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.
git clone --depth 1 https://github.com/cfircoo/claude-code-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/cfircoo/claude-code-toolkit/db-expert)<a href="https://agentmods.dev/agents/cfircoo/claude-code-toolkit/db-expert"><img src="https://agentmods.dev/badge/agents/cfircoo/claude-code-toolkit/db-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/cfircoo/claude-code-toolkit/db-expert"><img src="https://agentmods.dev/badge/agents/cfircoo/claude-code-toolkit/db-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.01038 |
| Opus 5.5 | $0.00020 | $0.00415 |
| Sonnet 5.5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
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 1mo 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.
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:
- List files to be created/modified
- Show complete code for each file
- Include necessary imports
- Provide migration command if schema changes
- 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>
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.
- 1mo ago First seen · 126 lines · 50 tokens per session scan A ee19dc306f65
db-expert is an agent published in the GitHub repository cfircoo/claude-code-toolkit (17 stars, last pushed 6mo ago), licensed MIT. It adds 50 tokens to every session and 1,038 once invoked, about $0.0002 per session on Opus 5.5. 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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