Which integrations are available for this server?

Enables the extraction of content from Bilibili videos to be processed into structured knowledge articles and summaries. Uses Google Gemini as a default AI provider to generate multi-layer summaries and structured knowledge articles from video transcripts. Uses OpenAI's AI models to generate multi-layer summaries and structured knowledge articles from video transcripts. Enables the extraction of content from YouTube videos to be processed into structured knowledge articles and summaries.

How do I use Open CLAW Knowledge Distiller?

This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.

Open CLAW Knowledge Distiller

by destinyfrancis

Overview Schema Source Related Servers Discussions Admin

Python

Local

Open CLAW Knowledge Distiller 🦞📚

龙虾知识蒸馏器 · 龍蝦知識蒸餾器

English · 繁體中文 · 简体中文

Turn YouTube, Bilibili, and Facebook videos into structured knowledge articles in seconds — locally, for free. 秒速将 YouTube、Bilibili、Facebook 视频转化为结构化知识文章 — 本地运行，完全免费。

English

What is Open CLAW Knowledge Distiller?

Open CLAW Knowledge Distiller（龍蝦知識蒸餾器，kd）is an open-source CLI tool and MCP server built for the Open CLAW AI agent ecosystem. It converts YouTube, Bilibili, and Facebook videos into structured knowledge articles — automatically, locally, and for free.

How it works:

If the video has subtitles → extracts them directly (no transcription needed, faster)
If no subtitles → downloads audio and transcribes locally with Qwen3-ASR MLX on Apple Silicon (no API key, no cloud cost)
Optionally generates a multi-layer AI summary: one-sentence essence + key points + cleaned transcript

Who is it for?

Researchers and students who need to digest hours of video content quickly
AI agent users (Claude Code / Open CLAW 龍蝦) who want to process videos programmatically
Anyone who wants structured notes from videos without watching them in full

Features

Feature	Details
🎙️ Local ASR	Qwen3-ASR MLX runs entirely on-device (Apple Silicon). No API key, no cloud, free forever.
📝 Smart subtitle detection	Auto-detects existing subtitles — skips ASR for faster processing
🤖 AI summarization	Supports Google Gemini, OpenAI, and Anthropic as summary providers
🎨 8 summary styles	Standard, Academic, Action List, News Brief, Investment Analysis, Podcast Digest, ELI5, Bullet Notes
🔌 MCP Server	Connect from Claude Code, Open CLAW, or any MCP-compatible AI agent
🌏 Multilingual	Cantonese (粵語), Mandarin, English, Japanese, Korean, and 50+ languages
⚡ Zero API key mode	`--no-summary`: pure local transcription, no external services needed

Installation

Prerequisites:

brew install ffmpeg    # audio extraction

Install:

pip install openclaw-knowledge-distiller
# or with uv:
uv add openclaw-knowledge-distiller

Qwen3-ASR model (~1-2 GB) downloads automatically from Hugging Face on first use.

Install from source (for development):

git clone https://github.com/destinyfrancis/openclaw-knowledge-distiller.git
cd openclaw-knowledge-distiller
uv sync

Quick Start

# ── No API key needed (100% local) ────────────────────────────────
kd process "https://youtube.com/watch?v=dQw4w9WgXcQ" --no-summary

# Cantonese video with dialect hint
kd process "https://youtube.com/watch?v=..." \
  --language yue \
  --asr-prompt "這是粵語口語對話，請保留懶音" \
  --no-summary

# ── With AI summary ────────────────────────────────────────────────
kd config set api-key "AIzaSy..."   # Google Gemini (default provider)
kd process "https://youtube.com/watch?v=..."

# Save as Markdown file
kd process "https://youtube.com/watch?v=..." --output notes.md

# ── Choose a summary style ─────────────────────────────────────────
kd process "https://youtube.com/watch?v=..." --style investment
kd process "https://youtube.com/watch?v=..." --style academic
kd process "https://youtube.com/watch?v=..." --style podcast
kd process "https://youtube.com/watch?v=..." --style eli5

# List all available styles
kd styles

# ── Other AI providers ─────────────────────────────────────────────
kd process "..." --provider openai --model gpt-4o-mini
kd process "..." --provider anthropic --model claude-haiku-4-5-20251001

Summary Styles

Run kd styles to list all styles. Choose with --style <key>:

