Agent skill

Pydantic

by kid-sid in kid-sid/claude-spellbook

A skill your agent uses when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring…

MITAuto-check: notesDevOps & Cloud

Install Pydantic

skills CLI
$ npx skills add kid-sid/claude-spellbook --skill pydantic -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install kid-sid/claude-spellbook pydantic --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/kid-sid/claude-spellbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pydantic .claude/skills/pydantic && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pydantic
GitHub stars
189
Token cost
~3k tokens
SKILL.md length
415 words
Files
1
Skills in repo
52
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring…

  • Defining request/response schemas
  • SKILL.md covers When to Activate, BaseModel Basics, Field Constraints and Validators, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Writing custom validators

What it does

Pydantic is an agent skill from kid-sid/claude-spellbook. Use when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring app settings from environment variables with pydantic-settings.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Secrets management. It works with Pydantic. The repository describes itself as: A curated collection of skills, prompts, and workflows that extend Claude's capabilities — your personal grimoire for AI-powered development. The licence is MIT.

When your agent uses it

  • Defining request/response schemas
  • Writing custom validators
  • Controlling serialization for PATCH endpoints
  • Validating non-model data with TypeAdapter

Example prompts

  • “/pydantic”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit a7c2ac9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pydantic loads about 3k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 415 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:315
    env_file=".env",

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from kid-sid/claude-spellbook at commit a7c2ac9, republished under its MIT licence (© kid-sid). 415 words, ~3,042 tokens.

Download SKILL.mdSave it as .claude/skills/pydantic/SKILL.md (or your agent's skills folder).
name
pydantic
description
Use when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring app settings from environment variables with pydantic-settings.

Pydantic v2 Patterns

Validation, serialization, and settings management with Pydantic v2.

When to Activate

  • Defining request/response schemas or domain models
  • Writing @field_validator or @model_validator for custom validation
  • Using Annotated to build reusable constrained types
  • Controlling serialization with model_dump() / model_dump_json()
  • Building generic models or discriminated unions
  • Validating arbitrary data (not a model) with TypeAdapter
  • Configuring app settings from environment variables with pydantic-settings

BaseModel Basics

python
from pydantic import BaseModel, Field
from datetime import datetime
from uuid import UUID

class User(BaseModel):
    id: UUID
    name: str
    email: str
    age: int = Field(ge=0, le=150)
    role: str = "user"          # default value
    created_at: datetime | None = None

# Instantiate
user = User(id="a1b2...", name="Alice", email="alice@example.com", age=30)

# Access
user.name        # "Alice"
user.model_fields  # dict of FieldInfo

# Validate from dict / JSON
user = User.model_validate({"id": "...", "name": "Alice", ...})
user = User.model_validate_json('{"id": "...", "name": "Alice", ...}')

Field Constraints

python
from pydantic import BaseModel, Field
from typing import Annotated

class Product(BaseModel):
    name: str = Field(min_length=1, max_length=200, strip_whitespace=True)
    price: float = Field(gt=0, description="Price in USD")
    discount: float = Field(ge=0, le=1, default=0.0)    # 0–100%
    tags: list[str] = Field(default_factory=list, max_length=10)
    sku: str = Field(pattern=r"^[A-Z]{3}-\d{6}$")
    metadata: dict = Field(default_factory=dict)

    # Alias — accept "product_name" in input, use "name" in Python
    name: str = Field(alias="product_name")
Reusable constrained types with Annotated
python
from typing import Annotated
from pydantic import Field

# Define once, reuse everywhere
PositiveInt   = Annotated[int,   Field(gt=0)]
Percentage    = Annotated[float, Field(ge=0.0, le=1.0)]
NonEmptyStr   = Annotated[str,   Field(min_length=1, strip_whitespace=True)]
EmailStr      = Annotated[str,   Field(pattern=r"^[^@]+@[^@]+\.[^@]+$")]
UserId        = Annotated[str,   Field(min_length=36, max_length=36)]

class CreateUserRequest(BaseModel):
    name: NonEmptyStr
    email: EmailStr
    age: PositiveInt
    discount: Percentage = 0.0

Validators

@field_validator — validate / transform a single field
python
from pydantic import BaseModel, field_validator

class User(BaseModel):
    name: str
    email: str
    role: str

    @field_validator("email")
    @classmethod
    def lowercase_email(cls, v: str) -> str:
        return v.strip().lower()

    @field_validator("role")
    @classmethod
    def valid_role(cls, v: str) -> str:
        allowed = {"admin", "user", "viewer"}
        if v not in allowed:
            raise ValueError(f"role must be one of {allowed}")
        return v

    # Validate multiple fields at once
    @field_validator("name", "email", mode="before")  # runs before type coercion
    @classmethod
    def strip_strings(cls, v: str) -> str:
        return v.strip() if isinstance(v, str) else v

mode="before" runs before type coercion. mode="after" (default) runs after.

