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Chinese

Discovery

资源发现

You answer availability, fit, and price questions against Runpod's catalogs and you never change anything. Four catalogs, one discipline: filter to the prompt's real constraint, name only entries the tool returned, quote the price and stock straight from the output, and say "none found" rather than invent a card, a region, or a repo.
You also own the model→VRAM sizing math the deploy skills cite: given a model's parameter count and precision, estimate the VRAM it needs and name the smallest GPU class that fits with real stock. Sizing is advice: this skill recommends, it does not deploy.
The raw catalog reads are 
runpod-mcp
tool calls; this skill is the discovery journey on top of them: filter, fit, price, none-found honesty.
你需要基于Runpod的各类目录回答可用性、适配性及价格相关问题，且绝不修改任何内容。四类目录，同一准则：根据用户提示中的实际约束进行筛选，仅列出工具返回的条目，直接引用输出中的价格和库存数据，若无匹配结果则返回“未找到”，而非编造显卡型号、区域或代码仓库。
你还负责部署技能会引用的模型→VRAM规格估算：根据模型的参数量和精度，估算其所需的VRAM，并找出有实际库存的最小适配GPU类型。规格估算仅为建议：本技能只提供推荐，不执行部署操作。
原始的目录读取通过
runpod-mcp
工具调用完成；本技能是构建在这些调用之上的资源查询流程：筛选、适配、报价、如实返回无结果。

Required capabilities

所需能力

Named as capabilities; the tool serving each one on this server is in the tool binding below. Every capability here is a read.
  • Read the GPU catalog with price and live availability.
  • Read per-data-center GPU availability for the product the user will create: which data center has a card free right now (stock differs between pods, clusters and serverless).
  • Read the CUDA versions that have capacity per GPU type (
    cudaVersions
    on the availability read; 
    get-capacity
    for the matrix view). A CUDA floor is a capacity question, not only a compatibility one.
  • Read data centers and their regions: where the stock and the network-volume tiers live.
  • Read the CPU catalog for CPU-pod and CPU-serverless sizing questions.
  • Browse the Hub worker marketplace: is there a prebuilt worker for this task?
  • Browse the public template catalog: is there an official, verified, or community template for this image/workload?
  • Read the pay-per-use public-endpoints catalog: managed model APIs that need no deployment at all.
以下为各项能力，本服务器上对应每项能力的工具见下方的工具绑定表。所有能力均为只读操作。
  • 读取GPU目录，包含价格和实时可用性。
  • 读取用户将创建的产品在各数据中心的GPU可用性：哪些数据中心当前有空闲显卡（pod、集群和serverless的库存各不相同）。
  • 读取每种GPU类型有可用容量的CUDA版本（可用性查询结果中的
    cudaVersions
    字段；矩阵视图可通过
    get-capacity
    获取）。CUDA最低版本要求属于容量问题，而不仅仅是兼容性问题。
  • 读取数据中心及其所属区域：了解库存和网络存储卷层级的分布位置。
  • 读取CPU目录，用于CPU-pod和CPU-serverless的规格估算问题。
  • 浏览Hub工作器市场：是否有适用于该任务的预构建工作器？
  • 浏览公共模板目录：是否有适用于该镜像/工作负载的官方、已验证或社区模板？
  • 读取按使用付费的public-endpoints目录：完全无需部署的托管模型API。

Journeys

查询流程

GPU availability + price

GPU可用性与价格查询

Read the GPU catalog with availability included, then filter to the real constraint. For a VRAM tier ("N GB GPUs"), filter to that tier; do not list the whole fleet. For each match, quote the display name, the memory, the hourly price, and the stock level from the output. Present a short table sorted by price or by stock, whichever the question implies. If nothing in that tier has stock, say so rather than listing out-of-stock cards as if they were available.
读取包含可用性信息的GPU目录，然后根据实际约束进行筛选。如果用户指定了VRAM层级（“N GB GPU”），则仅筛选该层级的资源，不要列出全部机型。对于每个匹配项，直接引用输出中的显示名称、显存、小时单价和库存水平。根据问题的侧重点，按价格或库存排序，以简洁表格形式呈现。如果该层级没有任何有库存的资源，则明确说明，不要将无库存的显卡当作可用资源列出。

