sqlalchemy-postgres
Expert guidance for SQLAlchemy 2.0 + Pydantic + PostgreSQL. Use when setting up database layers, defining models, creating migrations, or any database-related work. Automatically activated for DB tasks.
- Installs
- 500
- Last 24h
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/cfircoo/claude-code-toolkit --skill sqlalchemy-postgres- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sqlalchemy-postgres automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
<essential_principles>
SQLAlchemy 2.0 + Pydantic + PostgreSQL Best Practices
This skill provides expert guidance for building production-ready database layers.
Stack
- SQLAlchemy 2.0 with async support (asyncpg driver)
- Pydantic v2 for validation and serialization
- Alembic for migrations
- PostgreSQL only
Core Principles
1. Separation of Concerns
models/ # SQLAlchemy ORM models (database layer)
schemas/ # Pydantic schemas (API layer)
repositories/ # Data access patterns
services/ # Business logic
2. Type Safety First
Always use SQLAlchemy 2.0 style with Mapped[] type annotations:
from sqlalchemy.orm import Mapped, mapped_column
class User(Base):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str] = mapped_column(String(100))
3. Async by Default Use async engine and sessions for FastAPI:
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
engine = create_async_engine("postgresql+asyncpg://...")
4. Pydantic-SQLAlchemy Bridge Keep models and schemas separate but mappable:
# Schema reads from ORM
class UserRead(BaseModel):
model_config = ConfigDict(from_attributes=True)
5. Repository Pattern Abstract database operations for testability and clean code. </essential_principles>
<intake> What do you need help with?- Setup database layer - Initialize SQLAlchemy + Pydantic + Alembic from scratch
- Define models - Create SQLAlchemy models with Pydantic schemas
- Create migration - Generate and manage Alembic migrations
- Query patterns - Async CRUD, joins, eager loading, optimization
- Full implementation - Complete database layer for a feature </intake>
Auto-detection triggers (use this skill when user mentions):
- database, db, sqlalchemy, postgres, postgresql
- model, migration, alembic
- repository, crud, query
- async session, connection pool </routing>
<reference_index>
Domain Knowledge
| Reference | Purpose |
|---|---|
| references/best-practices.md | Production patterns, security, performance |
| references/patterns.md | Repository, Unit of Work, common queries |
| references/async-patterns.md | Async session management, FastAPI integration |
| </reference_index> |
<workflows_index>
| Workflow | Purpose |
|---|---|
| workflows/setup-database.md | Initialize complete database layer |
| workflows/define-models.md | Create models + schemas + relationships |
| workflows/create-migration.md | Alembic migration workflow |
| workflows/query-patterns.md | CRUD operations and optimization |
| </workflows_index> |
<quick_reference>
File Structure
src/
├── db/
│ ├── __init__.py
│ ├── base.py # DeclarativeBase
│ ├── session.py # Engine + async session factory
│ └── dependencies.py # FastAPI dependency
├── models/
│ ├── __init__.py
│ └── user.py # SQLAlchemy models
├── schemas/
│ ├── __init__.py
│ └── user.py # Pydantic schemas
├── repositories/
│ ├── __init__.py
│ ├── base.py # Generic repository
│ └── user.py # User repository
└── alembic/
├── alembic.ini
├── env.py
└── versions/
Essential Imports
# Models
from sqlalchemy import String, Integer, ForeignKey, DateTime
from sqlalchemy.orm import Mapped, mapped_column, relationship, DeclarativeBase
# Async
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
# Pydantic
from pydantic import BaseModel, ConfigDict, Field
Connection String
# PostgreSQL async
DATABASE_URL = "postgresql+asyncpg://user:pass@localhost:5432/dbname"
</quick_reference>
<success_criteria> Database layer is complete when:
- Async engine and session factory configured
- Base model with common fields (id, created_at, updated_at)
- Models use Mapped[] type annotations
- Pydantic schemas with from_attributes=True
- Alembic configured for async
- Repository pattern implemented
- FastAPI dependency for session injection
- Connection pooling configured for production </success_criteria>
FAQ
What does the sqlalchemy-postgres skill do?
Expert guidance for SQLAlchemy 2.0 + Pydantic + PostgreSQL. Use when setting up database layers, defining models, creating migrations, or any database-related work. Automatically activated for DB tasks.
How do I install sqlalchemy-postgres?
Run npx skills add https://github.com/cfircoo/claude-code-toolkit --skill sqlalchemy-postgres in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sqlalchemy-postgres/ (personal) or .claude/skills/sqlalchemy-postgres/ (project).
Which AI agents can use sqlalchemy-postgres?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sqlalchemy-postgres safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sqlalchemy-postgres?
It is published by cfircoo in cfircoo/claude-code-toolkit. It has 500 recorded installs.
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