跳到正文
cccskills
免费在 GitHub 公开中文介绍

add-model-provider

本技能指导在 LobeHub 中接入 AI 模型提供商,涵盖运行时、模型信息、设置、品牌展示、文档和验收,并帮助判断新增模型是否需要新的提供商集成。

原文查看原文说明

Add an AI model provider to LobeHub, including runtime integration, model cards, configuration, branding, documentation, and acceptance. Also use for provider documentation updates; adding models to an existing provider does not require a new provider integration.

lobehub/lobehub星标 83,504(GitHub 83,504 + cccskills 0)

实测评分 未实测

查看安装方法

适用场景

  • 为 LobeHub 接入新的 AI 模型提供商
  • 更新提供商设置说明或介绍
  • 判断添加模型是否需要新的提供商集成

中文介绍

功能

本技能指导将 AI 模型提供商接入 LobeHub,从设置配置一直到运行时调用。内容包括登记提供商和模型、配置认证与连接地址、确认支持的 API 方式,以及核对各模型的功能、价格和上下文上限。它还涵盖运行时接线、设置项、图标、显示顺序,以及英文和中文说明文案。模型功能与价格应依据官方公告或 API 资料核实;无法确认的值按未知处理,不作推测。技能也包括根据提供商的调用路径进行测试和验收。

适用场景

添加 LobeHub 尚未支持的提供商,或更新现有提供商的设置和使用文档时,可以使用本技能。若只是给已有提供商添加模型,应遵循单独的模型管理流程。

使用示例

  • “请将使用这个 API 的提供商接入 LobeHub。”
  • “请根据实现情况更新这个提供商的设置说明。”
  • “请确认添加这个模型是否需要新的提供商集成。”

注意事项

资料核对会参考官方公告和 API 文档。托管模型的配置属于另一套接入流程。没有可用的实时认证信息时,实际推理调用路径应标记为尚未验证。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

文档检查

AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。

安全性发送数据、删除文件、执行命令等是否有说明
A良好未知值不作推测;缺少实时凭据时将调用路径标为未验证。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足涉及官方资料核查和认证信息,但资料未完整说明安装条件。

不计入评分。 由 AI(OpenAI Codex)于 2026年10月26日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(5)

  • SKILL.md11.0 KB
  • agents/openai.yaml43 B
  • references/documentation.md4.0 KB
  • references/integration-map.md16.8 KB
  • references/local-providers.md6.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Add a Model Provider

Deliver a provider that users can configure, recognize, and use through the supported API paths. This skill includes the provider documentation workflow.

Paths below are relative to the open-source repository root. In a Cloud checkout, prefix them with lobehub/ and follow the parent repository's install and Git rules. Hosted model configuration is a separate workflow: use the deployment operator's model-onboarding process when that is the requested target. Do not copy private deployment settings or routing information into this skill or an OSS change.

For a documentation-only request, inspect the implemented configuration and follow Provider documentation; do not add runtime code merely to satisfy this checklist.

For a model-only request on an existing provider, follow model-bank-metadata for knowledgeCutoff, family, and generation, using its single-model guidance rather than the repo-wide sweep. Use the research and focused validation below for other model-card fields, including capabilities, pricing, and context limits. Skip new provider registration, authentication configuration, and provider-list reordering unless the requested model exposes a confirmed gap in those paths.

Establish the Integration Contract

  • Distinguish a new provider from a model added to an existing provider. Record the stable provider ID, display name, model IDs, authentication method, endpoint, and supported API modes. File/export casing may differ from the lowercase provider ID; follow actual consumers.
  • Read the official announcement, API reference, model catalog, pricing, and migration notes. Record source URLs and access dates for capabilities, constraints, and pricing. Search for announcements as well as documentation: parameter changes often appear only in release notes. Use a rendered browser when a fetched page is an empty client-rendered shell.
  • Verify the endpoint actually being integrated. A model's advertised capabilities do not prove that a compatible endpoint supports its search parameters, tools, images, or structured output. Do not describe a third-party endpoint or its credentials as the provider's official API.
  • For local servers or desktop applications, read Local provider integration. Verify launch modes, installed-model discovery, browser access, and capability refresh separately; an OpenAI-compatible model list alone does not establish these behaviors.
  • Compare the latest provider addition and the closest existing implementation by protocol and authentication. Read their diffs, not just file lists. Integration map and precedents identifies the registration chain and contrasting examples; historical omissions are not requirements to repeat.
  • Set model metadata from evidence: exact IDs, family/generation, context and output limits, release/cutoff dates when known, capabilities, extend parameters, search behavior, pricing currency and units. Distinguish API usage prices from subscription entitlements, cached input from ordinary input, and current promotions from list prices. Mark unknowns rather than inventing values; preserve existing supported models.

