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.
npx skills add 0xDarkMatter/claude-mods --skill python-database-opsgit clone --depth 1 https://github.com/0xDarkMatter/claude-modsWrote 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/skills/0xdarkmatter/claude-mods/python-database-ops)<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/python-database-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/python-database-ops.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.01209 |
| Opus 5 | $0.00022 | $0.00605 |
| Sonnet 5 | $0.00009 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
python-database-ops 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Database Patterns
SQLAlchemy 2.0 and database best practices.
SQLAlchemy 2.0 Basics
from sqlalchemy import create_engine, select
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, Session
class Base(DeclarativeBase):
pass
class User(Base):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str] = mapped_column(String(100))
email: Mapped[str] = mapped_column(String(255), unique=True)
is_active: Mapped[bool] = mapped_column(default=True)
# Create engine and tables
engine = create_engine("postgresql://user:pass@localhost/db")
Base.metadata.create_all(engine)
# Query with 2.0 style
with Session(engine) as session:
stmt = select(User).where(User.is_active == True)
users = session.execute(stmt).scalars().all()
Async SQLAlchemy
from sqlalchemy.ext.asyncio import (
AsyncSession,
async_sessionmaker,
create_async_engine,
)
from sqlalchemy import select
# Async engine
engine = create_async_engine(
"postgresql+asyncpg://user:pass@localhost/db",
echo=False,
pool_size=5,
max_overflow=10,
)
# Session factory
async_session = async_sessionmaker(engine, expire_on_commit=False)
# Usage
async with async_session() as session:
result = await session.execute(select(User).where(User.id == 1))
user = result.scalar_one_or_none()
Model Relationships
from sqlalchemy import ForeignKey
from sqlalchemy.orm import relationship, Mapped, mapped_column
class User(Base):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str]
# One-to-many
posts: Mapped[list["Post"]] = relationship(back_populates="author")
class Post(Base):
__tablename__ = "posts"
id: Mapped[int] = mapped_column(primary_key=True)
title: Mapped[str]
author_id: Mapped[int] = mapped_column(ForeignKey("users.id"))
# Many-to-one
author: Mapped["User"] = relationship(back_populates="posts")
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 188 lines · 43 tokens per session scan A 72ee9dc2f41b
python-database-ops is a skill published in the GitHub repository 0xDarkMatter/claude-mods (32 stars, last pushed 14d ago), licensed MIT. It adds 43 tokens to every session and 1,209 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
psycopg
PostgreSQL adapter for Python - customer support tech enablement for database operations, query optimization, and data management.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
python-logging-best-practices
Python logging with loguru, structlog, and orjson. TRIGGERS - loguru, structlog, structured logging.
python-memory-safe-scripts
Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloctrim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU…
pypi-doppler
LOCAL-ONLY PyPI publishing with Doppler credentials. TRIGGERS - publish to PyPI, pypi upload, local publish. NEVER use in CI/CD.
python-workspace
Python workspace for MQL5 integration. TRIGGERS - MetaTrader 5 Python, mt5 package, MQL5-Python setup.