AI-Native Squad — Spotify-model autonomous engineering team. 12 specialized agents (Tech Lead, Backend, Frontend, Mobile, UX, UI, Product, Scrum Master, DevOps, QA, Data, Security) with orchestrated collaboration, domain-specific skills, and cross-functional workflows. Based on the AI Native Developer methodology.
Use this agent when the user needs help with API development, database design or queries, server-side logic, microservices architecture, REST/GraphQL/gRPC endpoints, authentication and authorization, backend performance optimization, or any server-side engineering task. Also triggers for keywords: "api", "endpoint", "database", "query", "migration", "schema", "microservice", "auth", "jwt", "oauth", "cache", "redis", "queue", "backend", "server". <example> Context: User needs a new API endpoint user: "Create a REST API for user management with CRUD operations and role-based access" assistant: "I'll design the API contract (routes, DTOs, status codes), implement the controller/service/repository layers, add RBAC middleware, write integration tests, and generate OpenAPI docs." <commentary>Full API lifecycle from contract to tests</commentary> </example> <example> Context: User has database performance issues user: "Our product listing query takes 3 seconds, the table has 2M rows" assistant: "I'll analyze the query plan with EXPLAIN ANALYZE, check index coverage, evaluate the schema for denormalization opportunities, implement query optimization (pagination, selective columns, covering indexes), and add a caching layer if needed." <commentary>Database optimization with systematic diagnosis</commentary> </example> <example> Context: User wants to design a microservice architecture user: "Break our monolith's order processing into microservices" assistant: "I'll map the bounded contexts (Order, Payment, Inventory, Notification), define the service boundaries and data ownership, design the inter-service communication (sync via gRPC, async via events), implement the saga pattern for distributed transactions, and set up health checks and circuit breakers." <commentary>Microservice decomposition with distributed systems patterns</commentary> </example>
Use this agent when the user needs help with data pipelines, ETL/ELT processes, data modeling, analytics, data warehouses, BigQuery, Snowflake, event tracking, data quality, schema design, or metrics definition. Examples: <example> Context: Company needs to move data from multiple sources into a warehouse user: "Design a data pipeline to ingest data from our PostgreSQL database and Stripe API into BigQuery" assistant: "I'll design an ELT pipeline with extraction connectors, staging layers, transformation models in dbt, and incremental load strategy." <commentary>Triggers on data pipeline, ETL/ELT, or data ingestion requests</commentary> </example> <example> Context: Product team needs to track user behavior for analytics user: "Set up event tracking for our web app with a proper tracking plan" assistant: "I'll create a tracking plan with event taxonomy, naming conventions, properties schema, and implementation guide for Segment/Amplitude." <commentary>Triggers on analytics event tracking, Segment, Amplitude, or Mixpanel requests</commentary> </example> <example> Context: Team needs a dimensional model for their business domain user: "Create a data model for our e-commerce analytics — orders, products, customers" assistant: "I'll design a star schema with fact tables (orders, order_items) and dimension tables (customers, products, dates) optimized for analytical queries." <commentary>Triggers on data modeling, schema design, or warehouse architecture</commentary> </example>
Use this agent when the user needs help with CI/CD pipelines, Docker, Kubernetes, infrastructure as code, Terraform, monitoring, logging, deployment strategies, cloud architecture (AWS/GCP/Azure), or security scanning. Examples: <example> Context: Team needs automated build, test, and deploy workflow user: "Set up a CI/CD pipeline with GitHub Actions for our Node.js monorepo" assistant: "I'll design a GitHub Actions workflow with build matrix, caching, test parallelization, and staged deployments to staging and production." <commentary>Triggers on CI/CD pipeline setup and automation requests</commentary> </example> <example> Context: Application needs to be containerized for consistent deployments user: "Create a production-ready Dockerfile for our Next.js app" assistant: "I'll build a multi-stage Dockerfile with optimized layers, non-root user, health checks, and minimal final image size." <commentary>Triggers on Docker, containerization, and image optimization requests</commentary> </example> <example> Context: Team needs visibility into production system health user: "Set up monitoring and alerting for our microservices on Kubernetes" assistant: "I'll design a monitoring stack with Prometheus metrics, Grafana dashboards, structured logging, and PagerDuty alert routing." <commentary>Triggers on monitoring, observability, and alerting setup</commentary> </example>
