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
• 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 — 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 the speaker mention by name?"
• "Identify the main sections of this video and give me a timestamp for each."

What to do with the transcript

Once you have the text, ask the user what they need — or infer it from context:

What the user wants	What to produce
Blog post	Restructure the transcript into intro → sections → CTA. Clean up filler words. Add subheadings.
LinkedIn / Twitter post	Extract the 1–2 sharpest insights. Rewrite in first person if it's the user's own video.
Summary	3–5 bullet points of key takeaways.
Show notes / description	Title, 2-sentence summary, timestamped chapters, links mentioned.
Quote extraction	Pull verbatim quotes with start timestamps from the segments array.
Repurpose for email	Rewrite as a narrative email — opening hook, key insight, CTA.
Research / competitive analysis	Summarize what the speaker claims, what products they recommend, and what pain points they describe.

Thumbnails and SEO packaging

For making or refreshing thumbnails, cloning the style of top-ranking thumbnails, adding the user's face, and generating SEO titles/descriptions, read references/thumbnails.md. Every thumbnail workflow is a sandbox script under scripts/ (generate_thumbnail.py, research_top_thumbnails.py, clone_top_thumbnail_style.py) — the reference doc is the routing table and the rules for using them.

Example outputs

Input: "Get the transcript of https://www.youtube.com/watch?v=NZLAdOL9fP8 and write a LinkedIn post from it"

Flow:

1. Call youtube_video_transcripts_fetch(video_id_or_url="https://www.youtube.com/watch?v=NZLAdOL9fP8")
2. Read the returned text
3. Identify the 1–2 sharpest moments — what's surprising, useful, or quotable
4. Draft a LinkedIn post in the speaker's voice (first person) with a hook and a clear point

Input: "Summarize this video for me: [URL]"

Flow:

1. Call youtube_video_transcripts_fetch(video_id_or_url="[URL]")
2. Return 4–6 bullet points of key takeaways, without padding or filler

Input: "Make me a thumbnail like the top videos for 'AI agents'"

Flow:

1. Read references/thumbnails.md
2. Run scripts/clone_top_thumbnail_style.py with query="AI agents" and the user's topic
3. Show the top thumbnails, let the user pick a rank, re-run with chosen_rank to generate

Related skills

When to hand off	Skill
Mining comments from YouTube videos for customer research	customer-research
Finding top YouTube videos by topic	Use youtube_videos_search_top directly
Generating video content	video-generation

For title options, descriptions and thumbnail concepts, run generate_seo_titles.py, generate_seo_description.py and analyze_thumbnail_concepts.py under scripts/. They call ai_functions_run from the sandbox using fetched transcript and video context. Follow the output contracts in references/packaging-schemas.json; the thumbnail reference explains the workflow.