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voice-ai-integration语音 AI 集成

Agent Skill

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

总安装

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周安装

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公开资料未说明

下载量

3,469
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:voice-ai-integration(语音 AI 集成)
来源仓库:https://github.com/hugochaan/voice-ai-integration
安装命令:
openclaw skills install voice-ai-integration
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install voice-ai-integration

简介

用于集成声网 ConvoAI、RTC 音视频等产品能力。

  • 支持语音代理、消息推送与云录音功能调用。
  • 适合实时通信与语音交互类应用开发。
  • 需确认服务地域可用性与计费规则。voice-ai-integration 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 涉及语音数据时请遵循隐私保护规范。

SKILL.md

name
voice-ai-integration
description
|
license
MIT
metadata
author
shengwang
version
1.0.0

Shengwang Integration

Workflow

Step 0: Ensure doc index exists (MANDATORY)

⚠️ Execute this BEFORE any routing or code generation.

Check if references/docs.txt already exists. If it does, skip this step entirely. If it does not exist, download it:

bash skills/voice-ai-integration/scripts/fetch-docs.sh

This downloads a static doc index from doc.shengwang.cn — no user data is sent. If download fails, proceed with local reference docs and fallback URLs.

Step 1: Route to the correct product module

Match the user's request to a product module using the route table. If the match is clear, route directly — do not ask extra questions.

Route Table

User intentRoute to
Credentials, AppID, REST authgeneral
Generate Token, token server, AccessToken2token-server
ConvoAI voice agent workconversational-ai
RTC SDK integrationrtc
RTM messaging / signalingrtm
Cloud Recordingcloud-recording
Download SDK, sample project, GitHub repoRoute to the relevant product module above

Product Recognition Aid

When the user describes a use case without naming a product, use this to infer the match:

ProductWhat it doesTypical user says
ConvoAIAI voice agent (ASR→LLM→TTS over RTC)"AI语音", "voice bot", "对话式AI", "AI agent", "AI 客服"
RTC SDKReal-time audio/video between humans"视频通话", "直播", "video call", "live streaming"
RTMReal-time messaging / signaling"聊天", "消息", "chat", "signaling"
Cloud RecordingRecord RTC sessions server-side"录制", "recording", "存档", "回看"
Token generationGenerate RTC / RTM tokens"token", "鉴权", "token server"

Common Combinations

Use caseProducts needed
AI voice assistantConvoAI (primary) + RTC SDK (client)
AI voice assistant + chat historyConvoAI + RTC SDK + RTM
1v1 / group video callRTC SDK
Video call + chatRTC SDK + RTM
Live streaming with recordingRTC SDK + Cloud Recording
Record AI conversationsConvoAI + RTC SDK + Cloud Recording
Chat / messaging onlyRTM

Routing Rules

  • Infer obvious context — do not ask if the answer is already clear
  • Do not ask product-specific configuration questions (providers, SDK versions, project structure) at this level; let the product module handle those
  • If the product is clear but the request mode is ambiguous (quickstart vs debugging vs feature), let the product module decide internally
  • If multiple products are needed, route to the primary product first, then address supporting products in order
  • ConvoAI has the most detailed internal routing (see its README.md); always delegate ConvoAI-specific decisions to its module

When the product is still unclear

If the route table and recognition aid above are not enough to determine the product:

  1. Ask only for the missing detail that would change the routing decision
  2. Ask at most one question at a time
  3. Prefer natural wording over an interview script
  4. Once the product is clear, produce a short routing recap and continue:

ZH:

已了解的信息
─────────────────────────────
场景:          [use case]
主要产品:      [primary product]
配套产品:      [supporting products / 无]
─────────────────────────────

EN:

What I have so far
─────────────────────────────
Use case:       [use case]
Primary:        [primary product]
Supporting:     [supporting products / none]
─────────────────────────────

Do not stop for a separate confirmation step — continue to the product module automatically.

Step 2: Let the product module drive implementation

Each product module follows its own workflow. Do not duplicate implementation logic here.

Common pattern across modules:

  1. Use local reference docs in references/ first
  2. Fetch remote docs via doc-fetching.md only when local references are insufficient
  3. Fallback to web search only after doc fetching has been attempted

Runtime Requirements

  • bash and curl for local doc-fetch helper scripts
  • git for sample-repo inspection when the sample-aligned path is chosen
  • Network access to doc.shengwang.cn, doc-mcp.shengwang.cn, and gitee.com

Network behavior:

  • fetch-docs.sh downloads a static file from doc.shengwang.cn/llms.txt — no user data is sent
  • fetch-doc-content.sh fetches a single doc page by URI from doc-mcp.shengwang.cn — only the doc URI is sent, no user context
  • git clone is used only for sample repo inspection from gitee.com — only the repo URL is sent

Credential and service-activation requirements vary by product — see each product module and general/credentials-and-auth.md for details. Never hardcode credentials.

Safety & Consent Rules

  • Do not clone external repos into the user's main workspace by default — prefer a temporary path
  • Do not modify an existing user project until the user explicitly asks for code generation
  • Do not write secrets into project files — prefer env vars and example placeholders
  • Before performing network fetches or repo clones, state what will be downloaded
  • If a required dependency or credential is missing, stop and explain the blocker

Download Rules

  • Use git clone --depth 1 <url> with HTTPS repo root URLs only
  • On any download failure: report the error, provide the URL for manual download, never silently skip

Links

  • Console: https://console.shengwang.cn/
  • Docs (CN): https://doc.shengwang.cn/
  • GitHub: https://github.com/Shengwang-Community

适合场景

01

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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按下载量换算2,905

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external-service

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