Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Python Development Engine

    1

    Python project routing for Web API/crawler/automation/data/CLI. FastAPI/Django/Flask selection, async patterns, deployment strategies.

    Free

    6 installsSecurity scanned

    Python Development Engine

    Example session with this skill installed

    Building a REST API that scrapes 10K product pages daily, processes data, and serves results. Should I use FastAPI or Django? Async or Celery?

    • Read your context and instructions
    • Compiled the python development engine

    Tech Stack Decision: Scraping + API Service

    Project Profile

    • Type: Data pipeline + Web API
    • Scale: 10K pages/day (~7/minute avg, 50/minute peak)
    • Output: REST API serving processed results

    Framework: FastAPI ✅

    FactorFastAPIDjango
    Async supportNativeRequires ASGI + DRF
    Scraping integrationDirect asyncioNeeds Celery bridge
    Performance2-3x faster for I/O-heavyOverhead for ORM
    Setup speed1 day2-3 days

    Concurrency: asyncio + Background Tasks ✅

    • 10K pages/day doesn't need Celery complexity
    • FastAPI BackgroundTasks for scheduled scraping
    • asyncio + aiohttp for concurrent page fetching
    • Only add Celery if you need: retries, monitoring, multi-worker queues

    Architecture

    FastAPI app
    ├── /api/* (endpoints)
    ├── BackgroundTasks (daily scrape scheduler)
    ├── Scraper module (asyncio + aiohttp)
    ├── Processing pipeline (Pandas/Polars)
    └── SQLite → PostgreSQL (when concurrent users > 50)
    

    Don't Over-Engineer

    Start with: FastAPI + asyncio + SQLite. Add Celery/Redis/PostgreSQL only when you hit actual bottlenecks.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Python开发决策引擎

    专家级决策系统——不是教Python语法,而是在框架选型、并发模型、爬虫架构、部署策略这些关键岔路做对选择。


    目录

    1. 项目类型路由器
    2. 框架决策树
    3. 异步方案决策
    4. 爬虫架构决策
    5. 数据工具链
    6. CLI工具开发
    7. 部署决策
    8. 2026年趋势
    9. 质量门控三级清单
    10. 10个常见错误模式
    11. 执行流程

    1. 项目类型路由器

    输入: 项目需求 → 输出: 框架+并发模型+部署方案+数据库选择
    
    决策流程:
      │
      ├─ 是Web API/微服务吗?
      │   ├─ Yes → FastAPI + asyncio + Docker + PostgreSQL
      │   └─ No
      │
      ├─ 需要Admin后台/用户系统/认证吗?
      │   ├─ Yes → Django 5.x + WSGI/ASGI + Docker + MySQL/PostgreSQL
      │   └─ No
      │
      ├─ 是爬虫项目吗?
      │   ├─ 静态页面 + 大规模 → Scrapy + asyncio + Redis队列 + 代理池
      │   ├─ 动态页面/JS渲染 → Playwright + asyncio + IP轮换
      │   └─ 快速原型 → requests + BeautifulSoup
      │
      ├─ 是数据处理/ETL吗?
      │   ├─ <1GB数据 → Polars + Pandas
      │   ├─ 1-10GB数据 → Polars + DuckDB
      │   └─ >10GB数据 → DuckDB + 分区 + 流式处理
      │
      ├─ 是自动化脚本吗?
      │   └─ Typer/Click + subprocess + cron + 日志
      │
      ├─ 是CLI工具吗?
      │   ├─ 分发简单 → Typer + Rich + PyInstaller
      │   └─ 性能敏感 → Typer + Rich + Nuitka编译
      │
      └─ 是AI/ML推理服务吗?
          └─ FastAPI + ONNX Runtime + Docker GPU + 批处理
    

    1.1 Web API完整决策矩阵

    | 需求维度 | 轻量API | 中型API | 企业级API | 性能敏感型 | |----------|---------|---------|-----------|-----------| | 推荐框架 | FastAPI | FastAPI | FastAPI/Django | FastAPI + uvicorn | | ORM | SQLAlchemy | SQLAlchemy | SQLAlchemy/Django ORM | asyncpg + SQLAlchemy | | 缓存 | 内存Cache | Redis | Redis + 分布式 | Redis Cluster | | 认证 | API Key | JWT | OAuth2 + SSO | JWT + Refresh Token | | 文档 | OpenAPI自动 | OpenAPI自动 | OpenAPI + Swagger | OpenAPI + ReDoc | | 测试 | pytest + httpx | pytest + coverage | pytest + CI | pytest + loadtest | | 部署 | Docker单实例 | Docker + Load Balancer | K8s + HPA | K8s + HPA + 监控 | | 数据库 | SQLite/PostgreSQL | PostgreSQL | PostgreSQL + 读写分离 | PostgreSQL + 连接池 | | 日日夜夜 | 日志 | 结构化日志 | 结构化日志 + ELK | 结构化日志 + 链路追踪 |