Key		Name	Best For
`standard`	📋	Standard Summary	General videos (default)
`academic`	🎓	Academic Notes	Lectures, research talks, conference papers
`actions`	✅	Action List	Tutorials, how-to guides, step-by-step videos
`news`	📰	News Brief	Interviews, current events, news commentary
`investment`	📈	Investment Analysis	Finance, stocks, crypto, macro economics
`podcast`	🎙️	Podcast Digest	Conversations, talk shows, Q&A sessions
`eli5`	🧒	Explain Like I'm 5	Tech, science, academic topics for a general audience
`bullets`	⚡	Bullet Notes	Ultra-concise, fast scanning, quick reference

CLI Reference

`kd process <url>`

Full pipeline: detect subtitles → transcribe (if needed) → summarize.

Flag	Default	Description
`--language`, `-l`	auto-detect	Language code: `zh`, `yue` (Cantonese), `en`, `ja`, `ko`…
`--style`, `-s`	`standard`	Summary style preset (run `kd styles` to list all)
`--provider`, `-p`	`google`	AI provider: `google` \| `openai` \| `anthropic`
`--model`, `-m`	provider default	AI model name (e.g. `gemini-2.5-flash`, `gpt-4o-mini`)
`--prompt`	—	Custom summarization prompt (overrides `--style`)
`--output`, `-o`	stdout	Output file path
`--format`, `-f`	`markdown`	Output format: `markdown` \| `json` \| `text`
`--no-subtitles`	false	Always use ASR, skip subtitle detection
`--no-summary`	false	Transcript only — no AI, no API key needed
`--transcriber`	`qwen3-asr`	ASR backend: `qwen3-asr` \| `mlx-whisper`
`--model-size`	`1.7b`	Qwen3-ASR size: `1.7b` (accurate) \| `0.6b` (faster)
`--asr-prompt`	—	Context hint for ASR (e.g. dialect, domain, speaker style)

`kd styles`

List all built-in summary style presets.

`kd subtitles <url>`

Extract subtitles only — no ASR, no AI.

`kd config set <key> <value>`

Key	Example
`api-key`	`AIzaSy...`
`provider`	`google`, `openai`, `anthropic`
`model`	`gemini-2.5-flash`
`language`	`zh`
`transcriber`	`qwen3-asr`

`kd mcp-server`

Start the MCP server on stdio transport for Claude Code / Open CLAW.

MCP Server (Claude Code / Open CLAW)

Add to ~/.claude.json:

{
  "mcpServers": {
    "knowledge-distiller": {
      "command": "kd",
      "args": ["mcp-server"],
      "env": {
        "KD_API_KEY": "your-api-key-here",
        "KD_PROVIDER": "google"
      }
    }
  }
}

Available MCP Tools

Tool	Description
`process_url`	Submit a video URL → returns `job_id`. Supports `style`, `language`, `no_summary`, `model_size`…
`get_status`	Poll job progress: `status`, `progress` (0–1), `phase` message
`get_result`	Get result: `format=full` \| `summary` \| `transcript`
`list_jobs`	List all submitted jobs
`configure`	Update provider, model, default prompt

Typical Agent Workflow

Agent → process_url(url="https://youtube.com/watch?v=...", style="investment", language="zh")
      ← { "job_id": "a1b2c3d4" }

Agent → get_status(job_id="a1b2c3d4")
      ← { "status": "transcribing", "progress": 0.6, "phase": "Transcribing audio..." }

Agent → get_result(job_id="a1b2c3d4", format="summary")
      ← {
           "one_sentence": "核心投資論點...",
           "key_points": ["【投資論點】...", "【風險因素】..."]
         }

Configuration

Config file: ~/.config/knowledge-distiller/config.toml

provider = "google"
model = "gemini-2.5-flash"
language = "zh"
transcriber = "qwen3-asr"
default_prompt = ""

Environment variables (override config file):

export KD_PROVIDER=google
export KD_API_KEY=AIzaSy...
export KD_MODEL=gemini-2.5-flash
export KD_LANGUAGE=zh

System Requirements

Python 3.11+
macOS with Apple Silicon (M1/M2/M3/M4) — required for Qwen3-ASR and mlx-whisper local inference
ffmpeg: brew install ffmpeg
qwen-asr: pip install qwen-asr
mlx-whisper: pip install mlx-whisper (alternative ASR backend)

