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sqlalchemy-postgres — Claude Code skill: what it does, how to install

Directs agents on configuring an async database layer with SQLAlchemy 2.0, Pydantic v2, and PostgreSQL. Guides model definitions, Alembic migrations, and repository structures.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add cfircoo/claude-code-toolkit --skill sqlalchemy-postgres -a claude-code
Open on GitHub

What sqlalchemy-postgres does

This skill provides workflows for structuring an async database architecture. It activates whenever prompts mention models, migrations, CRUD, or connection pools. Core rules enforce strict separation: ORM models live in models/, validation schemas stay in schemas/, and queries sit in repositories/.

The stack targets PostgreSQL exclusively with the asyncpg driver. Alternative engines are not supported. All models require modern SQLAlchemy 2.0 style using Mapped[] and mapped_column, while Pydantic v2 schemas must include ConfigDict(from_attributes=True). Session injection setups are tailored directly for FastAPI dependencies.

What it helps with

  • initialize an async PostgreSQL layer from scratch
  • define SQLAlchemy 2.0 models with matching Pydantic v2 schemas
  • configure and run async Alembic migrations
  • implement repository patterns for clean database queries

Example prompts for the agent

  • Configure an async PostgreSQL connection pool with asyncpg and base declarative models
  • Create a User SQLAlchemy model with typed mapped columns and a Pydantic read schema
  • Generate an async Alembic migration for the newly created tables

How to install sqlalchemy-postgres

1Skills CLI (Vercel)

npx skills add cfircoo/claude-code-toolkit --skill sqlalchemy-postgres -a claude-code

2Manually: put the files in place

~/.claude/skills/sqlalchemy-postgres/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/sqlalchemy-postgres/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about sqlalchemy-postgres

How to install sqlalchemy-postgres in Claude Code?

Run npx skills add cfircoo/claude-code-toolkit --skill sqlalchemy-postgres -a claude-code in your terminal. You can also manually place the instruction file at ~/.claude/skills/sqlalchemy-postgres/SKILL.md.

Which databases are supported by sqlalchemy-postgres?

The skill only supports PostgreSQL using the asyncpg async driver. It does not provide setups for SQLite or MySQL.

What code conventions does this skill enforce?

It mandates SQLAlchemy 2.0 Mapped[] annotations, Pydantic v2 schemas with from_attributes=True, and strict directory separation between models and repositories.

Source text

SKILL.md · MIT
---
name: sqlalchemy-postgres
description: 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.
---

<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:
```python
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:
```python
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:
```python
# 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?

1. **Setup database layer** - Initialize SQLAlchemy + Pydantic + Alembic from scratch
2. **Define models** - Create SQLAlchemy models with Pydantic schemas
3. **Create migration** - Generate and manage Alembic migrations
4. **Query patterns** - Async CRUD, joins, eager loading, optimization
5. **Full implementation** - Complete database layer for a feature
</intake>

<routing>
| Response | Workflow |
|----------|----------|
| 1, "setup", "initialize", "start" | workflows/setup-database.md |
| 2, "model", "define", "create model" | workflows/define-models.md |
| 3, "migration", "alembic", "schema change" | workflows/create-migration.md |
| 4, "query", "crud", "repository" | workflows/query-patterns.md |
| 5, "full", "complete", "feature" | Run setup → define-models → create-migration |

**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
```python
# 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
```python
# 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>

© cfircoo · MIT · cfircoo/claude-code-toolkit

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