Fetches python-configuration from wshobson/agents and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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│ ── Universal (.agents/skills) ────────────────
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/python-configuration
Restart Cursor to activate python-configuration. Access via /python-configuration in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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
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
classSettings(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
classSettings(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 loadtry: 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
defget_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
classSettings(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.
classSettings(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.
Pydantic handles common conversions automatically.
from pydantic_settings import BaseSettings
from pydantic import Field, field_validator
classSettings(BaseSettings):# Automatically converts "true", "1", "yes" to True debug:bool=
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
