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-ai-servinggit 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-ai-serving)<a href="https://agentmods.dev/skills/litestar-org/litestar-skills/litestar-ai-serving"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-ai-serving/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-ai-serving"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-ai-serving.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 | $0.00046 | $0.00547 |
| Opus 5 | $0.00023 | $0.00273 |
| Sonnet 5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
litestar-ai-serving 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Litestar AI Serving
Use this skill for HTTP-facing AI agent endpoints, Google ADK integration, session-backed conversations, and Litestar service boundaries around model workflows.
Code Style Rules
- Keep agent orchestration behind service functions or providers.
- Use typed request and response DTOs at the HTTP boundary.
- Store multi-turn state through the project's database stack.
- Stream only when the client contract needs incremental output.
Quick Reference
- AI serving patterns: ai-serving.md
- Pair with sqlspec for ADK session stores.
- Pair with litestar-realtime for streaming or event fan-out.
Workflow
- Define the HTTP contract before agent internals.
- Wire agent runners through DI.
- Persist session state through the chosen data stack.
- Test deterministic failure, timeout, and cancellation paths.
Guardrails
- Do not expose raw agent internals as the API contract.
- Do not block request workers with unbounded model calls.
- Do not store prompts, tool outputs, or memory without a retention decision.
- Do not skip authorization on agent endpoints.
Validation Checkpoint
- Request and response DTOs are explicit.
- Session persistence is wired.
- Timeouts and model failures are handled.
- Auth policy matches the sensitivity of tools and data.
Example
@get("/chat/{session_id:str}")
async def chat(session_id: str, runner: Runner, body: ChatRequest) -> ChatResponse:
result = await runner.run_async(session_id=session_id, new_message=body.message)
return ChatResponse(message=result.final_response)
References Index
Official References
- https://docs.litestar.dev/ - Litestar documentation
- https://docs.litestar.dev/latest/reference/ - Litestar API reference
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 · 83 lines · 46 tokens per session scan A 83492ee0770a
litestar-ai-serving is a skill published in the GitHub repository litestar-org/litestar-skills (14 stars, last pushed 22d ago), licensed MIT. It adds 46 tokens to every session and 547 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.
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