    1.2 爬虫项目决策矩阵

    | 维度 | 快速原型 | 中等规模 | 大规模分布式 | 企业级 | |------|---------|---------|-------------|--------| | 爬取量/天 | <1000 | 1000-10万 | 10万-1000万 | >1000万 | | 页面类型 | 静态 | 静态+简单JS | 混合 | 混合+反爬 | | 技术栈 | requests+BS4 | Scrapy | Scrapy+Redis | Playwright集群 | | 存储 | CSV/JSON | PostgreSQL | PostgreSQL+MongoDB | 数据湖 | | 代理需求 | 无 | 少量代理 | 代理池 | 代理池+轮换策略 | | 失败重试 | 简单重试 | 指数退避 | 队列重试 | 智能重试 | | 调度 | 单机 | 单机+多线程 | Redis队列 | 分布式调度 | | 监控 | 手动 | 日志 | Prometheus | Grafana看板 |

    1.3 数据处理项目决策矩阵

    | 数据规模 | <100MB | 100MB-1GB | 1GB-10GB | >10GB | |----------|--------|-----------|---------|-------| | 推荐工具 | Pandas | Polars | Polars + DuckDB | DuckDB | | 处理模式 | 全量加载 | 全量/分块 | 分块/流式 | 流式/分区 | | 并行处理 | 单线程 | 单/多线程 | 多进程 | 多进程+分布式 | | 内存需求 | <2GB | 2-8GB | 8-32GB | 可溢出磁盘 | | SQL支持 | 无 | 有限 | 良好 | 完整SQL | | 生态集成 | 丰富 | 成长中 | 良好 | 良好 | | 学习曲线 | 低 | 中 | 中 | 中高 |

    1.4 CLI工具决策矩阵

    | 场景 | 简单脚本 | 内部工具 | 分发工具 | 企业级CLI | |------|---------|---------|---------|-----------| | 用户 | 开发者自己 | 团队成员 | 外部用户 | 客户/运维 | | 框架 | argparse | Typer/Click | Typer + Rich | Typer + Rich + 测试 | | 输出美化 | print | Rich | Rich | Rich + 进度条 | | 打包 | 直接运行 | PyInstaller | PyInstaller/nuitka | pip install / conda | | 自动补全 | 无 | bash/zsh | fish/zsh | 全平台 | | 文档 | README | README + --help | Sphinx/MkDocs | 完整文档站 | | 版本管理 | 无 | 简单 | semver | click-replace | | 错误处理 | sys.exit | Rich traceback | Rich traceback | 国际化错误 |

    1.5 技术栈推荐速查表

    # Web API 技术栈推荐
    WEB_API_STACK = {
        "fast": {
            "framework": "FastAPI",
            "orm": "SQLAlchemy[asyncio] + asyncpg",
            "cache": "Redis",
            "auth": "python-jose + passlib",
            "docs": "OpenAPI (auto)",
            "test": "pytest + httpx + pytest-asyncio",
            "deploy": "uvicorn (ASGI)"
        },
        "full": {
            "framework": "Django 5.x + Django REST Framework",
            "orm": "Django ORM",
            "cache": "Redis + django-redis",
            "auth": "django-allauth / JWT",
            "docs": "drf-spectacular (OpenAPI)",
            "test": "pytest-django",
            "deploy": "gunicorn (WSGI)"
        },
        "light": {
            "framework": "Flask",
            "orm": "SQLAlchemy",
            "cache": "Flask-Caching",
            "auth": "Flask-JWT-Extended",
            "docs": "flask-restx",
            "test": "pytest + Flask-Testing",
            "deploy": "gunicorn"
        }
    }
    
    # 爬虫技术栈推荐
    CRAWLER_STACK = {
        "simple": {
            "framework": "requests + BeautifulSoup",
            "parser": "lxml",
            "storage": "CSV/JSON",
            "concurrent": "concurrent.futures"
        },
        "medium": {
            "framework": "Scrapy",
            "parser": "parsel (内置)",
            "storage": "PostgreSQL/MongoDB",
            "concurrent": "asyncio + scrapy-asyncio"
        },
        "complex": {
            "framework": "Playwright + Scrapy",
            "parser": "parsel + JS执行",
            "storage": "PostgreSQL + S3",
            "concurrent": "asyncio + 进程池",
            "proxy": "代理池 + 轮换",
            "anti_detection": "stealth插件"
        }
    }
    
    # 数据处理技术栈推荐
    DATA_STACK = {
        "small": {
            "main": "pandas",
         
    

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

      Free skills download straight away. Paid skills unlock right after purchase.

    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    6 installs

    Downloaded by developers to date

    Free forever

    No account required to browse

    Trust & safety

    Security scanned

    Verified clean 3 months ago

    • Free to download with an account

    Listed3 months ago

    Frequently Asked Questions