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 agentmods add skills/sawrus/agent-guides/database-modelingnpx skills add sawrus/agent-guides --skill database-modelinggit clone --depth 1 https://github.com/sawrus/agent-guidesWhat 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 | $0.00020 | $0.01328 |
| Opus 5 | $0.00010 | $0.00664 |
| Sonnet 5 | $0.00004 | $0.00266 |
| Haiku 4.5 | $0.00002 | $0.00133 |
Grade A, and why
database-modeling 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Modeling Skill
Expertise: PostgreSQL schema design, SQLAlchemy (async), query optimization, indexing, migrations (Alembic), safe schema changes.
Schema Design Patterns
Standard column set (all tables)
from sqlalchemy import Column, Integer, DateTime, func
from sqlalchemy.orm import DeclarativeBase
class Base(DeclarativeBase):
pass
class TimestampMixin:
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), server_default=func.now(), nullable=False
)
updated_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), server_default=func.now(),
onupdate=func.now(), nullable=False
)
class Order(TimestampMixin, Base):
__tablename__ = "orders"
id: Mapped[int] = mapped_column(primary_key=True)
user_id: Mapped[int] = mapped_column(ForeignKey("users.id"), nullable=False, index=True)
status: Mapped[str] = mapped_column(String(20), nullable=False, default="pending")
total_amount: Mapped[Decimal] = mapped_column(Numeric(12, 2), nullable=False)
Soft delete pattern
class SoftDeleteMixin:
deleted_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
@property
def is_deleted(self) -> bool:
return self.deleted_at is not None
# Always filter in repository, never expose deleted records by default
class OrderRepository:
async def list_active(self, session: AsyncSession):
return await session.execute(
select(Order).where(Order.deleted_at.is_(None))
)
Indexing Strategy
-- Single column: high-cardinality columns used in WHERE/JOIN/ORDER BY
CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_orders_status ON orders(status) WHERE deleted_at IS NULL; -- partial index
-- Composite: query uses both columns together (order matters: equality first, then range)
CREATE INDEX idx_orders_user_created ON orders(user_id, created_at DESC);
-- Full-text search
CREATE INDEX idx_products_search ON products USING gin(to_tsvector('english', name || ' ' || description));
-- Never index: low-cardinality boolean columns, small tables (<1000 rows)
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 · 174 lines · 20 tokens per session scan A 59720af3fb63
database-modeling is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 12d ago), licensed MIT. It adds 20 tokens to every session and 1,328 once invoked, about $0.0001 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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