@model_validator — validate across multiple fields
python
from pydantic import BaseModel, model_validator

class DateRange(BaseModel):
    start_date: datetime
    end_date: datetime
    max_days: int = 90

    @model_validator(mode="after")
    def check_date_range(self) -> "DateRange":
        if self.end_date <= self.start_date:
            raise ValueError("end_date must be after start_date")
        delta = (self.end_date - self.start_date).days
        if delta > self.max_days:
            raise ValueError(f"Range cannot exceed {self.max_days} days")
        return self

class PasswordReset(BaseModel):
    password: str
    confirm_password: str

    @model_validator(mode="after")
    def passwords_match(self) -> "PasswordReset":
        if self.password != self.confirm_password:
            raise ValueError("Passwords do not match")
        return self

# mode="before" — receives raw dict, before field validation
    @model_validator(mode="before")
    @classmethod
    def handle_legacy_format(cls, data: dict) -> dict:
        if "user_name" in data:
            data["name"] = data.pop("user_name")   # rename legacy field
        return data

ConfigDict

python
from pydantic import BaseModel, ConfigDict

class UserResponse(BaseModel):
    model_config = ConfigDict(
        from_attributes=True,       # allow ORM model → Pydantic (was orm_mode in v1)
        populate_by_name=True,      # accept both alias and field name
        str_strip_whitespace=True,  # strip whitespace from all str fields
        str_to_lower=False,
        extra="forbid",             # reject unknown fields (good for request schemas)
        # extra="ignore"            # silently drop unknown fields
        # extra="allow"             # keep unknown fields in __pydantic_extra__
        frozen=True,                # immutable instances (hashable)
        arbitrary_types_allowed=True,  # allow non-Pydantic types
        json_schema_extra={"example": {"name": "Alice", "email": "alice@example.com"}},
    )

Serialization

python
user = User(id=uuid4(), name="Alice", email="alice@example.com", role="admin")

# To dict
user.model_dump()
user.model_dump(exclude={"password", "internal_id"})
user.model_dump(include={"id", "name", "email"})
user.model_dump(exclude_none=True)      # omit None values
user.model_dump(exclude_unset=True)     # omit fields not explicitly set (useful for PATCH)
user.model_dump(by_alias=True)          # use field aliases as keys
user.model_dump(mode="json")            # serialize to JSON-compatible types (UUID → str)

# To JSON string
user.model_dump_json()
user.model_dump_json(indent=2, exclude_none=True)

# From ORM (with from_attributes=True)
orm_user = db.query(UserORM).first()
user = UserResponse.model_validate(orm_user)

# Copy with overrides
updated = user.model_copy(update={"role": "admin"})

Discriminated Unions

python
from pydantic import BaseModel
from typing import Literal, Union, Annotated
from pydantic import Field

class CreditCard(BaseModel):
    type: Literal["credit_card"]
    number: str
    expiry: str
    cvv: str

class BankTransfer(BaseModel):
    type: Literal["bank_transfer"]
    account_number: str
    routing_number: str

class Crypto(BaseModel):
    type: Literal["crypto"]
    wallet_address: str
    currency: str

PaymentMethod = Annotated[
    Union[CreditCard, BankTransfer, Crypto],
    Field(discriminator="type"),   # Pydantic uses "type" to pick the right model
]

class Order(BaseModel):
    id: str
    payment: PaymentMethod

# Pydantic automatically picks the right union member
order = Order.model_validate({
    "id": "o-123",
    "payment": {"type": "credit_card", "number": "4111...", "expiry": "12/26", "cvv": "123"},
})
isinstance(order.payment, CreditCard)  # True

Generic Models

python
from pydantic import BaseModel
from typing import TypeVar, Generic

T = TypeVar("T")

class Page(BaseModel, Generic[T]):
    items: list[T]
    total: int
    page: int
    page_size: int
    has_next: bool

class ApiResponse(BaseModel, Generic[T]):
    data: T
    status: int = 200
    message: str = "ok"

# Concrete usage — fully typed
users_page: Page[User] = Page[User](items=[...], total=100, page=1, page_size=20, has_next=True)
response: ApiResponse[User] = ApiResponse[User](data=user)

TypeAdapter — validate without a model

python
from pydantic import TypeAdapter

# Validate a plain type or complex type
ta = TypeAdapter(list[int])
ta.validate_python([1, 2, "3"])   # [1, 2, 3] — coerces "3" to 3
ta.validate_json("[1, 2, 3]")

# Validate arbitrary dict shape
ta = TypeAdapter(dict[str, list[int]])
ta.validate_python({"a": [1, 2], "b": [3]})

# Great for validating webhook payloads, external API responses
StrippedStr = Annotated[str, Field(strip_whitespace=True, min_length=1)]
ta = TypeAdapter(StrippedStr)
ta.validate_python("  hello  ")   # "hello"

pydantic-settings

python
from pydantic_settings import BaseSettings, SettingsConfigDict
from pydantic import Field
from functools import lru_cache

class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=".env",
        env_file_encoding="utf-8",
        case_sensitive=False,
        extra="ignore",
    )

    # Required — raises if missing from env
    database_url: str
    secret_key: str

    # Optional with defaults
    environment: str = "development"
    debug: bool = False
    redis_url: str = "redis://localhost:6379"
    allowed_origins: list[str] = ["http://localhost:3000"]

    # Nested prefix: reads TEMPORAL__ADDRESS from env
    temporal_address: str = Field("localhost:7233", alias="TEMPORAL_ADDRESS")

    @property
    def is_production(self) -> bool:
        return self.environment == "production"


@lru_cache
def get_settings() -> Settings:
    return Settings()

settings = get_settings()   # cached singleton

Env var names match field names case-insensitively. list[str] reads from ALLOWED_ORIGINS=http://a.com,http://b.com (comma-separated).