Use this agent when the user needs help with UI implementation, component architecture, React/Vue/Svelte/Angular development, CSS/Tailwind styling, responsive design, state management, accessibility compliance, frontend performance optimization, or design system integration. Also triggers for keywords: "component", "react", "nextjs", "vue", "svelte", "css", "tailwind", "responsive", "accessibility", "a11y", "state management", "redux", "zustand", "frontend", "web vitals", "bundle size", "hydration", "ssr", "client-side". <example> Context: User needs a complex UI component user: "Build a data table component with sorting, filtering, pagination, and row selection" assistant: "I'll architect a composable DataTable with headless logic (sorting/filtering/pagination hooks), render the UI with accessible markup (proper ARIA roles, keyboard navigation), style with Tailwind for responsiveness, add virtualization for large datasets, and write component tests with Testing Library." <commentary>Complex component with accessibility, performance, and testability built in</commentary> </example> <example> Context: User needs state management setup user: "Set up global state management for our e-commerce app — cart, auth, and product filters" assistant: "I'll evaluate the state domains: cart (local + persisted), auth (global + secure), filters (URL-synced). I'll set up Zustand stores with slices, persist middleware for cart, and URL state sync for filters. Each store gets TypeScript types, selectors, and unit tests." <commentary>State architecture with domain-appropriate strategies per state type</commentary> </example> <example> Context: User needs responsive layout implementation user: "Implement the dashboard layout — sidebar nav, top bar, and main content area that works on mobile" assistant: "I'll build a responsive shell layout: collapsible sidebar (drawer on mobile, fixed on desktop), sticky top bar with breadcrumbs, and a fluid content area with CSS Grid. Mobile-first approach with Tailwind breakpoints, smooth transitions for sidebar toggle, and proper focus management for accessibility." <commentary>Responsive layout with mobile-first methodology and a11y considerations</commentary> </example>
Use this agent when the user needs help with mobile app development, React Native, Flutter, Swift, Kotlin, native modules, app store deployment, mobile CI/CD pipelines, push notifications, or deep linking. <example> Context: User is building a new feature for a mobile app user: "Create a new profile screen with tab navigation and pull-to-refresh" assistant: "I'll design and implement the profile screen with proper navigation integration, state management, and pull-to-refresh using platform-appropriate patterns." <commentary>Triggers on new screen implementation requiring cross-platform architecture decisions and native UI patterns.</commentary> </example> <example> Context: User needs to integrate a platform-specific SDK user: "Bridge the native health kit API to our React Native app" assistant: "I'll create the native module bridge for both iOS (HealthKit) and Android (Health Connect) with TypeScript type definitions and error handling." <commentary>Triggers on native module bridge work requiring platform-specific code and cross-platform abstraction.</commentary> </example> <example> Context: User reports app performance issues user: "The app is janky when scrolling through the feed, FPS drops below 30" assistant: "I'll profile the scroll performance, identify render bottlenecks, optimize list virtualization, and reduce unnecessary re-renders on both platforms." <commentary>Triggers on mobile performance optimization requiring profiling, rendering analysis, and platform-specific tuning.</commentary> </example>
Adversarial code review and security audit. Use for thorough code review with a devil's advocate perspective, security vulnerability scanning, architecture review, and quality gates.
Agile ceremonies and rituals facilitation. Use for sprint planning, daily standups, sprint reviews, retrospectives, backlog grooming, and team health checks.
Backend development patterns and best practices. Use for API design, database optimization, microservice architecture, authentication, caching strategies, and server-side implementation.
Data pipeline and analytics engineering patterns. Use for ETL/ELT design, data modeling, event tracking, data quality, and dashboard creation.
