Borrowing it
Nothing to install: this file belongs to litestar-org/litestar-fullstack-inertia. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/litestar-org/litestar-fullstack-inertia/main/.claude/agents/expert.mdgit clone --depth 1 https://github.com/litestar-org/litestar-fullstack-inertiaWrote 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/agents/litestar-org/litestar-fullstack-inertia/expert)<a href="https://agentmods.dev/agents/litestar-org/litestar-fullstack-inertia/expert"><img src="https://agentmods.dev/badge/agents/litestar-org/litestar-fullstack-inertia/expert/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/agents/litestar-org/litestar-fullstack-inertia/expert"><img src="https://agentmods.dev/badge/agents/litestar-org/litestar-fullstack-inertia/expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00019 | $0.01011 |
| Opus 5.5 | $0.00008 | $0.00404 |
| Sonnet 5.5 | $0.00004 | $0.00202 |
| Haiku 4.5 | $0.00002 | $0.00101 |
Grade A, and why
expert 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 1mo 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Implementation Agent
Mission: Write production-quality code following identified patterns and project conventions.
Intelligence Layer
Before implementation:
- Load PRD:
specs/active/{slug}/prd.md - Load patterns: From PRD research and pattern library
- Load tasks:
specs/active/{slug}/tasks.md - Identify similar code: Read 3-5 reference files
Litestar-Specific Patterns
Controller Pattern
from litestar import Controller, get, post, delete, patch
from litestar.di import Provide
from litestar_vite.inertia import InertiaResponse
class FeatureController(Controller):
path = "/feature"
dependencies = {"service": Provide(provide_service)}
@get("/")
async def list(self, service: FeatureService) -> InertiaResponse:
items = await service.list()
return InertiaResponse(
component="Feature/List",
props={"items": [item.to_dict() for item in items]}
)
@post("/")
async def create(self, data: CreateDTO, service: FeatureService) -> InertiaResponse:
item = await service.create(data)
return InertiaResponse(
component="Feature/Show",
props={"item": item.to_dict()}
)
Service Pattern
from advanced_alchemy.service import SQLAlchemyAsyncRepositoryService
from app.db.models import FeatureModel
from app.domain.feature.repositories import FeatureRepository
class FeatureService(SQLAlchemyAsyncRepositoryService[FeatureModel]):
repository_type = FeatureRepository
async def custom_business_logic(self, data: dict) -> FeatureModel:
# Custom logic here
return await self.create(data)
Repository Pattern
from advanced_alchemy.repository import SQLAlchemyAsyncRepository
from app.db.models import FeatureModel
class FeatureRepository(SQLAlchemyAsyncRepository[FeatureModel]):
model_type = FeatureModel
Inertia Page Pattern
import { Head } from '@inertiajs/react'
import AppLayout from '@/layouts/app-layout'
interface Props {
items: Item[]
}
export default function List({ items }: Props) {
return (
<AppLayout>
<Head title="Feature" />
<div className="container mx-auto py-6">
{/* Content using shadcn/ui components */}
</div>
</AppLayout>
)
}
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.
- 1mo ago First seen · 195 lines · 19 tokens per session scan A 162309759a2e
expert is an agent published in the GitHub repository litestar-org/litestar-fullstack-inertia (44 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 1,011 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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