Related MCP server: video-toolkit-mcp

繁體中文

Back to English

什麼是龍蝦知識蒸餾器？

Open CLAW Knowledge Distiller（龍蝦知識蒸餾器，kd）係一個專為 Open CLAW（龍蝦）AI agent 生態系統而設計的開源命令行工具同 MCP 伺服器，可以自動將 YouTube、Bilibili 同 Facebook 影片轉化為結構化知識文章。

處理流程：

若影片有字幕 → 直接提取（無需 ASR 轉錄，速度更快）
若無字幕 → 下載音頻，用 Qwen3-ASR MLX 本地轉錄（Apple Silicon，無需 API Key，零費用）
可選：用 AI 生成多層摘要（一句精華 + 要點列表 + 修正轉錄）

適合誰使用？

需要快速消化大量影片內容的研究者和學生
使用 Claude Code / Open CLAW（龍蝦）的 AI agent 用戶
想從影片獲取結構化筆記而無需完整觀看的人

主要功能

功能	說明
🎙️ 本地 ASR	Qwen3-ASR MLX 完全在設備上運行（Apple Silicon），無 API 費用，永久免費
📝 智能字幕偵測	自動偵測並提取現有字幕，有字幕就跳過 ASR，速度更快
🤖 AI 摘要	支援 Google Gemini、OpenAI、Anthropic
🎨 8 種摘要風格	標準、學術、行動清單、新聞速報、投資分析、播客速覽、深入淺出、極簡子彈
🔌 MCP 伺服器	可從 Claude Code、Open CLAW 或任何 MCP 相容 AI agent 連接
🌏 多語言	粵語、普通話、英語、日語、韓語及 50+ 種語言
⚡ 零 API Key 模式	`--no-summary`：純本地轉錄，無需任何外部服務

安裝

brew install ffmpeg   # 音頻提取工具

pip install openclaw-knowledge-distiller
# 或使用 uv：
uv add openclaw-knowledge-distiller

Qwen3-ASR 模型（約 1-2 GB）首次使用時自動從 Hugging Face 下載，無需手動操作。

從原始碼安裝（開發用）：

git clone https://github.com/destinyfrancis/openclaw-knowledge-distiller.git
cd openclaw-knowledge-distiller
uv sync

快速開始

# ── 無需 API Key（完全本地）──────────────────────────────────────
kd process "https://youtube.com/watch?v=..." --no-summary

# 粵語影片
kd process "https://youtube.com/watch?v=..." \
  --language yue \
  --asr-prompt "這是粵語口語對話，請保留懶音" \
  --no-summary

# ── 使用 AI 摘要（需要 API Key）──────────────────────────────────
kd config set api-key "AIzaSy..."   # 設定 Google Gemini（預設）
kd process "https://youtube.com/watch?v=..."

# 儲存為 Markdown
kd process "https://youtube.com/watch?v=..." --output notes.md

# ── 選擇摘要風格 ───────────────────────────────────────────────────
kd process "https://youtube.com/watch?v=..." --style investment   # 投資分析
kd process "https://youtube.com/watch?v=..." --style academic     # 學術筆記
kd process "https://youtube.com/watch?v=..." --style podcast      # 播客速覽
kd process "https://youtube.com/watch?v=..." --style eli5         # 深入淺出
kd process "https://youtube.com/watch?v=..." --style bullets      # 極簡子彈

# 列出所有可用風格
kd styles

8 種摘要風格

執行 kd styles 查看完整列表，使用 --style <key> 選擇：

Key		名稱	最適合
`standard`	📋	標準摘要	一般影片（預設）
`academic`	🎓	學術筆記	學術演講、研究討論、學術報告
`actions`	✅	行動清單	教程、How-to、步驟指引
`news`	📰	新聞速報	訪談、時事、新聞評論
`investment`	📈	投資分析	財經、股票、加密貨幣、宏觀經濟
`podcast`	🎙️	播客速覽	對話、訪問、脫口秀
`eli5`	🧒	深入淺出	科技、科學、複雜主題
`bullets`	⚡	極簡子彈	極速瀏覽、快速筆記

