Python ConfigurationSAFE
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Overview
Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi
adb71e0b2512OBSERVED · 2026-10-01What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
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.geTrust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (2)
CLAUDE.md
# Read from environment variable or file
Gates applied: no_behavioural_pass.
adb71e0b2512full audit observations/trust-audit/skill/wshobson__python-configuration.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-01 | adb71e0b2512 | SAFE | B | 89 | first audit |
Questions
What does the Python Configuration skill do?
Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi
Is Python Configuration safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Python Configuration access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (adb71e0b2512), read on 2026-10-01. The repository is watched, and a new audit runs when it changes — this is the first audit.