Enforce FastAPI and Pydantic v2 patterns with mock server conventions.
npx skills add https://github.com/AI-Riksarkivet/ra-hcp --skill hcp-backend
Please help me install this Agent Skill. Skill: hcp-backend Source: https://github.com/AI-Riksarkivet/ra-hcp/tree/main/.claude/skills/hcp-backend Command: npx skills add https://github.com/AI-Riksarkivet/ra-hcp --skill hcp-backend
HCP backend design patterns and mock server conventions that guide backend developers in building robust FastAPI services, ensuring consistency and reliability across the project.
Follow the conventions to structure new backend modules, implement clean domain services, and align with mock server guidelines.
High-intent search queries and answers about installing and using this skill.
FastAPI endpoint development is streamlined by enforcing consistent patterns, using async handlers and Pydantic v2 schemas. This approach ensures reliability and clean architecture across your backend project structure.
Wiring a mock server with FastAPI involves following established conventions to maintain consistency between endpoints and tests. This ensures your mock server behavior aligns correctly with clean domain services and project guidelines.
Specific modern Python tooling and dependency management are required to support scalable FastAPI services. Best practices for linting and project layout are enforced to maintain clean architecture and robust backend operations.
Pydantic v2 schema validation works with async FastAPI handlers by enforcing consistent patterns for robust service design. This combination provides clear project structure and reliable domain service implementation.
Mock server conventions should be used when adding new backend endpoints to maintain consistency with tests and domain services. They guide backend developers in building robust FastAPI services during the development phase.
These backend patterns can be applied when writing FastAPI endpoints and Pydantic models to achieve clean architecture. They satisfy requirements for modern Python tooling, clear project structure, and scalable domain services.
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