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Tubescribe

Community
AlekseiUL
tubescribe

YouTube video summarizer with speaker detection, formatted documents, and audio output.

Overview

PublisherAlekseiUL
Repositorysprut-agent-kit
Skill nametubescribe
Stars
66
Forks
21
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by AlekseiUL on GitHub. Read the source before you install it.

Installation

Install the Tubescribe AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AlekseiUL/sprut-agent-kit/tree/main/skills/tubescribe
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AlekseiUL/sprut-agent-kit.git /tmp/sprut-agent-kit
mkdir -p .claude/skills
cp -r /tmp/sprut-agent-kit/skills/tubescribe .claude/skills/tubescribe
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tubescribe in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tubescribe on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tubescribe is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

metadata: { "claudeclaw": { "emoji": "🎬", "requires": { "bins": ["summarize"] } } }

TubeScribe 🎬

Turn any YouTube video into a polished document + audio summary.

Drop a YouTube link → get a beautiful transcript with speaker labels, key quotes, timestamps that link back to the video, and an audio summary you can listen to on the go.

💸 Free & No Paid APIs

  • No subscriptions or API keys — works out of the box
  • Local processing — transcription, speaker detection, and TTS run on your machine
  • Network access — fetching from YouTube (captions, metadata, comments) requires internet
  • No data uploaded — nothing is sent to external services; all processing stays on your machine
  • Safe sub-agent — spawned sub-agent has strict instructions: no software installation, no network calls beyond YouTube

✨ Features

  • 📄 Transcript with summary and key quotes — Export as DOCX, HTML, or Markdown
  • 🎯 Smart Speaker Detection — Automatically identifies participants
  • 🔊 Audio Summaries — Listen to key points (MP3/WAV)
  • 📝 Clickable Timestamps — Every quote links directly to that moment in the video
  • 💬 YouTube Comments — Viewer sentiment analysis and best comments
  • 📋 Queue Support — Send multiple links, they get processed in order
  • 🚀 Non-Blocking Workflow — Conversation continues while video processes in background

🎬 Works With Any Video

  • Interviews & podcasts (multi-speaker detection)
  • Lectures & tutorials (single speaker)
  • Music videos (lyrics extraction)
  • News & documentaries
  • Any YouTube content with captions

Quick Start

When user sends a YouTube URL:

  1. Spawn sub-agent with the full pipeline task immediately
  2. Reply: "🎬 TubeScribe is processing — I'll let you know when it's ready!"
  3. Continue conversation (don't wait!)
  4. Sub-agent notification will announce completion with title and details

DO NOT BLOCK — spawn and move on instantly.

First-Time Setup

Run setup to check dependencies and configure defaults:

bash
python skills/tubescribe/scripts/setup.py

This checks: summarize CLI, pandoc, ffmpeg, Kokoro TTS

Full Workflow (Single Sub-Agent)

Spawn ONE sub-agent that does the entire pipeline:

python
sessions_spawn(
    task=f"""
## TubeScribe: Process {youtube_url}

⚠️ CRITICAL: Do NOT install any software.
No pip, brew, curl, venv, or binary downloads.
If a tool is missing, STOP and report what's needed.

Run the COMPLETE pipeline — do not stop until all steps are done.

