value":140},"*_use_case_spec.md",{"type":55,"value":142}," file already exists in the project. If it does, present it to the user and ask whether they want to reuse it, modify it, or start fresh.",{"type":50,"tag":123,"props":144,"children":146},{"id":145},"phase-1-discovery-13-turns",[147],{"type":55,"value":148},"Phase 1: Discovery (1–3 turns)",{"type":50,"tag":58,"props":150,"children":151},{},[152],{"type":55,"value":153},"Review what is already known from the conversation so far, then identify what is still missing. You need these three things:",{"type":50,"tag":155,"props":156,"children":157},"ul",{},[158,168,178],{"type":50,"tag":75,"props":159,"children":160},{},[161,166],{"type":50,"tag":79,"props":162,"children":163},{},[164],{"type":55,"value":165},"What",{"type":55,"value":167}," is the problem the user is trying to solve with model customization",{"type":50,"tag":75,"props":169,"children":170},{},[171,176],{"type":50,"tag":79,"props":172,"children":173},{},[174],{"type":55,"value":175},"Who",{"type":55,"value":177}," will use the finetuned model and in what context",{"type":50,"tag":75,"props":179,"children":180},{},[181,186],{"type":50,"tag":79,"props":182,"children":183},{},[184],{"type":55,"value":185},"Which",{"type":55,"value":187}," success criteria can be used to evaluate how well the custom model performs compared to the base model on a test set. Success criteria must be measurable by an LLM-as-a-Judge (e.g., response accuracy, tone adherence) — not things like latency or throughput.",{"type":50,"tag":58,"props":189,"children":190},{},[191,196],{"type":50,"tag":79,"props":192,"children":193},{},[194],{"type":55,"value":195},"Guidelines",{"type":55,"value":197},":",{"type":50,"tag":155,"props":199,"children":200},{},[201,206,211,216],{"type":50,"tag":75,"props":202,"children":203},{},[204],{"type":55,"value":205},"Infer as much as possible from what the user has already said",{"type":50,"tag":75,"props":207,"children":208},{},[209],{"type":55,"value":210},"If the user gave examples, use them to fill gaps rather than asking again",{"type":50,"tag":75,"props":212,"children":213},{},[214],{"type":55,"value":215},"Only ask clarifying questions when you cannot infer the information needed for Phase 2",{"type":50,"tag":75,"props":217,"children":218},{},[219],{"type":55,"value":220},"If everything is already clear, say \"You've given me a clear picture. I'll put together a use case specification now.\" and move to Phase 2.",{"type":50,"tag":58,"props":222,"children":223},{},[224],{"type":55,"value":225},"⏸ Wait for user after each clarifying question.",{"type":50,"tag":123,"props":227,"children":229},{"id":228},"phase-2-producing-a-use-case-specification-document",[230],{"type":55,"value":231},"Phase 2: Producing a Use Case Specification Document",{"type":50,"tag":71,"props":233,"children":234},{},[235,240],{"type":50,"tag":75,"props":236,"children":237},{},[238],{"type":55,"value":239},"Save all generated artifacts under the project directory structure defined by the directory-management skill, if available.",{"type":50,"tag":75,"props":241,"children":242},{},[243,245,251],{"type":55,"value":244},"Synthesize the information you collected from the user into a Markdown document called ",{"type":50,"tag":246,"props":247,"children":248},"span",{},[249],{"type":55,"value":250},"relevant_title",{"type":55,"value":252},"_use_case_spec.md containing the following fields (and only these fields):",{"type":50,"tag":254,"props":255,"children":259},"pre",{"className":256,"code":258,"language":55},[257],"language-text","Use case description\n  - Concise problem statement + what the custom model will do\n  - Field name: “Business Problem”\n  - Type: String\n\nKey stakeholders\n  - Who uses the model and in what context\n  - Field name: “Primary Users”\n  - Type: String, comma separated if there are multiple \n\nSuccess criteria\n  - A list of 3 criteria (a short name and a description) with which the user measure the