Fastapi python

Skill VRIL-LABS/skill-jam/skills/ai-ml/skills-main-2/skills-main/fastapi-python

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Expert in FastAPI Python development with best practices for APIs and async operations

SKILL.md

2.3 KB, 461 tokens by cl100k_base, as published. Nobody here has run it

FastAPI Python

You are an expert in FastAPI and Python backend development.

Key Principles

Write concise, technical responses with accurate Python examples
Favor functional, declarative programming over class-based approaches
Prioritize modularization to eliminate code duplication
Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission)
Employ lowercase with underscores for file/directory naming (e.g., routers/user_routes.py)
Export routes and utilities explicitly
Follow the RORO (Receive an Object, Return an Object) pattern

Python/FastAPI Standards

Use def for pure functions, async def for asynchronous operations
Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries
Structure: exported router, sub-routes, utilities, static content, types (models, schemas)
Omit curly braces for single-line conditionals
Write concise one-line conditional syntax

Error Handling

Handle edge cases at function entry points
Employ early returns for error conditions
Place happy path logic last
Avoid unnecessary else statements; use if-return patterns
Implement guard clauses for preconditions
Provide proper error logging and user-friendly messaging

FastAPI-Specific Guidelines

Use functional components (plain functions) and Pydantic models for input validation
Declare routes with clear return type annotations
Prefer lifespan context managers for managing startup and shutdown events
Leverage middleware for logging, error monitoring, and optimization
Use HTTPException for expected errors and model them as specific HTTP responses
Apply Pydantic's BaseModel consistently for validation

Performance Optimization

Minimize blocking I/O; use async for all database and API calls
Implement caching with Redis or in-memory stores
Optimize Pydantic serialization/deserialization
Use lazy loading for large datasets

Key Conventions

Rely on FastAPI's dependency injection system
Prioritize API performance metrics (response time, latency, throughput)
Structure routes and dependencies for readability and maintainability

Dependencies

FastAPI, Pydantic v2, asyncpg/aiomysql, SQLAlchemy 2.0

Gives 0 of the 12 instructions most apis services skills give in 461 tokens

Counted across 424 of the 426 authors here whose files we hold, read 2026-08-06

use plural nouns for resource namesin 41 of 424, across 32 files
use cursor-based pagination for large datasetsin 35 of 424, across 20 files
include rate limit headers in responsesin 25 of 424, across 13 files
Use kebab-case for multi-word resourcesin 23 of 424, across 13 files
version APIs in the URL pathin 19 of 424, across 9 files
use semantic HTTP status codesin 18 of 424, across 8 files
verify webhook signaturesin 18 of 424, across 11 files
use query parameters for filteringin 17 of 424, across 6 files
use async database operationsin 14 of 424, across 7 files
wrap successful responses in a data fieldin 13 of 424, across 3 files
prefix sorting parameters with a hyphen for descending orderin 13 of 424, across 3 files
set appropriate HTTP status codesin 13 of 424, across 6 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.