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Agent Skill · 开发

sqlalchemy(sqlalchemy 命令行)

sqlalchemy 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

sqlalchemy 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 14,593。

总安装 682周安装 29GitHub Stars 12下载 239核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sqlalchemy(sqlalchemy 命令行)
来源仓库:https://github.com/claude-dev-suite/claude-dev-suite
仓库路径:skills/sqlalchemy
安装命令:
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill sqlalchemy
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill sqlalchemy

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

集成 SQLAlchemy 命令行工具链,支持 ORM 模型操作。适用于 Python Web 项目的数据库交互开发。封装 alembic 迁移与 raw SQL 执行能力。需确保 Python 环境与依赖包版本匹配。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。sqlalchemy 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/sqlalchemy

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill sqla…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

sqlalchemy 怎么安装?

在终端执行 npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill sqlalchemy。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

sqlalchemy 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

sqlalchemy 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

sqlalchemy 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

sqlalchemy 用的人多吗?

公开数据是累计安装 682 次,近一周安装 29 次,下载 239 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

sqlalchemy 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
sqlalchemy

sqlalchemy 命令行 · 本页

68212接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

SQLAlchemy Core Knowledge

Deep Knowledge: Use mcp__documentation__fetch_docs with technology: sqlalchemy for comprehensive documentation.

When NOT to Use This Skill

  • TypeScript/Node.js Projects: Use prisma, drizzle, or typeorm skills
  • Django Applications: Use Django ORM documentation (not covered here)
  • Raw SQL Queries: Use database-query MCP server for direct SQL execution
  • NoSQL Databases: Use mongodb skill for MongoDB operations
  • FastAPI Specific: May need fastapi-expert for integration patterns
  • Database Design: Consult sql-expert or architect-expert for schema architecture
  • Other Python ORMs: Peewee, Pony ORM, Tortoise not supported by this skill

Model Definition

from sqlalchemy import Column, Integer, String, Boolean, DateTime, ForeignKey
from sqlalchemy.orm import relationship, DeclarativeBase
from datetime import datetime

class Base(DeclarativeBase):
    pass

class User(Base):
    __tablename__ = 'users'

    id = Column(Integer, primary_key=True)
    name = Column(String(100), nullable=False)
    email = Column(String(255), unique=True, nullable=False)
    is_active = Column(Boolean, default=True)
    created_at = Column(DateTime, default=datetime.utcnow)

    posts = relationship('Post', back_populates='author')

class Post(Base):
    __tablename__ = 'posts'

    id = Column(Integer, primary_key=True)
    title = Column(String(255), nullable=False)
    content = Column(String)
    author_id = Column(Integer, ForeignKey('users.id'))

    author = relationship('User', back_populates='posts')

Session Operations

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql://user:pass@localhost/db')
Session = sessionmaker(bind=engine)

# Create
with Session() as session:
    user = User(name='John', email='john@example.com')
    session.add(user)
    session.commit()

# Read
with Session() as session:
    users = session.query(User).all()
    user = session.query(User).filter_by(id=1).first()
    active_users = session.query(User).filter(User.is_active == True).all()

# Update
with Session() as session:
    user = session.query(User).filter_by(id=1).first()
    user.name = 'Jane'
    session.commit()

# Delete
with Session() as session:
    session.query(User).filter_by(id=1).delete()
    session.commit()

Async Support

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

engine = create_async_engine('postgresql+asyncpg://user:pass@localhost/db')
async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

async def get_users():
    async with async_session() as session:
        result = await session.execute(select(User))
        return result.scalars().all()

Anti-Patterns

Anti-PatternWhy It's BadBetter Approach
Not closing sessionsConnection leaks, pool exhaustionUse context managers (with Session())
Using session.query() in SQLAlchemy 2.0+Deprecated, legacy APIUse select() with session.execute()
No eager loading for relationsN+1 query problemUse selectinload() or joinedload()
Hardcoded connection stringsSecurity riskUse environment variables
Not configuring connection poolConnection issues, performanceSet pool_size, max_overflow, pool_recycle
Using session.flush() without understandingPartial commits, confusionUse commit() for transactions
No pool_pre_ping in productionStale connections after DB restartEnable pool_pre_ping=True
Missing indexes on foreign keysSlow joinsAdd Index() to frequently joined columns
Using ORM for bulk operationsVery slow for large datasetsUse bulk_insert_mappings() or Core
Not handling IntegrityErrorCryptic errors to usersCatch and provide meaningful messages

Quick Troubleshooting

IssueLikely CauseSolution
"No module named 'sqlalchemy'"SQLAlchemy not installedRun pip install sqlalchemy
"Can't connect to server"Wrong DATABASE_URL or DB downVerify connection string, check DB status
"Table doesn't exist"Migrations not runExecute alembic upgrade head
"DetachedInstanceError"Accessing relation outside sessionUse joinedload() or keep session open
"IntegrityError: duplicate key"Unique constraint violationCheck for existing record, handle error
"InvalidRequestError: SQL expression"Missing select() in 2.0 styleUse select(Model) not session.query()
Slow queriesN+1 problem, missing indexesAdd eager loading, create indexes
Pool timeoutToo many connectionsIncrease pool_size, fix connection leaks
"Stale data"Session caching old dataCall session.expire_all() or refresh objects
Alembic conflictMultiple migration headsMerge branches with alembic merge

