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 Jartan-LLC/grimoire --skill pydanticgit clone --depth 1 https://github.com/Jartan-LLC/grimoireWrote 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/jartan-llc/grimoire/pydantic)<a href="https://agentmods.dev/skills/jartan-llc/grimoire/pydantic"><img src="https://agentmods.dev/badge/skills/jartan-llc/grimoire/pydantic/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/skills/jartan-llc/grimoire/pydantic"><img src="https://agentmods.dev/badge/skills/jartan-llc/grimoire/pydantic.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 | $0.00025 | $0.07180 |
| Opus 5.5 | $0.00010 | $0.02872 |
| Sonnet 5.5 | $0.00005 | $0.01436 |
| Haiku 4.5 | $0.00003 | $0.00718 |
Grade A, and why
pydantic 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.
How it starts
The opening of the file, as written. The whole thing — 1,253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pydantic Validation Skill
Use it with python-configuration for how an application loads its configuration and secrets, python-error-handling for exception design around ValidationError, python-type-safety for protocols, generics and type narrowing, and python-testing for testing models.
Quick Start
from pydantic import BaseModel, Field, EmailStr
from datetime import datetime
class User(BaseModel):
id: int
name: str = Field(..., min_length=1, max_length=100)
email: EmailStr
created_at: datetime = Field(default_factory=datetime.now)
is_active: bool = True
# Validate data
user = User(id=1, name="Alice", email="[email protected]")
print(user.model_dump()) # {'id': 1, 'name': 'Alice', ...}
# Automatic type coercion
user2 = User(id="2", name="Bob", email="[email protected]")
assert user2.id == 2 # String "2" coerced to int
# Validation error
try:
User(id=3, name="", email="invalid")
except ValidationError as e:
print(e.errors())
Core Concepts
BaseModel Foundation
from pydantic import BaseModel, ConfigDict
class Product(BaseModel):
model_config = ConfigDict(
str_strip_whitespace=True,
validate_assignment=True,
use_enum_values=True,
arbitrary_types_allowed=False
)
name: str
price: float
quantity: int = 0
# Usage
product = Product(name=" Widget ", price=19.99)
assert product.name == "Widget" # Whitespace stripped
# Validate on assignment
product.price = "29.99" # Auto-converts to float
Field Configuration
from pydantic import Field, field_validator
from typing import Annotated
class Item(BaseModel):
# Field constraints
sku: str = Field(pattern=r'^[A-Z]{3}-\d{4}$')
price: float = Field(gt=0, le=10000)
stock: int = Field(ge=0, default=0)
# Annotated types (Pydantic v2)
quantity: Annotated[int, Field(ge=1, le=100)]
# Descriptions and examples
description: str = Field(
...,
description="Product description",
examples=["High-quality widget"]
)
# Deprecated fields
old_field: str | None = Field(None, deprecated=True)
@field_validator('sku')
@classmethod
def validate_sku(cls, v: str) -> str:
if not v.startswith('ABC'):
raise ValueError('SKU must start with ABC')
return v
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.
- 2d ago Changed · -11 lines 891736d2099c
- 1mo ago First seen · 1,264 lines · 25 tokens per session scan A 95bc97566acc
pydantic is a skill published in the GitHub repository Jartan-LLC/grimoire (2 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 7,180 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
django-expert
Use when building Django web applications or REST APIs with Django REST Framework. Invoke when working with settings.py, models.py, manage.py, or any Django project file. Creates Django models with proper indexes, optimizes ORM queries using selectrelated/prefetchrelated, builds DRF serializers and viewsets, and…
fastapi-expert
Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms…
beautifulsoup-parsing
Expert guidance for HTML/XML parsing using BeautifulSoup in Python with best practices for DOM navigation, data extraction, and efficient scraping workflows.
odoo-development
Expert guidance for Odoo ERP development including Python ORM, XML views, and module architecture.
rest-api-django
Comprehensive guidelines for Django REST API development covering project structure, views, models, serializers, authentication, performance, and error handling.
flask-python
Guidelines for Flask Python development with best practices for blueprints, RESTful APIs, and application factories.