CI/CD pipeline and infrastructure patterns. Use for GitHub Actions workflows, Docker configurations, Kubernetes manifests, Terraform modules, monitoring setup, and deployment strategies.
Uses power tools
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Build, Launch, and Scale with AI-Native Architecture.
Lemon AI Hub is a centralized and optimized repository to manage AI Skills and Plugins across different local agents (Claude Code, Codex, OpenCode, Agy). It is the technical foundation of the Lemon.dev ecosystem, designed by Anderson Lima to help founders and developers bridge the gap between AI-generated code and production-ready products.
This project is based on and inspired by Peter Steinberger's pattern (steipete/agent-scripts), but structured with a focus on .claude and shared with other providers via transparent symbolic links.
The best code is invisible—it works, scales, and converts. Lemon AI Hub embodies this by providing:
This repository ships a marketplace manifest (.claude-plugin/marketplace.json) so you can install its plugins directly inside Claude Code.
Inside a Claude Code session, run the slash command:
/plugin marketplace add Andersonlimahw/lemon-ai-hub
Open the interactive installer, or install a plugin by name using the plugin@marketplace syntax:
/plugin # browse the catalog UI
/plugin install spotify-squad@lemon-ai-hub
/plugin install agentic-value-loops@lemon-ai-hub
| Plugin | Description |
|---|---|
a11y-audit | WCAG 2.1 AA/AAA accessibility audit for web components, pages, and apps. Detects contrast failures, missing ARIA labels, keyboard trap issues, focus order problems, and screen-reader gotchas. Use when user wants to audit accessibility, fix a11y warnings, prepare for compliance review, or validate UI against WCAG standards. |
agent-sdk-dev | A comprehensive plugin for creating and verifying Agent SDK applications in Python and TypeScript. |
agentic-value-loops | Agentic Value Loops plugin — Continuous improvement engine that ships verified increments with constant quality. Includes loops for Feature Development, Maintenance & Security, Documentation Sync, and AI Tuning. |
ai-workspace-orchestrator | Project and workspace AI setup orchestrator. Builds AGENTS.md/CLAUDE.md contracts, optional global or project skills, Spotify Squad-style agent/subagent topology, and adversarial setup review for single apps, monorepos, and multi-domain workspaces. |
api-test-loop | API Test Loop plugin — Automated REST API validation loop. Executes HTTP requests via CURL, logs findings for REST best practices (method, input/output format, security, performance, docs), and implements backend fixes. |
app-store-connect-api | Apple Store Connect API Expert Agent and Skills. Provides comprehensive tools and knowledge for interacting with the App Store Connect API, covering domains like TestFlight, In-app purchases, Subscriptions, App management, and Provisioning. |
apple-store-release-agent | AI release agent for the Apple App Store. Audits Expo/React Native iOS builds, App Store metadata, App Privacy readiness, TestFlight, RevenueCat/IAP, Firebase, i18n parity, screenshots, and review notes, then emits a GO/GO_WITH_WARNINGS/NO_GO decision with full risk + privacy + IAP reports. Never submits to App Review or alters the store without explicit human approval. Generic core with presets for Expo/RN/Firebase/RevenueCat apps. |
async-patterns | Async and concurrency pattern advisor for JavaScript/TypeScript, Python, Go, and Rust. Identifies race conditions, missing error handling in promise chains, N+1 async anti-patterns, unbounded parallelism, and missing cancellation. Recommends correct patterns: Promise.all, p-limit, AbortController, AsyncLocalStorage, worker threads. Use when async code has bugs, timeouts, or memory leaks. |
bundle-analyzer | JavaScript/TypeScript bundle size analysis, tree-shaking audit, and code splitting recommendations. Identifies heavy dependencies, duplicate packages, unused imports, and suggests dynamic imports and lazy loading. Use when bundle is too large, Lighthouse score is low, or user wants to optimize web app loading performance. |
chaos-test | Chaos engineering scenarios for resilience testing. Designs fault injection experiments (network partitions, latency injection, dependency failures, disk pressure, memory leaks) and verifies circuit breakers, retries, and fallbacks work correctly. Use when building resilience features, verifying SLO under failure conditions, or preparing for production chaos days. |
Incident response runbooks, on-call workflows, and postmortem templates. Generates severity-tiered runbooks, communication templates, timeline reconstruction, and blameless postmortem docs. Use when user is in an incident, doing on-call prep, writing a postmortem, or building incident response playbooks.
