python-configuration

Source Statement: The content of this skill is derived from wshobson/agents (MIT License, Copyright (c) 2024 Seth Hobson; the full license text is available in the same directory LICENSE.txt). Use, modification, and redistribution must comply with the terms of the MIT License and retain the copyright notice.

Chinese Description

This skill provides best practices and pattern guidance related to full-stack development.

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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

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

Capability Boundaries

✅ Good at handling

• Best practices related to full-stack development
• Code architecture design guidance
• Performance optimization suggestions

⚠️ Requires materials

• Specific project context
• Technology stack information

❌ Out of scope

• Non-technical business decisions
• Specific code implementation (guidance only)

Workflow

Step 1: Requirements Analysis

Understand project requirements and technical constraints

Step 2: Architecture Design

Design system architecture and component division

Step 3: Implementation Guidance

Provide specific implementation suggestions and code patterns

Step 4: Code Review

Review code quality and adherence to best practices

Step 5: Optimization Suggestions

Provide performance optimization and improvement suggestions

Gotchas

1. Over-engineering

Avoid using complex design patterns in simple scenarios

2. Neglecting Performance

Do not neglect performance optimization, especially in production environments

3. Security Considerations

Always consider security and data protection

4. Test Coverage

Ensure sufficient test coverage, especially for edge cases

5. Documentation Maintenance

Keep documentation synchronized with code updates