Production Readiness

Engine Configuration

# database.py
from sqlalchemy import create_engine, event
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy.pool import QueuePool
import os

DATABASE_URL = os.environ['DATABASE_URL']

engine = create_engine(
    DATABASE_URL,
    poolclass=QueuePool,
    pool_size=20,
    max_overflow=10,
    pool_timeout=30,
    pool_recycle=1800,  # Recycle connections after 30 minutes
    pool_pre_ping=True,  # Check connection validity
    echo=os.environ.get('DEBUG') == 'true',
    connect_args={
        'sslmode': 'require' if os.environ.get('ENV') == 'production' else 'prefer',
        'connect_timeout': 10,
    }
)

# Event listeners for monitoring
@event.listens_for(engine, 'before_cursor_execute')
def before_cursor_execute(conn, cursor, statement, parameters, context, executemany):
    conn.info.setdefault('query_start_time', []).append(time.time())

@event.listens_for(engine, 'after_cursor_execute')
def after_cursor_execute(conn, cursor, statement, parameters, context, executemany):
    total = time.time() - conn.info['query_start_time'].pop()
    if total > 0.5:  # Log slow queries
        logger.warning(f'Slow query ({total:.2f}s): {statement[:100]}')

SessionLocal = sessionmaker(bind=engine)
ScopedSession = scoped_session(SessionLocal)

Context Manager Pattern

from contextlib import contextmanager
from typing import Generator

@contextmanager
def get_db() -> Generator[Session, None, None]:
    """Database session context manager with automatic cleanup."""
    session = SessionLocal()
    try:
        yield session
        session.commit()
    except Exception:
        session.rollback()
        raise
    finally:
        session.close()

# Usage
def create_user(name: str, email: str) -> User:
    with get_db() as db:
        user = User(name=name, email=email)
        db.add(user)
        db.flush()  # Get ID without committing
        return user

Async Configuration

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker

async_engine = create_async_engine(
    DATABASE_URL.replace('postgresql://', 'postgresql+asyncpg://'),
    pool_size=20,
    max_overflow=10,
    pool_recycle=1800,
    echo=False,
)

async_session = async_sessionmaker(
    async_engine,
    class_=AsyncSession,
    expire_on_commit=False,
)

async def get_async_db() -> AsyncGenerator[AsyncSession, None]:
    async with async_session() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise

Query Optimization

from sqlalchemy.orm import joinedload, selectinload
from sqlalchemy import select

# Eager loading to avoid N+1
async def get_users_with_posts(db: AsyncSession):
    stmt = (
        select(User)
        .options(selectinload(User.posts))
        .where(User.is_active == True)
    )
    result = await db.execute(stmt)
    return result.scalars().all()

# Pagination
async def paginate(
    db: AsyncSession,
    page: int,
    per_page: int
) -> dict:
    offset = (page - 1) * per_page

    # Count
    count_stmt = select(func.count(User.id))
    total = await db.scalar(count_stmt)

    # Data
    stmt = (
        select(User)
        .order_by(User.created_at.desc())
        .offset(offset)
        .limit(per_page)
    )
    result = await db.execute(stmt)

    return {
        'data': result.scalars().all(),
        'total': total,
        'page': page,
        'pages': math.ceil(total / per_page),
    }

# Bulk insert
def bulk_insert_users(db: Session, users_data: list[dict]):
    db.execute(insert(User), users_data)
    db.commit()

Alembic Migrations

# alembic/env.py
from sqlalchemy import engine_from_config, pool
from alembic import context
from models import Base

target_metadata = Base.metadata

def run_migrations_online():
    connectable = engine_from_config(
        config.get_section(config.config_ini_section),
        prefix='sqlalchemy.',
        poolclass=pool.NullPool,
    )

    with connectable.connect() as connection:
        context.configure(
            connection=connection,
            target_metadata=target_metadata,
            compare_type=True,
            compare_server_default=True,
        )

        with context.begin_transaction():
            context.run_migrations()

# CLI commands
# alembic revision --autogenerate -m "Add users table"
# alembic upgrade head
# alembic downgrade -1

Testing

# conftest.py
import pytest
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

@pytest.fixture(scope='session')
def engine():
    return create_engine(os.environ['TEST_DATABASE_URL'])

@pytest.fixture(scope='session')
def tables(engine):
    Base.metadata.create_all(engine)
    yield
    Base.metadata.drop_all(engine)

@pytest.fixture
def db(engine, tables):
    connection = engine.connect()
    transaction = connection.begin()
    Session = sessionmaker(bind=connection)
    session = Session()

    yield session

    session.close()
    transaction.rollback()
    connection.close()

# tests/test_user.py
def test_create_user(db):
    user = User(name='Test', email='test@example.com')
    db.add(user)
    db.flush()
    assert user.id is not None

Monitoring Metrics

MetricTarget
Query time (p99)< 100ms
Pool connections< max_size
Slow queries0 (> 500ms)
Connection errors0

Checklist

  • Connection pooling configured
  • SSL in production
  • pool_pre_ping enabled
  • pool_recycle for long-running apps
  • Context manager for sessions
  • Eager loading to prevent N+1
  • Pagination for list queries
  • Alembic migrations
  • Slow query logging
  • Test isolation with rollback

Reference Documentation

平台分布

Codex

36.18%
按下载量换算86

Claude

29.45%
按下载量换算70

Cursor

20.66%
按下载量换算49

Gemini CLI

10.27%
按下载量换算25

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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