Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/julien777z/agent-sync-action/python-configurationnpx skills add julien777z/agent-sync-action --skill python-configurationgit clone --depth 1 https://github.com/julien777z/agent-sync-actionWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00036 | $0.01390 |
| Opus 5 | $0.00018 | $0.00695 |
| Sonnet 5 | $0.00007 | $0.00278 |
| Haiku 4.5 | $0.00004 | $0.00139 |
Grade A, and why
python-configuration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- python-configuration — 95% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 214 lines · 36 tokens per session scan A 52067feee4ce
python-configuration is a skill published in the GitHub repository julien777z/agent-sync-action (5 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 1,390 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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