Skills correspondant au nom, à la description ou à l’auteur GitHub.
Test implementation of thin router skill for DiPeO backend. Provides decision criteria and documentation anchors for FastAPI server, CLI (dipeo run/results/metrics/compile/export), SQLite schema, and MCP integr...
Use when building web APIs, backend services, or encountering FastAPI/Django/Express/GraphQL questions, microservices architecture, authentication, or message queues - routes to 11 specialist skills rather than...
Production-grade FastAPI development with async patterns, Pydantic v2, dependency injection, ML/AI endpoint design, and modern Python best practices for building high-performance REST APIs
Defines patterns for implementing FastAPI REST endpoints, including route definition, request/response models, error handling, and integration tests. Use when implementing any API endpoint to ensure consistency...
API 개발 전문가. REST API, FastAPI, Flask, 인증, 문서화.
Generates OpenAPI 3.x specifications from existing code or API designs. This skill should be used when users want to document APIs, generate OpenAPI specs from Flask/FastAPI/Express routes, or create API contra...
Provides REST and GraphQL API design patterns for Node.js, Flask, and FastAPI. Use when designing endpoints, request/response structures, API architecture, pagination, authentication, rate limiting, or when wor...
FastAPI modern Python web framework. Use for building APIs, async endpoints, dependency injection, and Python backend development.
FastAPI framework workflow guidelines. Activate when working with FastAPI projects, uvicorn, or FastAPI-specific patterns.
Python FastAPI patterns, Alembic migrations, Pydantic v2, and AI Engineering (raw patterns, LangChain, Google ADK). This skill should be used when working with Python APIs, database migrations, or AI/LLM applic...
Building REST APIs with FastAPI, Pydantic validation, and OpenAPI. Use when creating routes, handling requests, designing endpoints, implementing validation, error responses, pagination, or generating API docum...
Senior Python Backend Engineer skill for FastAPI. Use when scaffolding production-ready APIs, enforcing clean architecture, optimizing async patterns, or auditing FastAPI codebases.
This skill should be used when the user asks to "create a FastAPI app", "add an API endpoint", "create a REST API", "implement authentication", "add OAuth2", "create Pydantic models", "add request validation",...
This skill should be used when the user asks to "create a FastAPI app", "add an API endpoint", "create a REST API", "implement authentication", "add OAuth2", "create Pydantic models", "add request validation",...
FastAPI, REST APIs, GraphQL, data service design, and API best practices
[Extends backend-developer] Python FastAPI specialist. Use for FastAPI apps, async endpoints, Pydantic v2, SQLAlchemy async, dependency injection. Invoke alongside backend-developer for Python API projects.
Generates FastAPI endpoints with proper Pydantic models, dependency injection, async handlers, and OpenAPI documentation. Use when building Python REST APIs.
Python backend expert. PROACTIVELY use when working with Django, FastAPI, Flask, Python APIs. Triggers: python, django, fastapi, flask, async python
Builds high-performance FastAPI applications with async/await, Pydantic v2, dependency injection, and SQLAlchemy. Use when creating Python REST APIs, async backends, or microservices.
FastAPI web framework for building modern APIs with async support
Better Auth JWT verification for Python/FastAPI backends. Use when integrating Python APIs with a Better Auth TypeScript server via JWT tokens. Covers JWKS verification, FastAPI dependencies, SQLModel/SQLAlchem...
FastAPI patterns for building high-performance Python APIs. Covers routing, dependency injection, Pydantic models, background tasks, WebSockets, testing, and production deployment.
Create SQLAlchemy base model with UUID, timestamp, and soft delete mixins for FastAPI
Overview and guidelines for FastAPI 3-layer architecture with async SQLAlchemy, Pydantic v2, and best practices
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.