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 skills add litestar-org/litestar-skills --skill litestar-settingsgit clone --depth 1 https://github.com/litestar-org/litestar-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/litestar-org/litestar-skills/litestar-settings)<a href="https://agentmods.dev/skills/litestar-org/litestar-skills/litestar-settings"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-settings/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/litestar-org/litestar-skills/litestar-settings"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-settings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00048 | $0.00556 |
| Opus 5 | $0.00024 | $0.00278 |
| Sonnet 5 | $0.00010 | $0.00111 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
litestar-settings 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Litestar Settings
Use this skill for typed settings, env loading, cached settings factories, and app-state wiring.
Code Style Rules
- Use dataclass settings plus get_env for fresh Litestar apps.
- Use pydantic-settings when the project already depends on Pydantic for config.
- Cache settings once per process.
- Keep secret values out of logs and generated docs.
Quick Reference
- Settings patterns: settings.md
- Pair with litestar-di for settings providers.
- Pair with litestar-deployment for runtime env wiring.
Workflow
- Inventory required env vars and defaults.
- Choose dataclass settings or the existing Pydantic settings path.
- Add a cached factory.
- Inject settings through app state or DI.
Guardrails
- Do not parse env vars repeatedly in handlers.
- Do not use msgspec Structs for env loading.
- Do not bake environment-specific values into code.
- Do not log secrets while debugging config.
Validation Checkpoint
- Settings are typed.
- Settings are cached.
- Required values fail early.
- Tests can override settings without mutating global process env unexpectedly.
Example
from dataclasses import dataclass, field
from functools import lru_cache
from os import getenv
def get_env(key: str, default: str) -> str:
return getenv(key, default)
@dataclass(frozen=True)
class AppSettings:
name: str = field(default_factory=lambda: get_env("APP_NAME", "api"))
@lru_cache(maxsize=1)
def get_settings() -> AppSettings:
return AppSettings()
References Index
Official References
- https://docs.litestar.dev/ - Litestar documentation
- https://docs.litestar.dev/latest/reference/ - Litestar API reference
Shared Styleguide Baseline
What ships with it
1 file 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.
- 11d ago First seen · 96 lines · 48 tokens per session scan A 40b7ebc15b2d
litestar-settings is a skill published in the GitHub repository litestar-org/litestar-skills (14 stars, last pushed 21d ago), licensed MIT. It adds 48 tokens to every session and 556 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 2026-08-30.
Other skills, from other repositories
python-backend-expert
This skill should be used when the user is writing, reviewing, debugging, or architecting Python backend code using Litestar or FastAPI with SQLAlchemy or Advanced Alchemy. Provides expert critique covering SOLID principles, hexagonal architecture, repository/service patterns, dependency injection, async correctness…
odoo-python
Use when writing or reviewing any Python in an Odoo module — models, computes, overrides, controllers, wizards. Carries PSAE review-derived principles for ORM correctness, performance, style, and security, plus references for exact patterns. Invoke before writing logic in models/, controllers/, or wizard/.
fastapi
FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.
cloudflare-workers-multi-lang
Multi-language Workers development with Rust, Python, and WebAssembly. Use when building Workers in languages other than JavaScript/TypeScript, or when integrating WASM modules for performance-critical code.
craftcms
Craft CMS 5 plugin and module development — extending Craft with PHP. Covers elements, element queries, services, models, records, controllers, migrations, queue jobs, console commands, field types, native fields, events, behaviors, Twig extensions, widgets, filesystems, permissions, project config, GraphQL, testing…
python-services
Python patterns for CLI tools, async parallelism, and backend services. Use when building CLI apps, async/parallel Python, FastAPI services, background jobs, or configuring Python project tooling (uv, ruff, ty).