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Use when configuring Spring Boot as an OAuth2 resource server, validating JWTs from an external auth provider (Keycloak, Auth0, Okta, Cognito), extracting claims, or implementing scope-based authorization.
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Create Convex queries, mutations, and actions with proper validation, authentication, and error handling. Use when implementing new API endpoints.
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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.
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Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
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Build real-time web applications with Azure Web PubSub SDK for Java. Use when implementing WebSocket-based messaging, live updates, chat applications, or server-to-client push notifications.
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Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
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Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
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Build event-driven applications with Azure Event Grid SDK for Java. Use when publishing events, implementing pub/sub patterns, or integrating with Azure services via events.
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Send SMS messages with Azure Communication Services SMS Java SDK. Use when implementing SMS notifications, alerts, OTP delivery, bulk messaging, or delivery reports.
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Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
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Clarify requirements before implementing. Use when serious doubts arise.
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Hotwire（Turbo + Stimulus）を使用してRuby on Railsの管理画面を実装し、PlaywrightによるE2Eテストを含めた完全な実装を行うスキル
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Accessibility implementation guide for SwiftUI apps. Use when implementing VoiceOver support, adding accessibilityLabel/Hint/Value, supporting Dynamic Type, ensuring color contrast, testing accessibility, or reviewing accessibility in PRs. Covers iOS accessibility APIs, WCAG guidelines, and testing tools.
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SwiftUI UI component catalog. Use when implementing NavigationStack, Swift Charts, PhotosPicker, TipKit, ScrollView enhancements, animations, layouts, WebView, Liquid Glass and other UI components. Organized by feature category with OS version compatibility info. Reference for discovering available components and implementation patterns.
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Android data layer implementation guide. Provides comprehensive patterns for Room Database (Entity, DAO, Migration), DataStore (Preferences), Repository layer design, and common pitfalls. Use when implementing local data storage, cache strategies, or data source integration in Android projects.
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Core coding guidelines for iOS, iPadOS, macOS, watchOS, tvOS, and visionOS app development using SwiftUI and Swift. Use when implementing Views, working with @State/@Binding/@Observable/@StateObject, using NavigationStack/List/LazyVStack, designing components, or asking about SwiftUI patterns (infinite scroll, pull-to-refresh, search). Covers best practices, implementation patterns, and anti-patterns to avoid.
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Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach.
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Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
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Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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