/youtube

name: youtube
description: Work with YouTube content end to end — fetch transcripts and turn them into summaries, blog posts, social content, quotes, or show notes; create high-CTR thumbnails (with the user's face from an upload), clone the style of top-ranking thumbnails; and produce SEO-optimised titles + descriptions. Use when the user pastes a YouTube URL, wants to repurpose video content, research competitor videos, make or refresh a thumbnail, or package a video for upload.
use_cases:
  - Get the transcript of a YouTube video
  - Summarize a YouTube video or extract key points without watching
  - Turn a video into a blog post, LinkedIn post, or show notes
  - Pull verbatim quotes with timestamps
  - Generate a YouTube thumbnail from a video idea, transcript, or YouTube URL
  - Research the top-performing thumbnails for a search term and clone their style
  - Add the user's face (uploaded as an image) to a thumbnail
  - Produce an SEO-optimised title set and description for an upcoming video
triggers:
  - youtube
  - youtube transcript
  - youtube video
  - video summary
  - repurpose video
  - show notes
  - thumbnail
  - youtube thumbnail
  - thumbnail clone
  - SEO titles
  - video description
  - youtube title
requires_toolkits:
  - youtube_toolkit
suggested_toolkits:
  - image_gen
  - sandbox
  - file_manager
icon: youtube
short_description: Fetch transcripts, repurpose video content, and create thumbnails for YouTube.

YouTube

Fetch the full transcript of any YouTube video and turn it into whatever the user needs — summaries, blog posts, social content, quotes, show notes, or raw text. Then package videos for upload: high-CTR thumbnails, SEO titles, and descriptions.

Routing

| User intent | Where to go | | --- | --- | | Transcript, summary, repurposing, quotes, chapters | This guide (below) | | Thumbnails, style cloning, SEO titles/descriptions | `references/thumbnails.md` |

Requirements

• **Hyper MCP installed.** [https://app.hyperfx.ai/mcp](https://app.hyperfx.ai/mcp)
• **Sandbox text workflows:** require `ai_functions_run` in the connected catalog and the sandbox toolkit. These scripts run inline LLM calls through the sandbox tool bridge.
• **YouTube toolkit enabled** at [https://app.hyperfx.ai/apps](https://app.hyperfx.ai/apps) — provides `youtube_video_transcripts_fetch` and `youtube_videos_read`.
• Thumbnail workflows additionally need the image generation and sandbox toolkits.

If `search("youtube_video_transcripts_fetch")` does not find `youtube_video_transcripts_fetch`, stop and tell the user to enable the YouTube toolkit in Hyper.

How to run the tools in this skill

Every tool in this skill is named by its canonical tool name. Run it with the call your surface gives you:

| Surface | Find a tool | Run it | | --- | --- | --- | | MCP client (Claude, Cursor, Codex, ChatGPT) | `search("<what you want to do>")`, then `describe("<name>")` | `call("<name>", {...})` | | Hyper CLI | `hyperai search "<what you want to do>"`, then `hyperai describe <name>` | `hyperai call <name> --json '{...}'` |

If a tool is not found, its integration is not connected or not enabled for the workspace: stop and tell the user which integration to connect.

Two tools — pick the right one

| Tool | When to use | Returns | | --- | --- | --- | | `youtube_video_transcripts_fetch` | You need the raw transcript text or timestamped segments. Fast, reliable, always get this first. | Full text string + segments with start/duration timestamps | | `youtube_videos_read` | You need AI-powered extraction from the video — summaries, Q&A, topic segmentation, translation, visual descriptions. | Free-form answer to your instruction |

**Default: start with `youtube_video_transcripts_fetch`.** Use `youtube_videos_read` when you need something the raw text can't give you (e.g. visual descriptions, translation, or a structured extraction from a very long video).

Critical rules

1. **`youtube_video_transcripts_fetch` takes 15–30 seconds.** It spins up an isolated sandbox. Tell the user it's running and to expect a short wait — don't make them think it's stuck. 2. **Both video IDs and full URLs are accepted.** `"NZLAdOL9fP8"` and `"https://www.youtube.com/watch?v=NZLAdOL9fP8"` both work. 3. **Don't fabricate transcript content.** Always fetch before summarizing. Never rely on training knowledge about what a specific video says. 4. **Very long videos (>2 hours):** `youtube_video_transcripts_fetch` handles these fine. Only use `youtube_videos_read` on long videos if you specifically need AI-powered extraction — it can hit token limits on very long content. 5. **No transcript available:** Some videos have transcripts disabled. If `youtube_video_transcripts_fetch` fails, try `youtube_videos_read` as a fallback — it uses a different extraction method.

Fetching the transcript

youtube_video_transcripts_fetch(
    video_id_or_url="https://www.youtube.com/watch?v=NZLAdOL9fP8",
    language="en"   # optional — omit to auto-detect
)

**Response structure:**

{
  "success": true,
  "video_id": "NZLAdOL9fP8",
  "language": "English (auto-generated)",
  "text": "Full transcript as one string...",
  "segments": [
    { "text": "This week we launched Hyper MCP.", "start": 0.0, "duration": 3.2 },
    { "text": "It brings Hyper's built-in tools...", "start": 3.2, "duration": 4.1 }
  ],
  "total_duration": 342.0
}

Use `text` for most tasks. Use `segments` when you need timestamps (e.g. chapters, clip references, karaoke captions).

Using youtube_videos_read for AI-powered extraction

youtube_videos_read(
    url="https://www.youtube.com/watch?v=NZLAdOL9fP8",
    instruction="Summarize the key points. Then list the main features demonstrated, with timestamps."
)

Good `instruction` examples:

• `"Extract every claim made about pricing or cost."`
• `"List the action items mentioned, in order."`
• `"Translate this to Spanish."`
• `"What tools or products does