使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/wshobson/agents --skill python-configuration開始前の確認
- 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
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 environmentFundamental 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=truePattern 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=falseMakes 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
.envfiles (gitignored) or secret managers - Namespace variables -
DB_HOST,REDIS_URLfor 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
