Python Expert

A Python development specialist covering web frameworks such as FastAPI and Django, asynchronous code, data validation with Pydantic, and database access with SQLAlchemy.

In plain words
What is it for?
Building and reviewing Python web APIs and applications, defining validated data models, handling asynchronous work, and working with SQLAlchemy databases.
Why use it?
It helps developers avoid outdated library syntax, incorrect validation, inefficient asynchronous code, and common database or security mistakes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/sergei-aronsen/claude-code-toolkit/python
Any agent
npx skills add sergei-aronsen/claude-code-toolkit --skill python
Clone the repo
git clone --depth 1 https://github.com/sergei-aronsen/claude-code-toolkit

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,193 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.02193
Opus 5 $0.00014 $0.01097
Sonnet 5 $0.00006 $0.00439
Haiku 4.5 $0.00003 $0.00219

Measured 2d ago against content hash 496e334e6224, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Python 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 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.

templates/python/skills/python/SKILL.md · 350 lines

How it starts

The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Expert Skill

This skill provides deep Python expertise including FastAPI/Django patterns, async handling, Pydantic v2 validation, SQLAlchemy 2.0, and security best practices.


Pydantic v2 (IMPORTANT!)

Always Use v2 Syntax

from pydantic import BaseModel, Field, ConfigDict, EmailStr, field_validator

# ✅ Pydantic v2 syntax
class UserCreate(BaseModel):
    email: EmailStr
    name: str = Field(min_length=2, max_length=100)
    age: int | None = Field(default=None, ge=0, le=150)

class UserResponse(BaseModel):
    id: int
    email: str
    name: str

    model_config = ConfigDict(from_attributes=True)

# ❌ Pydantic v1 syntax (DON'T USE!)
class UserOld(BaseModel):
    class Config:           # Wrong! Use model_config
        orm_mode = True     # Wrong! Use from_attributes

Validation

from pydantic import field_validator, model_validator

class OrderCreate(BaseModel):
    items: list[OrderItem]
    discount_code: str | None = None

    @field_validator('items')
    @classmethod
    def validate_items(cls, v: list[OrderItem]) -> list[OrderItem]:
        if not v:
            raise ValueError('Order must have at least one item')
        return v

    @model_validator(mode='after')
    def validate_order(self) -> 'OrderCreate':
        if self.discount_code and len(self.items) < 3:
            raise ValueError('Discount requires at least 3 items')
        return self

Async Patterns

Always await I/O Operations

# ✅ Correct - async for I/O
async def get_user(db: AsyncSession, user_id: int) -> User | None:
    result = await db.execute(select(User).where(User.id == user_id))
    return result.scalar_one_or_none()

# ✅ Parallel operations
async def get_dashboard_data(db: AsyncSession, user_id: int):
    user, posts, notifications = await asyncio.gather(
        get_user(db, user_id),
        get_user_posts(db, user_id),
        get_notifications(db, user_id),
    )
    return {"user": user, "posts": posts, "notifications": notifications}

# ❌ Sequential when could be parallel
async def get_dashboard_data_slow(db: AsyncSession, user_id: int):
    user = await get_user(db, user_id)
    posts = await get_user_posts(db, user_id)  # Doesn't depend on user
    notifications = await get_notifications(db, user_id)
    return {"user": user, "posts": posts, "notifications": notifications}

Read the full file on GitHub · 350 lines

Changes

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.

  1. 2d ago First seen · 350 lines · 28 tokens per session scan A 496e334e6224

Subscribe to this mod's changes

Python Expert is a skill published in the GitHub repository sergei-aronsen/claude-code-toolkit (5 stars, last pushed 16d ago), licensed MIT. It adds 28 tokens to every session and 2,193 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 2026-08-31.

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