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python-configuration

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

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skills CLI npx skills add https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-configuration
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Git git clone https://github.com/wshobson/agents.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole wshobson/agents collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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

  1. Never hardcode config - All environment-specific values from env vars
  2. Use typed settings - Pydantic-settings with validation
  3. Fail fast - Crash on missing required config at startup
  4. Provide dev defaults - Make local development easy
  5. Never commit secrets - Use .env files (gitignored) or secret managers
  6. Namespace variables - DB_HOST, REDIS_URL for clarity
  7. Import settings singleton - Don't call os.getenv() throughout code
  8. Document all variables - README should list required env vars
  9. Validate early - Check config correctness at boot time
  10. Use secrets_dir - Support mounted secrets in containers
Files (agents)
  • references
    • details.md 3.9 KB
      # python-configuration — detailed worked examples
      
      ## Advanced Patterns
      
      ### Pattern 5: Type Coercion
      
      Pydantic handles common conversions automatically.
      
      ```python
      from pydantic_settings import BaseSettings
      from pydantic import Field, field_validator
      
      class Settings(BaseSettings):
          # Automatically converts "true", "1", "yes" to True
          debug: bool = False
      
          # Automatically converts string to int
          max_connections: int = 100
      
          # Parse comma-separated string to list
          allowed_hosts: list[str] = Field(default_factory=list)
      
          @field_validator("allowed_hosts", mode="before")
          @classmethod
          def parse_allowed_hosts(cls, v: str | list[str]) -> list[str]:
              if isinstance(v, str):
                  return [host.strip() for host in v.split(",") if host.strip()]
              return v
      ```
      
      Usage:
      
      ```bash
      ALLOWED_HOSTS=example.com,api.example.com,localhost
      MAX_CONNECTIONS=50
      DEBUG=true
      ```
      
      ### Pattern 6: Environment-Specific Configuration
      
      Use an environment enum to switch behavior.
      
      ```python
      from enum import Enum
      from pydantic_settings import BaseSettings
      from pydantic import Field, computed_field
      
      class Environment(str, Enum):
          LOCAL = "local"
          STAGING = "staging"
          PRODUCTION = "production"
      
      class Settings(BaseSettings):
          environment: Environment = Field(
              default=Environment.LOCAL,
              alias="ENVIRONMENT",
          )
      
          # Settings that vary by environment
          log_level: str = Field(default="DEBUG", alias="LOG_LEVEL")
      
          @computed_field
          @property
          def is_production(self) -> bool:
              return self.environment == Environment.PRODUCTION
      
          @computed_field
          @property
          def is_local(self) -> bool:
              return self.environment == Environment.LOCAL
      
      # Usage
      if settings.is_production:
          configure_production_logging()
      else:
          configure_debug_logging()
      ```
      
      ### Pattern 7: Nested Configuration Groups
      
      Organize related settings into nested models.
      
      ```python
      from pydantic import BaseModel
      from pydantic_settings import BaseSettings
      
      class DatabaseSettings(BaseModel):
          host: str = "localhost"
          port: int = 5432
          name: str
          user: str
          password: str
      
      class RedisSettings(BaseModel):
          url: str = "redis://localhost:6379"
          max_connections: int = 10
      
      class Settings(BaseSettings):
          database: DatabaseSettings
          redis: RedisSettings
          debug: bool = False
      
          model_config = {
              "env_nested_delimiter": "__",
              "env_file": ".env",
          }
      ```
      
      Environment variables use double underscore for nesting:
      
      ```bash
      DATABASE__HOST=db.example.com
      DATABASE__PORT=5432
      DATABASE__NAME=myapp
      DATABASE__USER=admin
      DATABASE__PASSWORD=secret
      REDIS__URL=redis://redis.example.com:6379
      ```
      
      ### Pattern 8: Secrets from Files
      
      For container environments, read secrets from mounted files.
      
      ```python
      from pydantic_settings import BaseSettings
      from pydantic import Field
      from pathlib import Path
      
      class Settings(BaseSettings):
          # Read from environment variable or file
          db_password: str = Field(alias="DB_PASSWORD")
      
          model_config = {
              "secrets_dir": "/run/secrets",  # Docker secrets location
          }
      ```
      
      Pydantic will look for `/run/secrets/db_password` if the env var isn't set.
      
      ### Pattern 9: Configuration Validation
      
      Add custom validation for complex requirements.
      
      ```python
      from pydantic_settings import BaseSettings
      from pydantic import Field, model_validator
      
      class Settings(BaseSettings):
          db_host: str = Field(alias="DB_HOST")
          db_port: int = Field(alias="DB_PORT")
          read_replica_host: str | None = Field(default=None, alias="READ_REPLICA_HOST")
          read_replica_port: int = Field(default=5432, alias="READ_REPLICA_PORT")
      
          @model_validator(mode="after")
          def validate_replica_settings(self):
              if self.read_replica_host and self.read_replica_port == self.db_port:
                  if self.read_replica_host == self.db_host:
                      raise ValueError(
                          "Read replica cannot be the same as primary database"
                      )
              return self
      ```
      
  • SKILL.md 6.1 KB
    ---
    name: python-configuration
    description: Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.
    ---
    
    # 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
    
    ```python
    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.
    
    ```python
    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:
    
    ```python
    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.
    
    ```python
    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.
    
    ```python
    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):
    
    ```bash
    # .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.
    
    ```bash
    # 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
    
    1. **Never hardcode config** - All environment-specific values from env vars
    2. **Use typed settings** - Pydantic-settings with validation
    3. **Fail fast** - Crash on missing required config at startup
    4. **Provide dev defaults** - Make local development easy
    5. **Never commit secrets** - Use `.env` files (gitignored) or secret managers
    6. **Namespace variables** - `DB_HOST`, `REDIS_URL` for clarity
    7. **Import settings singleton** - Don't call `os.getenv()` throughout code
    8. **Document all variables** - README should list required env vars
    9. **Validate early** - Check config correctness at boot time
    10. **Use secrets_dir** - Support mounted secrets in containers
    

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