'fastapi' 검색 결과

Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

API hardening for Express, FastAPI, and serverless. Use for rate limiting, CORS, input validation, API keys, or OWASP API Top 10.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Python applications with FastAPI or Flask. Use when designing maintainable backends with separation of concerns, implementing repository patterns, creating entities/value objects/aggregates, or structuring domain logic independent of frameworks for testability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides ChatGLM2-6B Transformers inference, local demos, FastAPI/OpenAI-compatible serving, C-Eval evaluation, and P-Tuning v2 fine-tuning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use BiSheng's enterprise LLM application DevOps platform repository, including FastAPI backend, workflow/RAG/Linsight runtimes, permissions, dual React frontends, deployment, and maintainer workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate boilerplate code and project templates/skeletons automatically. Use when: (1) Creating new projects from scratch (React app, FastAPI backend, Express API), (2) Generating repetitive code patterns (CRUD endpoints, models, controllers), (3) Scaffolding components, services, or modules, (4) Creating test boilerplate, (5) Setting up monorepo structures. Provides project templates and code generation patterns for common development tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Local and self-hosted text-to-speech with Kokoro, the ~82M-parameter open-weight (Apache 2.0) model by hexgrad. Use when synthesizing speech offline, on-device, in the browser, or on your own server without per-character API cost — for high-volume narration, audiobooks, privacy-constrained pipelines, and prototyping. Covers running Kokoro via the Python `kokoro` package, ONNX / kokoro-js in the browser, and the OpenAI-compatible Kokoro-FastAPI wrapper; picking voices and language codes; blending voices; chunking long text; controlling pronunciation via misaki/espeak-ng; and judging when Kokoro is the right tool versus when its lack of voice cloning, narrow emotional range, and weaker non-English quality mean you should reach for a hosted or larger model instead. Not for voice cloning, expressive/emotional character performance, or high-fidelity multilingual work — say so and route elsewhere.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fetch up-to-date documentation for any library, framework, API, or service into context. Use when the user wants to look up API references, check function signatures or required fields, find feature-specific docs, or verify how an external tool actually works. Triggers for queries about third-party libraries like Stripe, SQLAlchemy, Tailwind, FastAPI, shadcn, Drizzle, Hono, Better Auth — any time the answer lives in official docs rather than in the project codebase. Use this instead of guessing from trained knowledge, which is stale.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when reviewing Python changes - Django, Flask and FastAPI handlers, subprocess, SQL, Jinja2, format strings, and data/ML code that loads pickles, models, notebooks or remote model code

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when a model is served online (FastAPI, BentoML) or scored in batch, when changing how a model artifact is loaded or versioned, or when adding drift, quality or prediction monitoring and a rollout plan for a new model

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for the Kimix HTTP serve system — FastAPI backend + TypeScript/Vite frontend. Use when adding new backend endpoints, modifying the session manager, changing the frontend API client, or understanding how data flows from backend to UI. Covers dummy_app.py, dummy_session_manager.py, sse_cli.py, and src/app/.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for Kimix opencode-style HTTP server (FastAPI + SSE), including route definitions, builtin endpoints, and app.post usage.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive Python/FastAPI backend code review with optional parallel agents

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Python backend development standards for FastAPI, SQLModel, pytest, Ruff, and Pyright. Use when writing or modifying backend code in src/merchant, src/payment, src/apps_sdk, agents, tests, API routes, services, models, or protocol logic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level skill for Python programming (Python 3.13/3.14+). Covers type safety, generic syntax (PEP 695), async/await TaskGroups, FastAPI 0.115+, Pydantic v2, uv package manager, Ruff, and pytest in English and Indonesian.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploys websites, web apps, APIs, and agents to the internet with a strict pre-deploy audit and a preview → health-check → prod flow. Handles plain HTML/CSS/JS pages and static sites (Astro, Hugo, Jekyll, Eleventy, Vite) as well as Next, Remix, Nuxt, SvelteKit, FastAPI, Flask, Express, MCP servers, Claude Agent SDK scripts, Python workers, and Dockerized projects. Routes to free static hosts — Cloudflare Pages, Netlify, GitHub Pages, Render — or to Vercel, Railway, Docker+SSH VPS, or a cloudflared tunnel, and can also run the project locally. Use when the user says "/all-deploy", "/deploy", "/despliega", "deploy this", "ship this", "push to prod", "get this online", "publish my website", "sube mi página web", "publicar mi página", "hosting gratis", "free hosting", "custom domain", "dominio propio", "run it locally", "corre esto localmente", or otherwise asks to publish a site or project to a URL.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.