CLI 參考

`kd process <url>`

旗標	預設值	說明
`--language`, `-l`	自動偵測	語言代碼：`zh`、`yue`（粵語）、`en`、`ja`、`ko`…
`--style`, `-s`	`standard`	摘要風格（執行 `kd styles` 查看全部）
`--provider`, `-p`	`google`	AI 供應商：`google` \| `openai` \| `anthropic`
`--model`, `-m`	供應商預設	AI 模型名稱（例如 `gemini-2.5-flash`）
`--prompt`	—	自訂摘要 prompt（覆蓋 `--style`）
`--output`, `-o`	標準輸出	輸出檔案路徑
`--format`, `-f`	`markdown`	輸出格式：`markdown` \| `json` \| `text`
`--no-subtitles`	false	跳過字幕偵測，強制使用 ASR
`--no-summary`	false	純轉錄模式，無需 AI，無需 API Key
`--transcriber`	`qwen3-asr`	ASR 引擎：`qwen3-asr` \| `mlx-whisper`
`--model-size`	`1.7b`	Qwen3-ASR 模型大小：`1.7b`（高精度）\| `0.6b`（更快）
`--asr-prompt`	—	ASR 上下文提示（例如方言、領域、語氣）

MCP 伺服器配置（Claude Code / Open CLAW 龍蝦）

在 ~/.claude.json 加入：

{
  "mcpServers": {
    "knowledge-distiller": {
      "command": "kd",
      "args": ["mcp-server"],
      "env": {
        "KD_API_KEY": "你的 API Key",
        "KD_PROVIDER": "google"
      }
    }
  }
}

典型 Agent 工作流程

Agent → process_url(url="https://youtube.com/watch?v=...", style="investment", language="zh")
      ← { "job_id": "a1b2c3d4" }

Agent → get_status(job_id="a1b2c3d4")
      ← { "status": "transcribing", "progress": 0.6 }

Agent → get_result(job_id="a1b2c3d4", format="summary")
      ← {
           "one_sentence": "核心投資論點...",
           "key_points": ["【投資論點】...", "【風險因素】..."],
           "full_transcript": "..."
         }

系統需求

Python 3.11+
macOS Apple Silicon（M1/M2/M3/M4）— Qwen3-ASR MLX 本地推理必需
ffmpeg：brew install ffmpeg
qwen-asr：pip install qwen-asr

简体中文

回到英文 · 回到繁體中文

什么是龙虾知识蒸馏器？

Open CLAW Knowledge Distiller（龙虾知识蒸馏器，kd）是一款专为 Open CLAW AI 智能体生态系统设计的开源命令行工具和 MCP 服务器。它能自动将 YouTube、Bilibili 和 Facebook 视频转化为结构化知识文章，完全本地运行，无需任何云端费用。

工作流程：

若视频有字幕 → 直接提取（最快，无需转录）
若无字幕 → 下载音频，用 Qwen3-ASR MLX 在本地转录（Apple 芯片，无需 API 密钥）
将转录文本和风格提示词返回给 Open CLAW，由智能体自行完成摘要生成

核心设计理念： kd 只负责下载和转录这两件重活，摘要生成交给龙虾自己的 AI 来完成——无需额外的 AI API 密钥。

主要功能

功能	说明
🎙️ 本地 ASR	Qwen3-ASR MLX 完全在设备上运行（Apple 芯片），无 API 费用，永久免费
📝 智能字幕检测	自动检测并提取现有字幕，有字幕直接跳过 ASR，速度更快
🤖 智能体摘要	返回转录文本和提示词，由 Open CLAW 自身 AI 完成摘要，无需额外 API 密钥
🎨 8 种摘要风格	标准、学术、行动清单、新闻速报、投资分析、播客速览、深入浅出、极简子弹
🔌 MCP 服务器	可从 Claude Code、Open CLAW 或任何兼容 MCP 的 AI 智能体连接
🌏 多语言支持	粤语、普通话、英语、日语、韩语及 50+ 种语言
⚡ 零 API 密钥模式	`--no-summary`：纯本地转录，无需任何外部服务

安装

brew install ffmpeg   # 音频提取工具

pip install openclaw-knowledge-distiller
# 或使用 uv（推荐）：
uv add openclaw-knowledge-distiller

Qwen3-ASR 模型（约 1-2 GB）首次使用时自动从 Hugging Face 下载，无需手动操作。

快速上手

# ── 零 API 密钥，纯本地转录 ─────────────────────────────────────
# 直接转录，输出文本
kd process "https://www.bilibili.com/video/BV..." --no-summary

# 指定普通话
kd process "https://www.bilibili.com/video/BV..." \
  --language zh \
  --no-summary

# 指定粤语（广东话）
kd process "https://youtube.com/watch?v=..." \
  --language yue \
  --asr-prompt "这是粤语口语对话，请保留原有发音特色" \
  --no-summary

# ── 配置 AI 摘要（可选，需要 API 密钥）───────────────────────────
kd config set api-key "AIzaSy..."       # 设置 Google Gemini（默认）
kd process "https://youtube.com/watch?v=..."

