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HL7 FHIR interoperability covering FHIR resources, RESTful API patterns, server implementation, SMART on FHIR authorization, CDS Hooks, Bulk Data Access, and US Core Implementation Guide profiles.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | domain-healthcare:hl7-fhir |
| description | HL7 FHIR interoperability covering FHIR resources, RESTful API patterns, server implementation, SMART on FHIR authorization, CDS Hooks, Bulk Data Access, and US Core Implementation Guide profiles. |
| allowed-tools | Read, Grep, Glob, Bash |
/metadata accurately; clients depend on it to know what you actually supportIf-None-Exist headers on POST; without them, retries create duplicate resourcesreferences/fhir-resources-api.md — core resource table, CRUD operations, search syntax with system/code, _include/_revinclude, Bundle pagination, $everything/$validate/$export operationsreferences/fhir-server-implementation.md — HAPI FHIR setup and interceptors, Microsoft FHIR Server with Azure AD, versioning, conditional create/update, Subscription patterns, capability statementreferences/smart-cds-bulk.md — SMART on FHIR authorization flow, OAuth scopes, EHR and standalone launch contexts, CDS Hooks hook points and card format, Bulk Data NDJSON export, US Core profiles and USCDI alignmentGuides on AI/ML system design, LLM application architecture, MLOps pipelines, AI SaaS platform selection (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure OpenAI, Hugging Face, Cohere, Replicate, Together AI), model serving, feature stores, experiment tracking, evaluation frameworks, RAG architecture, AI agent patterns, responsible AI, cost optimization, and emerging AI/ML trends. Use when designing AI systems, selecting AI providers, building LLM applications, setting up MLOps, or evaluating AI architecture patterns.
AI/LLM integration patterns: Anthropic SDK streaming with TypeScript, RAG architecture (chunking, embedding, vector search with pgvector), tool use / function calling, model selection guide (Haiku 4.5 vs Sonnet 5 vs Opus 4.8), prompt caching, structured output with Zod, token cost management. Use when building AI features, RAG pipelines, AI agents.
Flutter 3.27+ patterns — Riverpod 2 state with codegen, GoRouter 14+ navigation, Impeller rendering (default on all platforms), WASM web, extension types, on-device AI (firebase_ai, MediaPipe), Patrol 3.x testing, platform channels, performance
Expert guidance on Flutter 3.27+ (Impeller default on all platforms, Material 3, WASM web, DevTools, Flame), Dart 3.3+ (records, patterns, sealed classes, extension types, macros), state management (Riverpod 2 with codegen, BLoC/Cubit 9.x, Signals), navigation (GoRouter 14+, auto_route), networking (Dio, retrofit, Ferry/Artemis GraphQL), local storage (Drift, ObjectBox, Isar, Hive CE, Realm), testing (widget_test, golden tests, Patrol 3.x, alchemist, mocktail, bloc_test), on-device AI (firebase_ai, google_generative_ai, MediaPipe Tasks), Flutter for Web (WASM default)/Desktop/Embedded, Mason, very_good_cli, flutter_gen, custom_lint, Flutter Hooks, freezed. Use when building or optimizing Flutter apps.
Designs CI/CD pipelines with build, test, and deploy stages, blue-green/canary/rolling deployments, feature flags, artifact management, and environment promotion strategies. Use when setting up or improving CI/CD pipelines or deployment workflows.
Designs REST, GraphQL, gRPC, tRPC, WebSocket, SSE, and AsyncAPI/event-driven APIs. Covers OpenAPI 3.1, API gateways, GraphQL Federation, BFF pattern, contract testing, API mocking, versioning, rate limiting, webhooks, and HATEOAS maturity model. Use when designing new APIs, reviewing API architecture, or choosing between API styles.