fastapi-sqlmodel-crud-patterns
Standard patterns for building and maintaining CRUD APIs with FastAPI and SQLModel: models, routers, database access, and error handling in a reusable way.
Standard patterns for building and maintaining CRUD APIs with FastAPI and SQLModel: models, routers, database access, and error handling in a reusable way.
WHEN: GraphQL schema review, resolver patterns, N+1 detection, query complexity, API security WHAT: Schema design + N+1 detection + Query complexity + Input validation + Error handling + DataLoader patterns WHEN NOT: REST API → api-documenter, Database schema → schema-reviewer, ORM → orm-reviewer
Creates and configures hosted Model Context Protocol (MCP) server connections for OpenAI Agents SDK
Generate .agents.yml config from user answers. Provides tech stack templates for Rails, Python, Node, and Generic projects.
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.
Enables JMAP email operations using Node.js and jmap-jam library. Use when working with JMAP email servers, FastMail, Cyrus IMAP, Stalwart Mail Server, or when user mentions email search, reading, sending, or mailbox management.
Best practices for jQuery AJAX with JSON data handling including sending/receiving JSON, error handling, security (CSRF protection, XSS prevention), promise patterns, caching, and modern alternatives. Use when working with jQuery AJAX requests, implementing JSON APIs, troubleshooting AJAX issues, or migrating from jQuery to Fetch API.
Generate realistic JSON API mock response files with proper data types, nested structures, and configurable record counts. Triggers on "create mock API response", "generate JSON mock data", "fake API data for", "mock endpoint response".
Generate realistic fake data from JSON schemas, producing 1-50 valid records that conform exactly to the schema. Use when creating sample datasets from schema definitions, populating test databases, or generating example API responses. Returns only valid JSON that passes schema validation.
Thin HTTP layer controllers. Controllers contain zero domain logic, only HTTP concerns. Use when working with controllers, HTTP layer, web vs API patterns, or when user mentions controllers, routes, HTTP responses.
Routes to local LLM for sensitive operations. Use when user says "로컬에서 처리해줘", "외부로 보내지 마", "민감한 데이터야", "보안 감사해줘", "credential 분석", "시크릿 확인", or handles sensitive data that should not leave the machine.
Implement HTTP handlers that parse requests, invoke use cases, and format responses following REST conventions.
给定 API curl 命令或 URL/方法/参数,自动判断 API 类型 (LLM API 或普通 HTTP API),预检连通性后创建 LMeterX 压测任务。
输入一个网页 URL,后端自动爬取页面并识别核心业务 API, 对候选 API 执行连通性预检后创建 LMeterX 压测任务。
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
Use when adding, removing, or changing any public API surface in this repo (method signatures, class members, interface members, enum values, constructor parameters, or serialization behavior). Guides a systematic backward-compatibility check before committing.
Comprehensive knowledge base for jzero framework (enhanced go-zero). Use this skill when working with jzero to understand correct patterns for REST APIs (Handler/Logic/Context architecture), RPC services (service discovery, load balancing), Gateway services, database operations (sqlx, MongoDB, caching), resilience patterns (circuit breaker, rate limiting), and jzero-specific features (git-change-based generation, flexible configuration, custom templates). Essential for generating production-ready jzero code that follows framework conventions.
Creates reusable Flocks tools and API integrations. Supports YAML-HTTP for REST APIs and Python for local utilities, with mandatory verification and smoke testing. All output under ~/.flocks/plugins/tools/. When to use: creating or adding a new Flocks tool, building local utilities such as base64 encode-decode, URL encode-decode, JSON formatting, parsing, hashing, text or file transformation, or integrating an external REST API as a reusable tool. Example requests: "Create a base64 encode/decode tool", "Build a URL encode/decode utility", "Add a JSON formatter tool", "Integrate a REST API as a Flocks tool".
Use when creating OpenAPI mock examples for Microcks, setting up request/response routing with dispatchers, or mapping request fields to mock responses
Creates or modify a functional endpoint of a microservice. Use when explicitly asked by the user to create or modify a functional or RPC endpoint of a microservice.