pydantic settings config
Implement type-safe, validated configuration management for Python projects using pydantic-settings.
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Grok Bot+18 agentspydantic settings config
Example session with this skill installed
Create a pydantic-settings config for a project with database_url, an optional redis_host, and a port that must be between 1024 and 65535. Use a MYAPP_ prefix for env vars.
- Read your context and instructions
- Compiled the pydantic settings config
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
model_config = SettingsConfigDict(env_prefix="MYAPP_", env_file=".env")
database_url: str
redis_host: str | None = None
port: int = Field(default=8000, ge=1024, le=65535)
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About this skill
The problem
Managing application configuration using raw os.environ leads to runtime errors, missing environment variables, and zero type safety. Hand-rolled config parsers often lack proper validation for numeric ranges or specific string patterns, making deployments fragile.
What it does
- Generates type-safe configuration classes using pydantic-settings v2.
- Implements multi-source loading from environment variables, .env files, and local overrides.
- Adds validation logic for port ranges, log levels, and cross-field constraints.
- Structures nested configurations for complex systems like database and cache settings.
- Provides singleton patterns using lru_cache for efficient configuration access.
Frameworks & tools
Python 3.x, Pydantic v2, pydantic-settings, and FastAPI integration.
Why this beats prompting it yourself
This skill enforces Pydantic v2 best practices, ensuring you use SettingsConfigDict instead of deprecated v1 patterns. It automatically handles boilerplate like env_prefix, nested delimiters, and secret masking that generic prompts often overlook.
Use cases
- Setting up a new Python project with robust configuration management.
- Refactoring legacy environment variable access to a structured class.
- Implementing environment-specific overrides for dev, staging, and production.
- Integrating validated settings into FastAPI dependency injection.
Known limitations
Requires pydantic-settings package to be installed. Specifically targets Pydantic v2 syntax.
How to install
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