Agent skill
centralized-settings
Centralized settings pattern for all configuration and environment variables. Use when adding or accessing settings, secrets, or config in code.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/centralized-settings
SKILL.md
Centralized Settings Pattern (Python)
All configuration and environment variables MUST be centralized in a single settings module. Never scatter os.getenv() or os.environ calls across the codebase.
The Pattern
Use Pydantic BaseSettings for type-safe, validated configuration:
python
# settings.py - Single source of truth
from pydantic import Field
from pydantic_settings import BaseSettings
DEFAULT_TIMEOUT_SECONDS = 30
DEFAULT_MODEL = "gpt-4"
class Settings(BaseSettings):
# Type annotation + default + description + validation
api_key: str | None = Field(
default=None,
description="API key for external service.",
)
timeout_seconds: int = Field(
DEFAULT_TIMEOUT_SECONDS,
description="Request timeout in seconds (>0).",
gt=0,
)
model_name: str = Field(
DEFAULT_MODEL,
description="LLM model to use.",
)
settings = Settings()
Forbidden Patterns
python
# BAD: Scattered os.getenv() calls
api_key = os.getenv("API_KEY")
timeout = int(os.getenv("TIMEOUT", "30"))
debug = os.getenv("DEBUG", "false").lower() == "true"
# BAD: os.environ dictionary access
database_url = os.environ["DATABASE_URL"]
# BAD: Inline defaults scattered in code
model = os.getenv("MODEL_NAME", "gpt-4") # Why is this the default?
# BAD: Module-level env access
TIMEOUT = int(os.getenv("TIMEOUT", "30"))
Required Pattern
python
# GOOD: Import centralized settings
from myproject.settings import settings
async def call_api():
timeout = settings.timeout_seconds # Typed, validated
api_key = settings.api_key # Centralized
# GOOD: Import defaults for documentation
from myproject.settings import DEFAULT_TIMEOUT_SECONDS
For Secrets
python
# In settings.py - use secret manager
from myproject.secrets import get_secret
class Settings(BaseSettings):
api_key: str | None = Field(
default_factory=lambda: get_secret("API_KEY"),
description="API key (from secret manager or env).",
)
Exceptions
- Test files - May use
patch.dict(os.environ, ...)for mocking - CLI scripts - May read env for
--prod/--localswitching, then use settings - The settings.py file itself - Obviously reads env vars
Benefits
- Single source of truth - All settings in one place
- Type safety - Pydantic validates at startup
- Documentation - Every setting has a description
- Validation - Constraints catch config errors early
- IDE support - Autocomplete works
- Testability - Mock settings object, not scattered env vars
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