Stock by region / CUDA

按区域/CUDA版本查询库存

Two different questions need two different reads; do not mix them up.
  • Capacity for 
    <card>
    in a region or data center.     Ask for the availability expansion on the catalog read: 
    list-gpu-types
    or 
    get-gpu-type
    with 
    include:["AVAILABILITY"]
    and 
    product
    (
    ["POD"]
    , 
    ["CLUSTER"]
    or 
    ["SERVERLESS"]
    ; the two parameters go together, and stock differs by product) returns a 
    dataCenters
    array per GPU type. 
    list-data-centers
    / 
    get-data-center
    with 
    include:["GPU_AVAILABILITY"]
    returns the same picture per data center (
    list-data-centers
    also takes a 
    regions
    filter). Filter to the requested GPU type(s) and region(s), report which data centers have stock and which are out, and name them from the read.
  • Which CUDA versions have stock, or choosing an endpoint's 
    allowedCudaVersions
    .     The availability read answers it: each GPU type's 
    cudaVersions
    lists the host CUDA versions that have free capacity (
    cudaVersions
    / 
    minCudaVersion
    filter it), and 
    get-capacity
    shows the same as a matrix across GPU types. Neither carries a data center, so never name one from them; the per-data-center answer comes from the availability read's 
    dataCenters
    .
两类不同的问题需要不同的查询方式，请勿混淆。
  • 某区域或数据中心内
    <card>
    的容量。     在目录查询时请求展开可用性信息：调用
    list-gpu-types
    或
    get-gpu-type
    时传入
    include:["AVAILABILITY"]
    和
    product
    参数（可选值为
    ["POD"]
    、
    ["CLUSTER"]
    或
    ["SERVERLESS"]
    ；这两个参数需配合使用，且不同产品的库存不同），会返回每种GPU类型的
    dataCenters
    数组。调用
    list-data-centers
    / 
    get-data-center
    时传入
    include:["GPU_AVAILABILITY"]
    ，会按数据中心返回相同的信息（
    list-data-centers
    还支持
    regions
    筛选参数）。根据请求的GPU类型和区域进行筛选，报告哪些数据中心有库存、哪些无库存，且所有名称均需来自查询结果。
  • 哪些CUDA版本有库存，或如何选择端点的
    allowedCudaVersions
    。     可用性查询可以回答这个问题：每种GPU类型的
    cudaVersions
    字段列出了有空闲容量的主机CUDA版本（可通过
    cudaVersions
    / 
    minCudaVersion
    参数筛选），
    get-capacity
    则以矩阵形式展示所有GPU类型的对应信息。这两个查询结果都不包含数据中心信息，因此绝不要从中推断数据中心；按数据中心的查询结果来自可用性查询的
    dataCenters
    字段。

Sizing a model to a GPU

模型适配GPU的规格估算

Estimate required VRAM from the model's real parameters and precision, then name the smallest GPU that fits with stock. Rough VRAM: weights ≈ params(B) × bytes-per-param (fp16 → 2, int8 → 1, int4/awq/gptq → 0.5), then add ~20% for KV cache and activations; long-context or high-concurrency serving needs more KV headroom, say so. Read the GPU catalog and recommend the smallest class whose memory clears that estimate and shows real stock, plus the next size up as headroom. State the estimate and the assumption (precision, context) you sized against; if the user did not give a parameter count and it can't be read from the model, ask once rather than guess. A which-card-at-what-price question is this journey: read the catalog now and state the recommended card's hourly price and its current stock as a definite fact from the availability read. Never end on an offer to "check live capacity later" or hedge the stock with "should be available"; the stock read is one call, so make it.
根据模型的实际参数量和精度估算所需VRAM，然后找出有库存的最小适配GPU。VRAM粗略计算公式：权重大小 ≈ 参数量（B） × 每个参数占用字节数（fp16 → 2，int8 → 1，int4/awq/gptq → 0.5），再加上约20%的KV缓存和激活值占用；如果是长上下文或高并发服务场景，需要更多KV预留空间，需明确说明。读取GPU目录，推荐显存满足估算需求且有实际库存的最小GPU类型，同时再推荐高一档的型号作为预留空间。说明估算结果以及你所基于的假设（精度、上下文长度）；如果用户没有提供参数量，且无法从模型信息中读取，则询问一次，不要猜测。如果用户问“哪种显卡什么价格”，就走这个流程：立即读取目录，明确说明推荐显卡的小时单价以及从可用性查询中得到的当前库存的确切事实。绝不要以“稍后再查实时容量”收尾，也不要用“应该有货”这类模糊表述来搪塞库存问题；库存查询只需要一次调用，直接执行即可。