# 保存为 Markdown 文件
kd process "https://youtube.com/watch?v=..." --output 笔记.md

# ── 选择摘要风格 ───────────────────────────────────────────────────
kd process "https://youtube.com/watch?v=..." --style investment   # 投资分析
kd process "https://youtube.com/watch?v=..." --style academic     # 学术笔记
kd process "https://youtube.com/watch?v=..." --style actions      # 行动清单
kd process "https://youtube.com/watch?v=..." --style podcast      # 播客速览
kd process "https://youtube.com/watch?v=..." --style eli5         # 深入浅出
kd process "https://youtube.com/watch?v=..." --style bullets      # 极简子弹

# 查看所有可用风格
kd styles

8 种摘要风格

使用 kd styles 查看完整列表，通过 --style <key> 选择：

Key		名称	最适合
`standard`	📋	标准摘要	一般视频（默认）
`academic`	🎓	学术笔记	学术演讲、研究报告、学术会议
`actions`	✅	行动清单	教程、操作指南、步骤说明
`news`	📰	新闻速报	采访、时事评论、新闻报道
`investment`	📈	投资分析	财经、股市、加密货币、宏观经济
`podcast`	🎙️	播客速览	对话节目、访谈、脱口秀
`eli5`	🧒	深入浅出	科技、科学、复杂专业主题
`bullets`	⚡	极简子弹	快速浏览、会议记录、备忘

CLI 参考

`kd process <url>`

完整流程：检测字幕 → 转录（如需）→ 生成摘要。

参数	默认值	说明
`--language`, `-l`	自动检测	语言代码：`zh`、`yue`（粤语）、`en`、`ja`、`ko`…
`--style`, `-s`	`standard`	摘要风格（运行 `kd styles` 查看全部）
`--provider`, `-p`	`google`	AI 提供商：`google` \| `openai` \| `anthropic`
`--model`, `-m`	提供商默认	AI 模型名称（如 `gemini-2.5-flash`）
`--prompt`	—	自定义摘要提示词（覆盖 `--style`）
`--output`, `-o`	标准输出	输出文件路径
`--format`, `-f`	`markdown`	输出格式：`markdown` \| `json` \| `text`
`--no-subtitles`	false	跳过字幕检测，强制使用 ASR
`--no-summary`	false	纯转录模式，无需 AI，无需 API 密钥
`--transcriber`	`qwen3-asr`	ASR 引擎：`qwen3-asr` \| `mlx-whisper`
`--model-size`	`1.7b`	Qwen3-ASR 模型大小：`1.7b`（高精度）\| `0.6b`（更快）
`--asr-prompt`	—	ASR 上下文提示（如方言特征、专业领域等）

`kd styles`

列出所有内置摘要风格及其提示词。

`kd subtitles <url>`

仅提取字幕，不进行 ASR 或 AI 摘要。

`kd config set <key> <value>`

Key	示例
`api-key`	`AIzaSy...`
`provider`	`google`, `openai`, `anthropic`
`model`	`gemini-2.5-flash`
`language`	`zh`
`transcriber`	`qwen3-asr`

MCP 服务器配置（Open CLAW / Claude Code）

MCP 工具说明

工具	说明
`transcribe_url` ⭐	推荐：返回转录文本和摘要提示词，由 Open CLAW 自行完成摘要
`list_styles`	获取所有摘要风格的完整提示词
`process_url`	完整流程（需配置外部 AI API 密钥）
`get_status`	查询 process_url 任务进度
`get_result`	获取已完成任务的结果
`list_jobs`	列出所有任务

典型 Open CLAW 工作流程

# 第一步：获取转录和提示词
龙虾 → transcribe_url(url="https://www.bilibili.com/video/BV...", style="investment", language="zh")
     ← {
          "transcript": "今天我们来聊一下...",
          "suggested_prompt": "你是一位资深投资分析师...",
          "transcript_source": "qwen3-asr"  // 或 "subtitles"
        }