### Step 1: Extract
```bash
python3 skills/tubescribe/scripts/tubescribe.py "{youtube_url}"

Note the Source and Output paths printed by the script. Use those exact paths in subsequent steps.

Step 2: Read source JSON

Read the Source path from Step 1 output and note:

  • metadata.title (for filename)
  • metadata.video_id
  • metadata.channel, upload_date, duration_string

Step 3: Create formatted markdown

Write to the Output path from Step 1:

  1. # **<title>**

  1. Video info block — Channel, Date, Duration, URL (clickable). Empty line between each field.

  1. ## **Participants** — table with bold headers:
    | **Name** | **Role** | **Description** |
    |----------|----------|-----------------|

  1. ## **Summary** — 3-5 paragraphs of prose

  1. ## **Key Quotes** — 5 best with clickable YouTube timestamps. Format each as:
    "Quote text here." - [12:34](https://www.youtube.com/watch?v=ID&t=754s)
    
    "Another quote." - [25:10](https://www.youtube.com/watch?v=ID&t=1510s)
    Use regular dash -, NOT em dash —. Do NOT use blockquotes >. Plain paragraphs only.

  1. ## **Viewer Sentiment** (if comments exist)

  1. ## **Best Comments** (if comments exist) — Top 5, NO lines between them:
    Comment text here.
    
    *- ▲ 123 @AuthorName*
    
    Next comment text here.
    
    *- ▲ 45 @AnotherAuthor*
    Attribution line: dash + italic. Just blank line between comments, NO --- separators.

  1. ## **Full Transcript** — merge segments, speaker labels, clickable timestamps

Step 4: Create DOCX

Clean the title for filename (remove special chars), then:

bash
pandoc <output_path> -o ~/Documents/TubeScribe/<safe_title>.docx

Step 5: Generate audio

Write the summary text to a temp file, then use TubeScribe's built-in audio generation:

bash
# Write summary to temp file (use python3 to write, avoids shell escaping issues)
python3 -c "
text = '''YOUR SUMMARY TEXT HERE'''
with open('<temp_dir>/tubescribe_<video_id>_summary.txt', 'w') as f:
    f.write(text)
"

# Generate audio (auto-detects engine, voice, format from config)
python3 skills/tubescribe/scripts/tubescribe.py \
  --generate-audio <temp_dir>/tubescribe_<video_id>_summary.txt \
  --audio-output ~/Documents/TubeScribe/<safe_title>_summary

This reads ~/.tubescribe/config.json and uses the configured TTS engine (mlx/kokoro/builtin), voice blend, and speed automatically. Output format (mp3/wav) comes from config.

Step 6: Cleanup

bash
python3 skills/tubescribe/scripts/tubescribe.py --cleanup <video_id>

Step 7: Open folder

bash
open ~/Documents/TubeScribe/

Report

Tell what was created: DOCX name, MP3 name + duration, video stats. """, label="tubescribe", runTimeoutSeconds=900, cleanup="delete" )


**After spawning, reply immediately:**
> 🎬 TubeScribe is processing - I'll let you know when it's ready!
Then continue the conversation. The sub-agent notification announces completion.

## Configuration

Config file: `~/.tubescribe/config.json`

```json
{
  "output": {
    "folder": "~/Documents/TubeScribe",
    "open_folder_after": true,
    "open_document_after": false,
    "open_audio_after": false
  },
  "document": {
    "format": "docx",
    "engine": "pandoc"
  },
  "audio": {
    "enabled": true,
    "format": "mp3",
    "tts_engine": "mlx"
  },
  "mlx_audio": {
    "path": "~/.claudeclaw/tools/mlx-audio",
    "model": "mlx-community/Kokoro-82M-bf16",
    "voice": "af_heart",
    "lang_code": "a",
    "speed": 1.05
  },
  "kokoro": {
    "path": "~/.claudeclaw/tools/kokoro",
    "voice_blend": { "af_heart": 0.6, "af_sky": 0.4 },
    "speed": 1.05
  },
  "processing": {
    "subagent_timeout": 600,
    "cleanup_temp_files": true
  }
}

Output Options

OptionDefaultDescription
output.folder~/Documents/TubeScribeWhere to save files
output.open_folder_aftertrueOpen output folder when done
output.open_document_afterfalseAuto-open generated document
output.open_audio_afterfalseAuto-open generated audio summary

Document Options

OptionDefaultValuesDescription
document.formatdocxdocx, html, mdOutput format
document.enginepandocpandocConverter for DOCX (falls back to HTML)

Audio Options

OptionDefaultValuesDescription
audio.enabledtruetrue, falseGenerate audio summary
audio.formatmp3mp3, wavAudio format (mp3 needs ffmpeg)
audio.tts_enginemlxmlx, kokoro, builtinTTS engine (mlx = fastest on Apple Silicon)

MLX-Audio Options (preferred on Apple Silicon)

OptionDefaultDescription
mlx_audio.path~/.claudeclaw/tools/mlx-audiomlx-audio venv location
mlx_audio.modelmlx-community/Kokoro-82M-bf16MLX model to use
mlx_audio.voiceaf_heartVoice preset (used if no voice_blend)
mlx_audio.voice_blend{af_heart: 0.6, af_sky: 0.4}Custom voice mix (weighted blend)
mlx_audio.lang_codeaLanguage code (a=US English)
mlx_audio.speed1.05Playback speed (1.0 = normal, 1.05 = 5% faster)

Kokoro PyTorch Options (fallback)