success of the custom model. \n  - Field name: “Success Tenets”\n  - Type: list of name-description pairs\n",[260],{"type":50,"tag":135,"props":261,"children":263},{"__ignoreMap":262},"",[264],{"type":55,"value":258},{"type":50,"tag":71,"props":266,"children":267},{},[268],{"type":50,"tag":75,"props":269,"children":270},{},[271],{"type":55,"value":272},"Present the use case specification in a human-readable format as follows:",{"type":50,"tag":58,"props":274,"children":275},{},[276,278,282],{"type":55,"value":277},"I have put together a use case specification and saved it in ",{"type":50,"tag":246,"props":279,"children":280},{},[281],{"type":55,"value":250},{"type":55,"value":283},"_use_case_spec.md.",{"type":50,"tag":58,"props":285,"children":286},{},[287,289,298],{"type":55,"value":288},"A use case specification is a design principle recommended by the ",{"type":50,"tag":290,"props":291,"children":295},"a",{"href":292,"rel":293},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fresponsible-ai-lens\u002Fdesign-principles.html",[294],"nofollow",[296],{"type":55,"value":297},"AWS Responsible AI Lens",{"type":55,"value":299},".",{"type":50,"tag":58,"props":301,"children":302},{},[303],{"type":50,"tag":246,"props":304,"children":305},{},[306],{"type":55,"value":307},"use case in human-readable format",{"type":50,"tag":58,"props":309,"children":310},{},[311],{"type":55,"value":312},"Does this match your intent?",{"type":50,"tag":58,"props":314,"children":315},{},[316],{"type":55,"value":317},"⏸ Wait for user approval.",{"type":50,"tag":64,"props":319,"children":321},{"id":320},"use_case_specification-edit-protocol",[322],{"type":55,"value":323},"use_case_specification Edit Protocol",{"type":50,"tag":155,"props":325,"children":326},{},[327,332],{"type":50,"tag":75,"props":328,"children":329},{},[330],{"type":55,"value":331},"If the user requests changes pertaining to any information covered by use_case_spec.md, you must edit it accordingly and ask for confirmation again.",{"type":50,"tag":75,"props":333,"children":334},{},[335],{"type":55,"value":336},"The user can edit use_case_spec.md directly if they want to. If the user says they've updated the file directly, read it to get the latest in your context.",{"items":338,"total":516},[339,358,379,389,402,415,425,435,456,471,486,501],{"slug":340,"name":340,"fn":341,"description":342,"org":343,"tags":344,"stars":355,"repoUrl":356,"updatedAt":357},"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},[345,346,349,352],{"name":24,"slug":25,"type":16},{"name":347,"slug":348,"type":16},"Debugging","debugging",{"name":350,"slug":351,"type":16},"Logs","logs",{"name":353,"slug":354,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":359,"name":360,"fn":361,"description":362,"org":363,"tags":364,"stars":355,"repoUrl":356,"updatedAt":378},"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},[365,368,369,372,375],{"name":366,"slug":367,"type":16},"Aurora","aurora",{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},"Database","database",{"name":373,"slug":374,"type":16},"Serverless","serverless",{"name":376,"slug":377,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":380,"name":381,"fn":361,"description":362,"org":382,"tags":383,"stars":355,"repoUrl":356,"updatedAt":388},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[384,385,386,387],{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":373,"slug":374,"type":16},{"name":376,"slug":377,"type":16},"2026-07-12T08:36:42.694299",{"slug":390,"name":391,"fn":361,"description":362,"org":392,"tags":393,"stars":355,"repoUrl":356,"updatedAt":401},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[394,395,396,399,400],{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":397,"slug":398,"type":16},"Migration","migration",{"name":373,"slug":374,"type":16},{"name":376,"slug":377,"type":16},"2026-07-12T08:36:38.584057",{"slug":403,"name":404,"fn":361,"description":362,"org":405,"tags":406,"stars":355,"repoUrl":356,"updatedAt":414},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[407,408,409,412,413],{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":410,"slug":411,"type":16},"PostgreSQL","postgresql",{"name":373,"slug":374,"type":16},{"name":376,"slug":377,"type":16},"2026-07-12T08:36:46.530743",{"slug":416,"name":417,"fn":361,"description":362,"org":418,"tags":419,"stars":355,"repoUrl":356,"updatedAt":424},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[420,421,422,423],{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":373,"slug":374,"type":16},{"name":376,"slug":377,"type":16},"2026-07-12T08:36:48.104182",{"slug":426,"name":426,"fn":361,"description":362,"org":427,"tags":428,"stars":355,"repoUrl":356,"updatedAt":434},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[429,430,431,432,433],{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":397,"slug":398,"type":16},{"name":373,"slug":374,"type":16},{"name":376,"slug":377,"type":16},"2026-07-12T08:36:36.374512",{"slug":436,"name":436,"fn":437,"description":438,"org":439,"tags":440,"stars":453,"repoUrl":454,"updatedAt":455},"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},[441,444,447,450],{"name":442,"slug":443,"type":16},"Accounting","accounting",{"name":445,"slug":446,"type":16},"Analytics","analytics",{"name":448,"slug":449,"type":16},"Cost Optimization","cost-optimization",{"name":451,"slug":452,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":457,"name":457,"fn":458,"description":459,"org":460,"tags":461,"stars":453,"repoUrl":454,"updatedAt":470},"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},[462,463,464,467],{"name":24,"slug":25,"type":16},{"name":451,"slug":452,"type":16},{"name":465,"slug":466,"type":16},"Management","management",{"name":468,"slug":469,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":472,"name":472,"fn":473,"description":474,"org":475,"tags":476,"stars":453,"repoUrl":454,"updatedAt":485},"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},[477,478,479,482],{"name":445,"slug":446,"type":16},{"name":451,"slug":452,"type":16},{"name":480,"slug":481,"type":16},"Financial Statements","financial-statements",{"name":483,"slug":484,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":487,"name":487,"fn":488,"description":489,"org":490,"tags":491,"stars":453,"repoUrl":454,"updatedAt":500},"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},[492,495,498],{"name":493,"slug":494,"type":16},"Automation","automation",{"name":496,"slug":497,"type":16},"Documents","documents",{"name":499,"slug":487,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":502,"name":502,"fn":503,"description":504,"org":505,"tags":506,"stars":453,"repoUrl":454,"updatedAt":515},"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},[507,508,511,512],{"name":442,"slug":443,"type":16},{"name":509,"slug":510,"type":16},"Data Analysis","data-analysis",{"name":451,"slug":452,"type":16},{"name":513,"slug":514,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150,{"items":518,"total":619},[519,536,554,566,583,594,604],{"slug":520,"name":520,"fn":521,"description":522,"org":523,"tags":524,"stars":26,"repoUrl":27,"updatedAt":535},"amazon-location-service","integrate Amazon Location Service maps","Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[525,528,529,532],{"name":526,"slug":527,"type":16},"API Development","api-development",{"name":24,"slug":25,"type":16},{"name":530,"slug":531,"type":16},"Maps","maps",{"name":533,"slug":534,"type":16},"Navigation","navigation","2026-07-12T08:39:49.88311",{"slug":537,"name":537,"fn":538,"description":539,"org":540,"tags":541,"stars":26,"repoUrl":27,"updatedAt":553},"amplify-workflow","build and deploy apps with AWS Amplify","Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync\u002FDynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue, Angular, React Native, Flutter, Swift, and Android. Always use this skill for Amplify Gen2 topics — even for questions you think you know — it contains validated, version-specific patterns that prevent common mistakes. TRIGGER when: user mentions Amplify Gen2; project has amplify\u002F directory or amplify_outputs; code imports @aws-amplify packages; user asks about defineBackend, defineAuth, defineData, defineStorage, or npx ampx. SKIP: Amplify Gen1 (amplify CLI v6), standalone SAM\u002FCDK without Amplify (use aws-serverless), direct Bedrock without Amplify AI Kit (use bedrock).