Implement the Provider

Use Integration map and precedents while tracing the request from settings through configuration to the runtime.

  1. Register the provider ID, provider card, separate model cards, model imports/map/exports, and package subpath export. Keep chatModels: [] on modern provider cards when the separate model bank is the source of truth.
  2. Set a usable static checkModel when the provider guarantees that model is available. Otherwise, omit it and let the user select and persist a connection-check model from the fetched local list; do not put an arbitrary example in the card. Set accurate authentication, model-fetching, SDK-family, and browser-request settings. A static model catalog may be correct when the API has no model-list endpoint. Trace consumers of both top-level and nested browser-request flags before relying on one of them.
  3. Reuse the appropriate runtime factory or adapter. Register the runtime and its public export. If the provider needs a distinct routed API adapter, wire the RouterRuntime API type and base-runtime map too; provider registration alone does not establish that path.
  4. Validate protocol selection separately for streaming chat, non-streaming chat, and structured generation. Responses-only providers may need both chat and generateObject configuration, plus protection against a caller selecting an unsupported mode. Map tools, search, response formats, and reasoning only where supported. Never enable a capability solely because a sibling provider does.
  5. Add authentication/configuration plumbing appropriate to the provider. For API-key providers, check both environment schema and runtime bindings, then the generic server configuration and runtime initialization. OAuth providers need their authorization, refresh, disconnect, and account-metadata paths instead of invented API-key variables.
  6. For a new user-facing parameter, trace the model-card type, stored chat configuration, controls, request resolver, and translations. Prefer existing parameter definitions when their semantics match. If reasoning state must survive multiple turns, test serialization and replay through the real stream/non-stream paths rather than only the initial request.

Keep changes to shared factories or error handlers conditional on an observed gap. The checklist is not a mandate to modify every listed file.

Branding, Ordering, and Documentation

  • Check provider and model icons independently in @lobehub/icons: the provider brand and model family can require different artwork. Inspect the actual model-ID matcher, including prefixes, case variants, and collisions with existing model names. When changing a matcher, verify the new IDs and preserve the older family's matches.
  • If an icon is missing, identify the missing asset/mapping and prepare an appropriate change or explicit fallback within the authorized scope. Do not silently use an unrelated brand. An external icon PR or release is a separate authorized action; verify that the consumer's installed release contains the intended mapping before claiming the UI is fixed.
  • Place the provider deliberately in DEFAULT_MODEL_PROVIDER_LIST; this array controls default list order, not imports or enum declarations. Apply these ordering principles:
    • Favor providers with greater community popularity toward the front. Use current evidence of adoption and ecosystem recognition rather than alphabetical order, implementation convenience, or an invented numeric popularity score.
    • Keep local model managers and inference servers together, such as Ollama, LM Studio, vLLM, and Unsloth Studio.
    • Keep router/aggregator services together: services exposing models from multiple upstream providers through a unified API, such as OpenRouter and AIHubMix. Using the internal RouterRuntime abstraction alone does not place a provider in this group.
    • Group continuity takes precedence over strictly sorting every individual provider by popularity. Use community popularity to position groups and standalone providers, and to order providers within each group. Preserve an explicit user-specified position and existing user-customized ordering.
    • Insert a new provider into its appropriate group without reordering unrelated providers. A requested broader ordering cleanup can move the affected peers together.
  • Follow Provider documentation for bilingual usage/setup instructions, environment references, existing Docker configuration, safe examples, and screenshots. Keep the public provider identity and actual credential/endpoint requirements consistent across UI and docs.
  • Add <provider-id>.description to locales/en-US/providers.json and locales/zh-CN/providers.json for the provider's description. The default namespace in packages/locales/src/default/providers.ts is derived from provider cards; do not duplicate the description there. Follow repository conventions for other new copy and verify fallback behavior. Avoid running a repository-wide translation job for a few provider keys.

