db-expert

A database coding guide for Python projects using SQLAlchemy 2.0, Pydantic, and PostgreSQL. It covers models, data validation schemas, database changes, and repository code.

In plain words
What is it for?
Use it when setting up database layers, defining models and schemas, writing migrations, or implementing repositories that read and change stored data.
Why use it?
It gives database-related code a consistent structure and set of patterns, reducing the need to decide how each layer should be organised.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/cfircoo/claude-code-toolkit/db-expert
Clone the repo
git clone --depth 1 https://github.com/cfircoo/claude-code-toolkit
Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,038 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.01038
Opus 5 $0.00025 $0.00519
Sonnet 5 $0.00010 $0.00208
Haiku 4.5 $0.00005 $0.00104

Measured 2d ago against content hash ee19dc306f65, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

db-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/db-expert.md · 126 lines

What it actually says

<file_structure> Follow this structure when creating database layers:

src/
├── db/
│   ├── __init__.py          # Export Base, session, dependencies
│   ├── base.py              # DeclarativeBase + TimestampMixin
│   ├── session.py           # Async engine + session factory
│   ├── config.py            # DatabaseSettings (pydantic-settings)
│   └── dependencies.py      # FastAPI get_db dependency
├── models/
│   ├── __init__.py          # Export all models
│   └── {entity}.py          # SQLAlchemy models
├── schemas/
│   ├── __init__.py          # Export all schemas
│   └── {entity}.py          # Pydantic schemas (Create, Read, Update)
├── repositories/
│   ├── __init__.py
│   ├── base.py              # Generic BaseRepository
│   └── {entity}.py          # Entity-specific repository
└── alembic/
    ├── alembic.ini
    ├── env.py               # Async-configured
    └── versions/

</file_structure>

<code_patterns> Model Definition:

from sqlalchemy.orm import Mapped, mapped_column
from db.base import Base, TimestampMixin

class User(Base, TimestampMixin):
    __tablename__ = "users"
    id: Mapped[int] = mapped_column(primary_key=True)
    email: Mapped[str] = mapped_column(String(255), unique=True, index=True)

Pydantic Schema:

from pydantic import BaseModel, ConfigDict

class UserRead(BaseModel):
    model_config = ConfigDict(from_attributes=True)
    id: int
    email: str

Async Session:

from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker

engine = create_async_engine("postgresql+asyncpg://...", pool_pre_ping=True)
async_session_factory = async_sessionmaker(engine, expire_on_commit=False)

Eager Loading:

from sqlalchemy.orm import selectinload
stmt = select(User).options(selectinload(User.posts))

</code_patterns>

<output_format> When implementing database features:

  1. List files to be created/modified
  2. Show complete code for each file
  3. Include necessary imports
  4. Provide migration command if schema changes
  5. Show example usage in FastAPI route </output_format>

<success_criteria> Implementation is complete when:

  • All models use Mapped[] type annotations
  • Pydantic schemas have from_attributes=True
  • Async session factory is properly configured
  • Repository pattern abstracts data access
  • FastAPI dependency injection is set up
  • Alembic can generate migrations
  • No lazy loading in async context </success_criteria>
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 126 lines · 50 tokens per session scan A ee19dc306f65

Subscribe to this mod's changes

db-expert is an agent published in the GitHub repository cfircoo/claude-code-toolkit (17 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 1,038 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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