python-configuration

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npx skills add wshobson/agents --skill python-configuration

Documentation

SKILL.md

npx skills add ...

Documentation

SKILL.md

Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

npx skills add wshobson/agents --skill python-configuration

Documentation

SKILL.mdView source

Python Configuration Management

Externalize configuration from code using environment variables and typed settings. Well-managed configuration enables the same code to run in any environment without modification.

When to Use This Skill

Setting up a new project's configuration system
Migrating from hardcoded values to environment variables
Implementing pydantic-settings for typed configuration
Managing secrets and sensitive values
Creating environment-specific settings (dev/staging/prod)
Validating configuration at application startup

Core Concepts

1. Externalized Configuration

All environment-specific values (URLs, secrets, feature flags) come from environment variables, not code.

2. Typed Settings

Parse and validate configuration into typed objects at startup, not scattered throughout code.

3. Fail Fast

Validate all required configuration at application boot. Missing config should crash immediately with a clear message.

4. Sensible Defaults

Provide reasonable defaults for local development while requiring explicit values for sensitive settings.

Quick Start

from pydantic_settings import BaseSettings
from pydantic import Field
 
class Settings(BaseSettings):
    database_url: str = Field(alias="DATABASE_URL")
    api_key: str = Field(alias="API_KEY")
    debug: bool = Field(default=False, alias="DEBUG")
 
settings = Settings()  # Loads from environment

Fundamental Patterns

Pattern 1: Typed Settings with Pydantic

Create a central settings class that loads and validates all configuration.

from pydantic_settings import BaseSettings
from pydantic import Field, PostgresDsn, ValidationError
import sys
 
class Settings(BaseSettings):
    """Application configuration loaded from environment variables."""
 
    # Database
    db_host: str = Field(alias="DB_HOST")
    db_port: int = Field(default=5432, alias="DB_PORT")
    db_name: str = Field(alias="DB_NAME")
    db_user: str = Field(alias="DB_USER")
    db_password: str = Field(alias="DB_PASSWORD")
 
    # Redis
    redis_url: str = Field(default="redis://localhost:6379", alias="REDIS_URL")
 
    # API Keys
    api_secret_key: str = Field(alias="API_SECRET_KEY")
 
    # Feature flags
    enable_new_feature: bool = Field(default=False, alias="ENABLE_NEW_FEATURE")
 
    model_config = {
        "env_file": ".env",
        "env_file_encoding": "utf-8",
    }
 
# Create singleton instance at module load
try:
    settings = Settings()
except ValidationError as e:
    print(f"Configuration error:\n{e}")
    sys.exit(1)

Import settings throughout your application:

from myapp.config import settings
 
def get_database_connection():
    return connect(
        host=settings.db_host,
        port=settings.db_port,
        database=settings.db_name,
    )

Pattern 2: Fail Fast on Missing Configuration

Required settings should crash the application immediately with a clear error.

from pydantic_settings import BaseSettings
from pydantic import Field, ValidationError
import sys
 
class Settings(BaseSettings):
    # Required - no default means it must be set
    api_key: str = Field(alias="API_KEY")
    database_url: str = Field(alias="DATABASE_URL")
 
    # Optional with defaults
    log_level: str = Field(default="INFO", alias="LOG_LEVEL")
 
try:
    settings = Settings()
except ValidationError as e:
    print("=" * 60)
    print("CONFIGURATION ERROR")
    print("=" * 60)
    for error in e.errors():
        field = error["loc"][0]
        print(f"  - {field}: {error['msg']}")
    print("\nPlease set the required environment variables.")
    sys.exit(1)

A clear error at startup is better than a cryptic None failure mid-request.

Pattern 3: Local Development Defaults

Provide sensible defaults for local development while requiring explicit values for secrets.

class Settings(BaseSettings):
    # Has local default, but prod will override
    db_host: str = Field(default="localhost", alias="DB_HOST")
    db_port: int = Field(default=5432, alias="DB_PORT")
 
    # Always required - no default for secrets
    db_password: str = Field(alias="DB_PASSWORD")
    api_secret_key: str = Field(alias="API_SECRET_KEY")
 
    # Development convenience
    debug: bool = Field(default=False, alias="DEBUG")
 
    model_config = {"env_file": ".env"}

Create a .env file for local development (never commit this):

# .env (add to .gitignore)
DB_PASSWORD=local_dev_password
API_SECRET_KEY=dev-secret-key
DEBUG=true

Pattern 4: Namespaced Environment Variables

Prefix related variables for clarity and easy debugging.

# Database configuration
DB_HOST=localhost
DB_PORT=5432
DB_NAME=myapp
DB_USER=admin
DB_PASSWORD=secret
 
# Redis configuration
REDIS_URL=redis://localhost:6379
REDIS_MAX_CONNECTIONS=10
 
# Authentication
AUTH_SECRET_KEY=your-secret-key
AUTH_TOKEN_EXPIRY_SECONDS=3600
AUTH_ALGORITHM=HS256
 
# Feature flags
FEATURE_NEW_CHECKOUT=true
FEATURE_BETA_UI=false

Makes env | grep DB_ useful for debugging.

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

Never hardcode config - All environment-specific values from env vars
Use typed settings - Pydantic-settings with validation
Fail fast - Crash on missing required config at startup
Provide dev defaults - Make local development easy
Never commit secrets - Use .env files (gitignored) or secret managers
Namespace variables - DB_HOST, REDIS_URL for clarity
Import settings singleton - Don't call os.getenv() throughout code
Document all variables - README should list required env vars
Validate early - Check config correctness at boot time
Use secrets_dir - Support mounted secrets in containers

npx skills add wshobson/agents --skill python-configuration

Documentation

SKILL.md

npx skills add wshobson/agents --skill python-configuration

Documentation

SKILL.mdView source

Python Configuration Management

Externalize configuration from code using environment variables and typed settings. Well-managed configuration enables the same code to run in any environment without modification.