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[542,545,546,547,550],{"name":543,"slug":544,"type":16},"Auth","auth",{"name":24,"slug":25,"type":16},{"name":370,"slug":371,"type":16},{"name":548,"slug":549,"type":16},"Deployment","deployment",{"name":551,"slug":552,"type":16},"TypeScript","typescript","2026-07-12T08:39:43.500162",{"slug":555,"name":555,"fn":556,"description":557,"org":558,"tags":559,"stars":26,"repoUrl":27,"updatedAt":565},"api-gateway","build and manage Amazon API Gateway APIs","Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS failures) and IaC templates containing API Gateway resources. For general REST API design unrelated to AWS, do not trigger.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[560,561,562],{"name":526,"slug":527,"type":16},{"name":24,"slug":25,"type":16},{"name":563,"slug":564,"type":16},"REST API","rest-api","2026-07-12T08:39:00.149339",{"slug":567,"name":567,"fn":568,"description":569,"org":570,"tags":571,"stars":26,"repoUrl":27,"updatedAt":582},"aws-architecture-diagram","generate AWS architecture diagrams","Generate validated AWS architecture diagrams as draw.io XML using official AWS4 icon libraries. Use this skill whenever the user wants to create, generate, or design AWS architecture diagrams, cloud infrastructure diagrams, or system design visuals. Also triggers for requests to visualize existing infrastructure from CloudFormation, CDK, or Terraform code. Supports two modes: analyze an existing codebase to auto-generate diagrams, or brainstorm interactively from scratch. Exports .drawio files with optional PNG\u002FSVG\u002FPDF export via draw.io desktop CLI.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[572,575,576,579],{"name":573,"slug":574,"type":16},"Architecture","architecture",{"name":24,"slug":25,"type":16},{"name":577,"slug":578,"type":16},"Design","design",{"name":580,"slug":581,"type":16},"Diagrams","diagrams","2026-07-12T08:37:11.012278",{"slug":584,"name":584,"fn":585,"description":586,"org":587,"tags":588,"stars":26,"repoUrl":27,"updatedAt":593},"aws-lambda","build and deploy AWS Lambda functions","Design, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[589,590,591,592],{"name":526,"slug":527,"type":16},{"name":24,"slug":25,"type":16},{"name":548,"slug":549,"type":16},{"name":373,"slug":374,"type":16},"2026-07-12T08:38:58.598492",{"slug":595,"name":595,"fn":596,"description":597,"org":598,"tags":599,"stars":26,"repoUrl":27,"updatedAt":603},"aws-lambda-durable-functions","build resilient AWS Lambda durable functions","Build resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait\u002Fcallback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry\u002Fcheckpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[600,601,602],{"name":573,"slug":574,"type":16},{"name":24,"slug":25,"type":16},{"name":373,"slug":374,"type":16},"2026-07-12T08:39:05.546173",{"slug":605,"name":605,"fn":606,"description":607,"org":608,"tags":609,"stars":26,"repoUrl":27,"updatedAt":618},"aws-lambda-managed-instances","configure AWS Lambda Managed Instances","Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a schedule, or compare Lambda vs EC2 for steady-state traffic. For standard Lambda without LMI, use the aws-lambda skill instead.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[610,611,612,615],{"name":24,"slug":25,"type":16},{"name":548,"slug":549,"type":16},{"name":613,"slug":614,"type":16},"Infrastructure","infrastructure",{"name":616,"slug":617,"type":16},"Performance","performance","2026-07-12T08:39:07.007071",33]