Red Flags

  • Sharing API schemas with the domain layer — using the same Pydantic model as both the HTTP request schema and the internal domain entity couples the API contract to business logic; changes to the API surface silently affect domain behavior and vice versa
  • Mutable field defaults without default_factory — tags: list[str] = [] shares the same list object across all instances; use tags: list[str] = Field(default_factory=list) for any mutable default
  • Not using model_dump(exclude_unset=True) for PATCH — model_dump() on a partial-update model includes all fields set to their defaults, overwriting database values the client never sent; exclude_unset=True returns only the fields the caller explicitly provided
  • orm_mode = True (v1 syntax) in a v2 project — the v1 config key is silently ignored in Pydantic v2; use model_config = ConfigDict(from_attributes=True) instead
  • Catching bare Exception from model_validate — validation errors from Pydantic are ValidationError, not ValueError or Exception; catching the wrong type means bad input crashes the caller with an unhandled exception instead of a structured error response
  • model_dump() when JSON-safe types are needed — model_dump() returns Python objects (UUID, datetime, Decimal) that are not JSON-serializable; use model_dump(mode="json") or model_dump_json() when the result will be serialized to JSON or stored as a dict in MongoDB
  • Repeating Field(gt=0) on every model instead of Annotated types — duplicating constraints is error-prone and hard to update; define PositiveInt = Annotated[int, Field(gt=0)] once and reuse it everywhere
Show full SKILL.md (81 more words)Show less

Checklist

  • Annotated used to define reusable constrained types (not repeating Field() everywhere)
  • @field_validator with mode="before" for input normalization (strip, lowercase)
  • @model_validator for cross-field validation (date ranges, password confirm)
  • from_attributes=True in ConfigDict for ORM → schema conversion
  • extra="forbid" on request schemas to reject unknown input
  • model_dump(exclude_unset=True) for PATCH endpoints (only update what was sent)
  • model_dump(mode="json") when serializing UUIDs/datetimes to dicts
  • TypeAdapter for validating non-model types (lists, dicts, scalars)
  • pydantic-settings for all environment variable config (not raw os.environ)
  • @lru_cache on get_settings() — load once, reuse

© kid-sid, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/pydantic of kid-sid/claude-spellbook.

Open the folder on GitHubat commit a7c2ac9

Compare with similar skills

Pydantic next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Pydantic compared with similar skills
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Specx Settingsmaksimzayats/specx202—~707Automated safety check: NotesMIT
Env Configaiskillstore/marketplace430—~2.3kAutomated safety check: NotesNone
Iron Proxy Gateway for NanoClawnanocoai/nanoclaw31k—~4.6kAutomated safety check: NotesMIT
LangBot Deployment Guidelangbot-app/LangBot18k—~1.2kAutomated safety check: NotesApache-2.0

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Works with

Categories

Questions about Pydantic

What does Pydantic do?

A skill your agent uses when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring…. Pydantic is an agent skill from kid-sid/claude-spellbook. Use when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring app settings from environment variables with pydantic-settings.

When should I use Pydantic?

Pydantic fits situations like: defining request/response schemas; writing custom validators; controlling serialization for PATCH endpoints; validating non-model data with TypeAdapter.

How do I install Pydantic in Claude Code?

Run `npx skills add kid-sid/claude-spellbook --skill pydantic -a claude-code`. Or copy the skill folder (skills/pydantic in kid-sid/claude-spellbook) into .claude/skills/pydantic in your project. Claude Code loads it when a task matches its description.

How do I install Pydantic in Codex?

Run `npx skills add kid-sid/claude-spellbook --skill pydantic -a codex`. Or copy the skill folder (skills/pydantic in kid-sid/claude-spellbook) into .agents/skills/pydantic in your project. Codex loads it when a task matches its description.

Can I use Pydantic in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kid-sid/claude-spellbook --skill pydantic -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pydantic, .gemini/skills/pydantic, .github/skills/pydantic and .opencode/skills/pydantic in your project.

What does Pydantic need to run?

SKILL.md names no scripts, command-line tools or credentials: Pydantic is instructions for the agent only. Our summary lists: Python 3.

Does Pydantic access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pydantic safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Pydantic use?

Pydantic is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pydantic use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pydantic?

Skills that share tags, products or a category with Pydantic: Python Configuration (wshobson/agents, 40k stars), Specx Settings (maksimzayats/specx, 202 stars), Env Config (aiskillstore/marketplace, 430 stars) and Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pydantic?

kid-sid (a GitHub user) maintains it in kid-sid/claude-spellbook, which has 189 GitHub stars. The repository holds 52 skills in this directory. The repository was last updated on August 5, 2026.

Source: kid-sid/claude-spellbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.