When to Use This Skill

Setting up a new project's configuration system
Migrating from hardcoded values to environment variables
Implementing pydantic-settings for typed configuration
Managing secrets and sensitive values
Creating environment-specific settings (dev/staging/prod)
Validating configuration at application startup

Core Concepts

1. Externalized Configuration

All environment-specific values (URLs, secrets, feature flags) come from environment variables, not code.

2. Typed Settings

Parse and validate configuration into typed objects at startup, not scattered throughout code.

3. Fail Fast

Validate all required configuration at application boot. Missing config should crash immediately with a clear message.

4. Sensible Defaults

Provide reasonable defaults for local development while requiring explicit values for sensitive settings.

Quick Start

from pydantic_settings import BaseSettings
from pydantic import Field
 
class Settings(BaseSettings):
    database_url: str = Field(alias="DATABASE_URL")
    api_key: str = Field(alias="API_KEY")
    debug: bool = Field(default=False, alias="DEBUG")
 
settings = Settings()  # Loads from environment

Fundamental Patterns

Pattern 1: Typed Settings with Pydantic

Create a central settings class that loads and validates all configuration.

from pydantic_settings import BaseSettings
from pydantic import Field, PostgresDsn, ValidationError
import sys
 
class Settings(BaseSettings):
    """Application configuration loaded from environment variables."""
 
    # Database
    db_host: str = Field(alias="DB_HOST")
    db_port: int = Field(default=5432, alias="DB_PORT")
    db_name: str = Field(alias="DB_NAME")
    db_user: str = Field(alias="DB_USER")
    db_password: str = Field(alias="DB_PASSWORD")
 
    # Redis
    redis_url: str = Field(default="redis://localhost:6379", alias="REDIS_URL")
 
    # API Keys
    api_secret_key: str = Field(alias="API_SECRET_KEY")
 
    # Feature flags
    enable_new_feature: bool = Field(default=False, alias="ENABLE_NEW_FEATURE")
 
    model_config = {
        "env_file": ".env",
        "env_file_encoding": "utf-8",
    }
 
# Create singleton instance at module load
try:
    settings = Settings()
except ValidationError as e:
    print(f"Configuration error:\n{e}")
    sys.exit(1)

Import settings throughout your application:

from myapp.config import settings
 
def get_database_connection():
    return connect(
        host=settings.db_host,
        port=settings.db_port,
        database=settings.db_name,
    )

Pattern 2: Fail Fast on Missing Configuration

Required settings should crash the application immediately with a clear error.

from pydantic_settings import BaseSettings
from pydantic import Field, ValidationError
import sys
 
class Settings(BaseSettings):
    # Required - no default means it must be set
    api_key: str = Field(alias="API_KEY")
    database_url: str = Field(alias="DATABASE_URL")
 
    # Optional with defaults
    log_level: str = Field(default="INFO", alias="LOG_LEVEL")
 
try:
    settings = Settings()
except ValidationError as e:
    print("=" * 60)
    print("CONFIGURATION ERROR")
    print("=" * 60)
    for error in e.errors():
        field = error["loc"][0]
        print(f"  - {field}: {error['msg']}")
    print("\nPlease set the required environment variables.")
    sys.exit(1)

A clear error at startup is better than a cryptic None failure mid-request.

Pattern 3: Local Development Defaults

Provide sensible defaults for local development while requiring explicit values for secrets.

class Settings(BaseSettings):
    # Has local default, but prod will override
    db_host: str = Field(default="localhost", alias="DB_HOST")
    db_port: int = Field(default=5432, alias="DB_PORT")
 
    # Always required - no default for secrets
    db_password: str = Field(alias="DB_PASSWORD")
    api_secret_key: str = Field(alias="API_SECRET_KEY")
 
    # Development convenience
    debug: bool = Field(default=False, alias="DEBUG")
 
    model_config = {"env_file": ".env"}

Create a .env file for local development (never commit this):

# .env (add to .gitignore)
DB_PASSWORD=local_dev_password
API_SECRET_KEY=dev-secret-key
DEBUG=true

Pattern 4: Namespaced Environment Variables

Prefix related variables for clarity and easy debugging.

# Database configuration
DB_HOST=localhost
DB_PORT=5432
DB_NAME=myapp
DB_USER=admin
DB_PASSWORD=secret
 
# Redis configuration
REDIS_URL=redis://localhost:6379
REDIS_MAX_CONNECTIONS=10
 
# Authentication
AUTH_SECRET_KEY=your-secret-key
AUTH_TOKEN_EXPIRY_SECONDS=3600
AUTH_ALGORITHM=HS256
 
# Feature flags
FEATURE_NEW_CHECKOUT=true
FEATURE_BETA_UI=false

Makes env | grep DB_ useful for debugging.

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

Never hardcode config - All environment-specific values from env vars
Use typed settings - Pydantic-settings with validation
Fail fast - Crash on missing required config at startup
Provide dev defaults - Make local development easy
Never commit secrets - Use .env files (gitignored) or secret managers
Namespace variables - DB_HOST, REDIS_URL for clarity
Import settings singleton - Don't call os.getenv() throughout code
Document all variables - README should list required env vars
Validate early - Check config correctness at boot time
Use secrets_dir - Support mounted secrets in containers