Load testing setup, execution, and analysis with k6, Artillery, or Locust. Generates test scripts, defines VU ramp-up scenarios, interprets p99 latency and error rate results, and suggests infrastructure fixes. Use when user wants to load test an API, check throughput limits, validate SLO headroom, or diagnose performance under traffic.
Database index optimization advisor for PostgreSQL, MySQL, and SQLite. Analyzes slow queries, missing indexes, unused indexes, and over-indexed tables. Generates CREATE INDEX statements with EXPLAIN ANALYZE estimates. Use when queries are slow, p99 DB latency spikes, or when reviewing a new schema.
AI release agent for Google Play Store: audits Android/Expo/React Native/Firebase/RevenueCat before submission. Validates build readiness (app.json, eas.json, AAB, versionCode), Play Console metadata, Data Safety Form, Android permissions, RevenueCat/IAP, i18n parity (PT/EN/ES), policy-risk copy, internal/closed testing and staged rollout. Generates reports + GO/NO-GO checklist. Never publishes or rolls out without explicit human approval. Use when preparing an Android app for Google Play, auditing a submission, generating release notes, validating Data Safety or permissions, or running a release go/no-go.
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npx claudepluginhub andersonlimahw/lemon-ai-hub --plugin spotify-squadAutomatically routes tasks to the optimal AI agent, model, or provider based on complexity, cost, and capability. Use when implementing features, fixing bugs, or any multi-step development work. Triggers on "implement", "build", "create", "fix", "add feature", "develop", or when the user asks to do any coding task.
PROACTIVELY use this agent when complex multi-agent tasks begin, when agents seem stuck or overwhelmed, or when the team needs motivation and coordination. This agent serves as the elite performance coach for all other agents, ensuring they operate at their highest level while maintaining composure and excellence. Should be triggered automatically at the start of challenging projects or when detecting agent confusion. Examples:\n\n<example>\nContext: Starting a complex project requiring multiple agents\nuser: "We need to build a viral TikTok app in 2 weeks"\nassistant: "This is an ambitious goal that will require our A-team! Let me bring in the studio-coach to coordinate our agents and ensure everyone performs at their peak."\n<commentary>\nComplex projects benefit from having a coach to keep all agents aligned and motivated.\n</commentary>\n</example>\n\n<example>\nContext: When an agent seems stuck or is producing subpar results\nagent: "I'm having trouble identifying the right trends..."\nassistant: "Let me bring in the studio-coach to help refocus and elevate your approach."\n<commentary>\nAgents can get overwhelmed or lose focus - the coach helps them recenter and excel.\n</commentary>\n</example>\n\n<example>\nContext: Before launching a major sprint or initiative\nuser: "Tomorrow we start the 6-day sprint for our biggest project yet"\nassistant: "Time to rally the team! I'll have the studio-coach prepare everyone mentally and strategically for peak performance."\n<commentary>\nPre-sprint coaching ensures all agents start with clarity, confidence, and coordination.\n</commentary>\n</example>\n\n<example>\nContext: When celebrating wins or learning from failures\nuser: "Our app just hit #1 on the App Store!"\nassistant: "Incredible achievement! Let me bring in the studio-coach to celebrate with the team and capture what made this success possible."\n<commentary>\nThe coach helps institutionalize wins and extract learnings from both successes and failures.\n</commentary>\n</example>
Full-stack agents — frontend, backend, API, DevOps architects
Autonomous multi-agent development framework with spec-driven sprints and convergent iteration
Mobile engineer — native iOS/Android, cross-platform, app stores, mobile performance
Multi-agent team orchestration for parallel task execution, research, and implementation