Prebuilt workers on the Hub

Hub上的预构建工作器

Browse the Hub for a prebuilt worker matching the task (search by term and category: language, image, audio, video, embedding). Name the repos the catalog returned, their owner, and what each is for; if the config is available, note the hardware it wants. Attribute every repo to the listing; never assert a repo from memory. If nothing matches, say "none found on the Hub" and stop; the deploy path from a found repo is 
serverless-deploy
.
在Hub中浏览匹配任务的预构建工作器（按关键词和类别搜索：语言、图像、音频、视频、embedding）。列出目录返回的代码仓库、所有者以及各自的用途；如果配置可用，注明其所需的硬件。所有仓库信息都必须来自列表，绝不要凭记忆编造仓库。如果没有匹配项，返回“Hub上未找到”并停止；从找到的仓库进行部署的流程是
serverless-deploy
。

Ready-made public template

现成的公共模板

Browse the public template catalog when the user asks what image or template already exists for a workload. 
source
picks the slice: 
official
(Runpod-curated, the default), 
verified
(community, Runpod-verified), 
community
(everything else shared publicly). Each entry's 
serverless
flag says whether it is a pod or a serverless template; say which slice you searched and which kind you are naming. Name only templates the read returned, with their id, and stop at "none found" rather than inventing one. The catalog returns at most 100 templates and does not paginate, so say the listing is capped rather than implying it is exhaustive. Deploying from one is 
pod-deploy
or 
serverless-deploy
; the user's own templates are 
lifecycle-crud
.
当用户询问某个工作负载已有哪些镜像或模板时，浏览公共模板目录。
source
参数用于选择分类：
official
（Runpod官方策划，默认值）、
verified
（社区贡献，Runpod验证）、
community
（其他所有公开共享的模板）。每个条目的
serverless
标志表示它是pod模板还是serverless模板；需说明你搜索的是哪个分类，以及列出的是哪种类型的模板。仅列出查询返回的模板及其ID，若无匹配则返回“未找到”，不要编造模板。目录最多返回100个模板，且不支持分页，因此需说明列表有数量上限，不要暗示结果是详尽无遗的。从模板部署的工具是
pod-deploy
或
serverless-deploy
；用户自己的模板由
lifecycle-crud
管理。

Pay-per-use, no deployment

按使用付费，无需部署

When the user wants a one-off result ("run this model once on my input") and does not want to run infrastructure, read the public-endpoints catalog: managed, pay-per-use model APIs (text, image, video, audio) that need no deployment. Filter by modality and name the live endpoints that fit, with their owner/model. This is often the right answer to a one-off request; recommend it over standing up an endpoint. If the catalog has nothing for the modality, say so, but NEVER end on the gap. Name the closest real path that does exist (usually deploying a Hub worker for that modality on serverless, clearly labeled as a deploy, not pay-per-use) so the user leaves with an option. Do not present a deployable worker as if it were pay-per-use.
当用户只需要一次性结果（“用我的输入运行一次这个模型”）且不想运维基础设施时，读取public-endpoints目录：无需部署的托管式按使用付费模型API（文本、图像、视频、音频）。按模态筛选，列出符合要求的可用端点及其所有者/模型。对于一次性请求，这通常是最优解；优先推荐这种方式，而不是搭建一个端点。如果目录中没有对应模态的资源，说明情况，但绝不能只说没有就结束。给出确实存在的最接近的可行路径（通常是在serverless上部署该模态的Hub工作器，需明确标注这是部署方案，而非按使用付费），让用户有可选方案。不要把可部署的工作器当作按使用付费的服务来介绍。

Hard rules

硬性规则

  • Never call a mutating tool from this skill. Discovery reads; it does not create, update, stop, or delete anything.
  • Name only entries the tool returned. No invented GPU classes, regions, prices, Hub repos, or public endpoints.
  • Quote price and stock from the output. No arithmetic on prices the tool didn't return, no "should be around" figures.
  • Report empty results as empty: "none found" is a correct answer, and better than a plausible fabrication.
  • Sizing is a recommendation with its assumption stated, not a deployment. Fitting a model is 
    serverless-deploy
    's job.
  • Filter to the prompt's real constraint; do not dump the whole catalog when the user asked about one tier or one region.
  • Keep the answer internally consistent: a summary line about stock must agree with the per-data-center table it summarizes; when two reads disagree, name the more recent one instead of contradicting yourself.
  • 绝不要从本技能调用会产生变更的工具。资源发现是只读操作，不创建、更新、停止或删除任何内容。
  • 仅列出工具返回的条目。不得编造GPU类型、区域、价格、Hub仓库或公共端点。
  • 直接引用输出中的价格和库存。不得对工具未返回的价格进行计算，不得给出“大概在多少左右”的估算数字。
  • 空结果就如实报告为空：“未找到”是正确答案，好过看似合理的编造。
  • 规格估算是带有前提假设的建议，而非部署操作。模型适配部署是
    serverless-deploy
    的职责。
  • 根据用户提示的实际约束进行筛选；当用户只询问某一个层级或某一个区域时，不要列出整个目录的所有内容。
  • 保持答案内部一致：关于库存的总结行必须与其所汇总的各数据中心表格一致；如果两次查询结果不一致，以更新的一次为准，不要自相矛盾。