Validate the User Path

Use the existing provider test harness (packages/model-runtime/src/providerTestUtils.ts) and focused package tests. Cover the supported behavior rather than asserting that registration strings exist:

  • Provider/model lookup resolves the new cards and runtime; connection checking uses a supported model.
  • Requests select the intended API mode and endpoint, carry the right authentication, transform supported parameters, and preserve tool calls and structured results. Exercise streaming and non-streaming paths when supported.
  • Missing/invalid credentials, failed model fetching, and connection errors produce useful localized messages rather than raw codes or translation keys. Use synthetic HTTP responses for legacy/invalid wire payloads instead of casting them into a stricter current response type.
  • Trigger a connection failure and expand its details: the error must remain recoverable within the settings page. A successful connection does not exercise lazy error components or their shared UI contexts.
  • The provider settings page and model row display the intended name, icon, capabilities, and default order in light/dark themes. Check English and Chinese where copy changed. Inspect both provider and model icons in the real UI.
  • When shared runtime errors or lazy namespaces change, check other affected surfaces such as Workbench. Its separate language bundle may need a namespace even when the main application already loads it.

Run the owning repository's quality command and relevant package tests. For an OSS PR developed inside Cloud, verify the OSS type-check/build context as well: a passing parent type-check does not establish that the standalone OSS configuration passes. Attribute workspace-resolution failures separately; do not remove valid checks to manufacture a pass.

Use the repository's acceptance workflow for authorized live checks. Do not read secret files, put credentials into logs/screenshots, invent screenshots, or claim a successful inference from mocks. If live credentials or access are unavailable, report the precise unverified path and the completed offline checks.

When delivery includes a PR, follow repository commit/PR workflows and monitor CI and deployment for the current head. Attribute failures, fix in-scope issues, validate, push, and continue until checks complete or a concrete blocker is reached. Preserve applicable independent-review gates and human-only merge rules. Report changed behavior, validation, evidence, and remaining limitations.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

acceptance

免费有简介

通过实际操作产品验证交付内容,记录用户可查看的证据,并发布验收报告。可检查已有验证计划或创建检查项,选择合适的操作界面,并使用 `lh` CLI 发布结果。

  • 实际检查更改后的产品行为
  • 收集截图或其他验证证据
  • 编写验证计划和报告
lobehub/lobehub8.4万2026年10月26日 更新

add-setting-env

免费有简介

本技能指导如何用服务器端环境变量设置用户偏好默认值,涵盖变量校验、服务器配置、写入用户状态,以及更新 `.env.example` 和自托管文档。

  • 通过环境变量设置用户偏好默认值
  • 将设置接入服务器配置和用户状态
  • 更新环境变量示例及文档
lobehub/lobehub8.4万2026年10月26日 更新

agent-runtime-hooks

免费有简介

说明如何在智能体执行步骤、工具调用、人工审批、上下文压缩和子智能体调用时注册钩子。钩子可观察事件、模拟本地工具结果,也可通过 Webhook 按配置策略允许或拒绝工具调用。

  • 记录智能体步骤和错误
  • 测试时模拟工具结果
  • 通过 Webhook 允许或拒绝工具调用
lobehub/lobehub8.4万2026年10月26日 更新

agent-signal

免费有简介

此技能用于为智能体设计事件驱动的后台工作流,划分来源、信号、动作和策略的职责,并处理异步交接、去重、执行范围、完成记录与运行观测。

  • 添加事件触发的后台任务
  • 将工作移出对话请求
  • 防止动作重复执行
lobehub/lobehub8.4万2026年10月26日 更新

agent-testing-bot

免费有简介

本技能在 macOS 桌面聊天应用中测试机器人频道:打开应用、进入频道或联系人、发送消息、等待回复并截图。支持 Discord、Slack、Telegram、微信、飞书和 QQ;iMessage 使用 BlueBubbles 桥接。

  • 检查真实 Discord 频道中的机器人回复
  • 测试 Slack 或 Telegram 的对话流程
  • 记录 macOS 应用中的操作结果
lobehub/lobehub8.4万2026年10月26日 更新

agent-tracing

免费有简介

按操作编号检查智能体运行记录,查看失败的工具调用、参数与结果、运行上下文及传给大语言模型的内容;还可查看上下文映射,并用其他模型重放记录以便比较。

  • 排查失败的工具调用
  • 确认传给大语言模型的上下文
  • 按操作追踪工具及结果
lobehub/lobehub8.4万2026年10月26日 更新

查看 lobehub 的全部技能

报告此技能的问题