Set up a project for babysitting. Guides you through onboarding a new or existing project — researches the codebase, interviews you about goals and workflows, builds the project profile, installs the best tools, and optionally configures CI/CD integration.

Analysis for a run and its results, process, suggestions for process improvements, process optimizations, fixes, etc. for the next runs.

Set up babysitter for yourself. Guides you through onboarding — installs dependencies, interviews you about your specialties and preferences, builds your user profile, and configures the best tools for your workflow.

Orchestrate a babysitter run. use this command to start babysitting a complex workflow in a non-interactive mode, without any user interaction or breakpoints in the run.

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.

Orchestrates AWS Amplify Gen 2 workflows for building full-stack apps with React, Next.js, Vue, Angular, React Native, Flutter, Swift, or Android. Use when user wants to BUILD, CREATE, or DEPLOY Amplify projects, add authentication, data models, storage, GraphQL APIs, Lambda functions, or deploy to sandbox/production. Do NOT invoke for conceptual questions, comparisons, or troubleshooting unrelated to active development.

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.

AWS SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL operations, and query plan explainability. 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.

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/SVG/PDF export via draw.io desktop CLI.

Deploy applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS services.

Migrate workloads from Google Cloud Platform to AWS. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration. Runs a 5-phase process: discover GCP resources from Terraform files, clarify migration requirements, design AWS architecture, estimate costs, and plan execution.

Follow the workflow shown below. Locate the dataset, check the file type, and resolve any issues with missing files or wrong file types. Determine the fine-tuning model and fine-tuning strategy. Run s

Transforms a data set provided by the user into their desired format. All transformation code is delivered as a Jupyter notebook.

Manages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/) and resolves project naming.

Before starting this workflow, verify:

Generate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases. Use when users need to collect diagnostics from HyperPod cluster nodes, generate issue reports for AWS Support, investigate node failures or performance problems, document cluster state, or create diagnostic snapshots. Triggers on requests involving issue reports, diagnostic collection, support case preparation, or cluster troubleshooting that requires gathering logs and system information from multiple nodes.

Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.

Identifies the correct deployment pathway based on model characteristics and generates deployment code.

Generate code that evaluates a SageMaker model.

Discovers user intent and generates a structured, step-by-step plan for SageMaker AI model customization workflows (fine-tuning, data preparation, evaluation, deployment). Activate when the user's request relates to these areas or when the user asks to modify the current plan. Handles intent discovery, plan generation, plan iteration, and mid-execution plan alterations.

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.

Integrate Convex static self hosting into existing apps using the latest upstream instructions from get-convex/self-hosting every time. Use when setting up upload APIs, HTTP routes, deployment scripts, migration from external hosting, or troubleshooting static deploy issues across React, Vite, Next.js, and other frontends.

Vite next-generation frontend build tool with fast HMR and optimized builds. Use when configuring Vite, adding plugins, working with dev server, or building for production.

Spec reference: `skills/spec.md`

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Intelligent skill router and creator. Analyzes ANY input to recommend existing skills, improve them, or create new ones. Uses deep iterative analysis with 11 thinking models, regression questioning, evolution lens, and multi-agent synthesis panel. Phase 0 triage ensures you never duplicate existing functionality.

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AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

Android Java development with MVVM, ViewBinding, and Espresso testing

Android Kotlin development with Coroutines, Jetpack Compose, Hilt, and MockK testing

AWS Aurora Serverless v2, RDS Proxy, Data API, connection pooling

AWS DynamoDB single-table design, GSI patterns, SDK v3 TypeScript/Python

Azure Cosmos DB partition keys, consistency levels, change feed, SDK patterns

Cloudflare D1 SQLite database with Workers, Drizzle ORM, migrations

AST-based code graph for fast symbol lookup, dependency analysis, and blast radius via codebase-memory-mcp MCP server

Mandatory code reviews via /code-review before commits and deploys

OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration

Atomic commits, PR size limits, commit thresholds, stacked PRs

Analyze existing repositories, maintain structure, setup guardrails and best practices

Flutter development with Riverpod state management, Freezed, go_router, and mocktail testing

Google Gemini CLI code review with Gemini 2.5 Pro, 1M token context, CI/CD integration

TDD iteration loops using Claude Code Stop hooks - runs tests after each response, feeds failures back automatically

Klaviyo email/SMS marketing - profiles, events, flows, segmentation

AI-first application patterns, LLM testing, prompt management