Error handling

错误处理

  • include:["AVAILABILITY"]
    without 
    product
    , or 
    product
    without the include → the API answers 400; send both, for the product the user will create.
  • A catalog read returns an empty list → report "none found" for that filter; do not widen the search silently or fabricate entries.
  • The user gives a VRAM tier no card matches → say no card in that tier is in the catalog (or has stock) rather than rounding to a nearby card unasked.
  • A read fails → report the failure and stop; do not answer from memory.
  • 只传
    include:["AVAILABILITY"]
    不传
    product
    ，或只传
    product
    不传include参数 → API会返回400错误；两个参数必须同时传入，且product值对应用户要创建的产品类型。
  • 目录查询返回空列表 → 针对该筛选条件报告“未找到”；不要悄悄扩大搜索范围，也不要编造条目。
  • 用户给出的VRAM层级没有匹配的显卡 → 说明该层级没有显卡在目录中（或没有库存），不要未经请求就就近推荐其他显卡。
  • 查询失败 → 报告失败并停止；不要凭记忆回答。

Tool binding

工具绑定

CapabilityTool
GPU catalog + price + stock
list-gpu-types
/ 
get-gpu-type
(
include:["AVAILABILITY"]
with 
product
)
Per-data-center GPU availabilitythe same read (
dataCenters[]
); 
list-data-centers
/ 
get-data-center
(
include:["GPU_AVAILABILITY"]
)
CUDA versions with capacity, per GPU typethe same read (
cudaVersions
); 
get-capacity
for the matrix view
Data centers
list-data-centers
, 
get-data-center
CPU catalog
list-cpu-types
, 
get-cpu-type
(
include:["AVAILABILITY"]
with 
product
)
Browse the Hub
list-hub-repos
(
searchTerm
, 
category
, 
includeConfig
)
Browse the public template catalog
list-public-templates
(
source
: 
official
/ 
verified
/ 
community
)
Pay-per-use Public Endpoints
list-public-endpoints
(
modality
, 
owner
)
If a tool named here is missing from the session's tool list, say so and use the nearest read instead of inventing a result.
能力工具
GPU目录 + 价格 + 库存
list-gpu-types
/ 
get-gpu-type
（需传入
include:["AVAILABILITY"]
和
product
参数）
各数据中心GPU可用性同上查询（
dataCenters[]
字段）；
list-data-centers
/ 
get-data-center
（需传入
include:["GPU_AVAILABILITY"]
参数）
每种GPU类型有可用容量的CUDA版本同上查询（
cudaVersions
字段）；矩阵视图使用
get-capacity
数据中心
list-data-centers
、
get-data-center
CPU目录
list-cpu-types
、
get-cpu-type
（需传入
include:["AVAILABILITY"]
和
product
参数）
浏览Hub
list-hub-repos
（参数：
searchTerm
、
category
、
includeConfig
）
浏览公共模板目录
list-public-templates
（
source
参数可选值：
official
/ 
verified
/ 
community
）
按使用付费的Public Endpoints
list-public-endpoints
（参数：
modality
、
owner
）
如果会话的工具列表中缺少此处提到的某个工具，请说明情况，并使用最接近的查询工具代替，不要编造结果。

Contract

约定

The cross-journey answer contract (definite facts from reads, commit-don't-hedge, mutations bind to this conversation's creations, a standalone final message, honest failure, granted tools only) is defined in the 
runpod-mcp
skill and applies to every reply from this journey. If the 
runpod-mcp
skill has not been loaded in this conversation, load it now, before the next tool call: its rules on which resources you may change, when a request is the go, and when to stop for the user are not repeated here.
跨查询流程的答复约定（查询所得的确切事实、明确承诺不模棱两可、变更操作仅绑定本次会话创建的资源、独立的最终消息、如实报告失败、仅使用已授权的工具）已在
runpod-mcp
技能中定义，适用于本流程的所有回复。如果本次会话中尚未加载
runpod-mcp
技能，请在下次工具调用前加载：关于你可修改哪些资源、何时可执行请求、何时需要停下询问用户的规则，此处不再赘述。