OptionDefaultDescription
kokoro.path~/.claudeclaw/tools/kokoroKokoro repo location
kokoro.voice_blend{af_heart: 0.6, af_sky: 0.4}Custom voice mix
kokoro.speed1.05Playback speed (1.0 = normal, 1.05 = 5% faster)

Processing Options

OptionDefaultDescription
processing.subagent_timeout600Seconds for sub-agent (increase for long videos)
processing.cleanup_temp_filestrueRemove /tmp files after completion

Comment Options

OptionDefaultDescription
comments.max_count50Number of comments to fetch
comments.timeout90Timeout for comment fetching (seconds)

Queue Options

OptionDefaultDescription
queue.stale_minutes30Consider a processing job stale after this many minutes

Output Structure

~/Documents/TubeScribe/
├── {Video Title}.html         # Formatted document (or .docx / .md)
└── {Video Title}_summary.mp3  # Audio summary (or .wav)

After generation, opens the folder (not individual files) so you can access everything.

Dependencies

Required:

  • summarize CLI — brew install steipete/tap/summarize
  • Python 3.8+

Optional (better quality):

  • pandoc — DOCX output: brew install pandoc
  • ffmpeg — MP3 audio: brew install ffmpeg
  • yt-dlp — YouTube comments: brew install yt-dlp
  • mlx-audio — Fastest TTS on Apple Silicon: pip install mlx-audio (uses MLX backend for Kokoro)
  • Kokoro TTS — PyTorch fallback: see https://github.com/hexgrad/kokoro

yt-dlp Search Paths

TubeScribe checks these locations (in order):

PriorityPathSource
1which yt-dlpSystem PATH
2/opt/homebrew/bin/yt-dlpHomebrew (Apple Silicon)
3/usr/local/bin/yt-dlpHomebrew (Intel) / Linux
4~/.local/bin/yt-dlppip install --user
5~/.local/pipx/venvs/yt-dlp/bin/yt-dlppipx
6~/.claudeclaw/tools/yt-dlp/yt-dlpTubeScribe auto-install

If not found, setup downloads a standalone binary to the tools directory. The tools directory version doesn't conflict with system installations.

Queue Handling

When user sends multiple YouTube URLs while one is processing:

Check Before Starting

bash
python skills/tubescribe/scripts/tubescribe.py --queue-status

If Already Processing

bash
# Add to queue instead of starting parallel processing
python skills/tubescribe/scripts/tubescribe.py --queue-add "NEW_URL"
# → Replies: "📋 Added to queue (position 2)"

After Completion

bash
# Check if more in queue
python skills/tubescribe/scripts/tubescribe.py --queue-next
# → Automatically pops and processes next URL

Queue Commands

CommandDescription
--queue-statusShow what's processing + queued items
--queue-add URLAdd URL to queue
--queue-nextProcess next item from queue
--queue-clearClear entire queue

Batch Processing (multiple URLs at once)

bash
python skills/tubescribe/scripts/tubescribe.py url1 url2 url3

Processes all URLs sequentially with a summary at the end.

Error Handling

The script detects and reports these errors with clear messages:

ErrorMessage
Invalid URL❌ Not a valid YouTube URL
Private video❌ Video is private — can't access
Video removed❌ Video not found or removed
No captions❌ No captions available for this video
Age-restricted❌ Age-restricted video — can't access without login
Region-blocked❌ Video blocked in your region
Live stream❌ Live streams not supported — wait until it ends
Network error❌ Network error — check your connection
Timeout❌ Request timed out — try again later

When an error occurs, report it to the user and don't proceed with that video.

Tips

  • For long videos (>30 min), increase sub-agent timeout to 900s
  • Speaker detection works best with clear interview/podcast formats
  • Single-speaker videos (tutorials, lectures) skip speaker labels automatically
  • Timestamps link directly to YouTube at that moment
  • Use batch mode for multiple videos: tubescribe url1 url2 url3

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tubescribe AI skill do?

YouTube video summarizer with speaker detection, formatted documents, and audio output.

Why use Tubescribe on TypingMind?

Because you install it once and use it with any model. Tubescribe is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tubescribe in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlekseiUL/sprut-agent-kit/tree/main/skills/tubescribe. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tubescribe?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tubescribe?

As many as you like. As long as a model supports skills, you can use Tubescribe with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tubescribe AI skill free?

Yes. It is published on GitHub by AlekseiUL under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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