# 第二步：龙虾用自己的 AI + suggested_prompt 生成结构化摘要
# 无需任何额外 API 调用，零额外成本

系统要求

Python 3.11+
macOS Apple 芯片（M1/M2/M3/M4）— Qwen3-ASR MLX 本地推理必需
ffmpeg：brew install ffmpeg
Qwen3-ASR 模型会在首次使用时自动下载（约 1-2 GB）

Acknowledgements · 致謝

This project stands on the shoulders of remarkable open-source work. We are deeply grateful to the following teams and individuals:

Project	Authors	Contribution
Qwen3-ASR	Alibaba Qwen Team 阿里巴巴 Qwen 團隊	The core ASR model powering local transcription. World-class multilingual speech recognition including Cantonese, Mandarin, and 50+ languages.
Apple MLX	Apple Machine Learning Research	The on-device ML framework enabling Qwen3-ASR to run efficiently on Apple Silicon.
mlx-community	MLX Community Contributors	Quantized MLX model weights hosted on Hugging Face, making local inference accessible.
yt-dlp	yt-dlp contributors	Robust YouTube, Bilibili, and Facebook audio download and subtitle extraction without requiring any API key.
mlx-whisper	Apple MLX Examples Team	Alternative Apple Silicon ASR backend using OpenAI's Whisper architecture.
Pydantic	Samuel Colvin & contributors	Data validation and modelling powering all internal data structures.
Typer	Sebastián Ramírez (tiangolo)	The elegant CLI framework behind the `kd` command interface.
Rich	Will McGugan & Textualize	Beautiful terminal output, progress bars, and formatted tables.
MCP Python SDK	Anthropic & MCP contributors	The Model Context Protocol SDK enabling Claude Code / Open CLAW agent integration.
httpx	Tom Christie & encode	Async HTTP client powering AI provider API calls.

特別感謝 阿里巴巴 Qwen 團隊開發並開源 Qwen3-ASR 模型，令本地、免費、高精度的粵語及多語言轉錄成為可能。同時感謝 yt-dlp 團隊提供強大的音頻下載同字幕提取功能，支援 YouTube、Bilibili 同 Facebook 影片。

Special thanks to the Alibaba Qwen Team for developing and open-sourcing the Qwen3-ASR model, making high-accuracy local speech recognition in Cantonese and 50+ languages possible without any cloud cost. Also grateful to the yt-dlp community for robust audio download and subtitle extraction supporting YouTube, Bilibili, and Facebook videos.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Contributors

Avatar	Name	Role
	destinyfrancis	Creator & Maintainer

License

Powered by Qwen3-ASR · yt-dlp · Apple MLX · MCP

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Open CLAW Knowledge Distiller 🦞📚

龙虾知识蒸馏器 · 龍蝦知識蒸餾器

English

What is Open CLAW Knowledge Distiller?

Features

Installation

Quick Start

Summary Styles

CLI Reference

kd process <url>

kd styles

kd subtitles <url>

kd config set <key> <value>

kd mcp-server

MCP Server (Claude Code / Open CLAW)

Available MCP Tools

Typical Agent Workflow

Configuration

System Requirements

繁體中文

什麼是龍蝦知識蒸餾器？

主要功能

安裝

快速開始

8 種摘要風格

CLI 參考

kd process <url>

MCP 伺服器配置（Claude Code / Open CLAW 龍蝦）

典型 Agent 工作流程

系統需求

简体中文

什么是龙虾知识蒸馏器？

主要功能

安装

快速上手

8 种摘要风格

CLI 参考

kd process <url>

kd styles

kd subtitles <url>

kd config set <key> <value>

MCP 服务器配置（Open CLAW / Claude Code）

推荐工作流程（龙虾自行摘要）

MCP 工具说明

典型 Open CLAW 工作流程

系统要求

Acknowledgements · 致謝

Contributing

Contributors

License

Tool Schema Changelog

Maintenance

Resources

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V2W MCP Server

mcp-youtube

`kd process <url>`

`kd styles`

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`kd config set <key> <value>`

`kd mcp-server`

`kd process <url>`

`kd process <url>`

`kd styles`

`kd subtitles <url>`

`kd config set <key> <value>`