youtube-notes

name

youtube-notes

description

Pull captions from a YouTube video and turn them into chapter-aligned notes where every claim carries a clickable timestamp. Use this whenever a youtube.com or youtu.be link appears, including a bare pasted link with no instructions attached, and whenever the user wants to summarize, digest, triage, or take notes on a video, talk, lecture, keynote, podcast, or interview: "is this worth watching", "TL;DR this talk", "what do they say about X", "find the part where they discuss Y", "save this to my notes", "get me the transcript". Prefer this over generic web fetching or page readers for YouTube links, because the page HTML carries no transcript. Not for downloading video or audio files, and not for local media.

YouTube Notes

Turn a video into something citable. The bundled script does the mechanical work — fetching captions, aligning them to the uploader's chapters, merging caption slivers into readable blocks — so your job is synthesis and judgment.

Fetch

One TypeScript file, run directly by Node with no build step. The --help output is the authoritative flag reference:

node ${CLAUDE_PLUGIN_ROOT}/skills/youtube-notes/scripts/fetch_video.ts --help

It shells out to uvx for youtube-transcript-api (captions) and yt-dlp (metadata, chapters). Both run on demand; nothing is installed globally. If uvx is missing the script exits 2 and tells the user to brew install uv.

The transcript is cached, so re-running is free

The first fetch for a video is cached by id. Every later call for that video reads from disk and touches no network: 35s becomes 0.06s, and switching modes or pulling different chapters costs nothing.

This shapes how to work. Do not ration calls to avoid refetching, because after the first one there is no refetch. Pull triage to see the shape, then pull the chapters you actually need, then pull different ones when the next question lands. The only real budget is context, not network.

It is also the main defense against rate limiting, which is the failure mode this skill hits most. A request you never make cannot be throttled.

--refresh forces a refetch, which you need only if a video's captions were genuinely republished. --no-cache skips it entirely.

The main use: answering questions about a video

Most requests here are conversational. Someone wants to interrogate a video, not receive a document. Expect several questions across a conversation about one video.

Load once, properly, then answer from what you have:

1. Pull --mode triage to see the chapter map and size.
2. For a short or medium video, pull --mode full and keep it in context. Every follow-up is then answered without another call.
3. For something multi-hour, pull the chapters that bear on the question with --chapters, and pull more as the conversation moves. Cached, so cheap.

Answer with the deep links inline so the user can jump to the source. When the video does not address something, say so plainly rather than reaching for an adjacent passage.

Resist turning every question into a digest. If someone asks what the speaker thinks about X, answer that, cite it, and stop.

Mode by intent

User intent	Mode
"is this worth watching", "what's this about", bare pasted link	triage
"summarize", "take notes", "digest this", "save to my notes"	full
a question about the content, or a conversation about the video	triage, then full (or --chapters <n>)
"give me the transcript"	transcript

node ${CLAUDE_PLUGIN_ROOT}/skills/youtube-notes/scripts/fetch_video.ts "<url>" --mode triage

Long videos

Chapter indices are stable across calls, so an index from triage is safe to reuse later. A 3h45m podcast is ~75k tokens in full and ~2k in triage, so target chapters rather than loading everything:

node ${CLAUDE_PLUGIN_ROOT}/skills/youtube-notes/scripts/fetch_video.ts "<url>" \
  --mode full --chapters 3,7 --out /tmp/video.json

The script prints an estimated token count to stderr and warns when output is large. Above roughly 25k tokens, write to --out and read the file back rather than piping it through stdout.

When captions fail

Two failures, one remedy:

• Exit 4, rate limited. YouTube throttles the caption endpoint per IP. It is transient and affects only captions; metadata, chapters, and the audio stream keep working.
• Exit 3, no captions published. Permanent for that video.

Both are answered by --whisper-fallback, which downloads the audio and transcribes it locally. It works during a block because the media CDN is not subject to the caption quota. Verified: youtube-transcript-api, yt-dlp subtitles, and curl with a real browser User-Agent all get 429 on the caption endpoint while audio downloads at full speed.

Ask before using it on the first video of a session. It costs a one-time model download of roughly 1.6 GB and a minute or two of compute, which is a real cost the user should agree to. Once they have agreed, keep using it for that conversation without asking again.

node ${CLAUDE_PLUGIN_ROOT}/skills/youtube-notes/scripts/fetch_video.ts "<url>" \
  --mode full --whisper-fallback

Do not suggest browser cookies as a workaround. Upstream warns that authenticating that way eventually gets the account permanently banned.

Whisper output is cached like any other transcript, so the cost is paid once per video.

What comes back

JSON modes return metadata plus a chapters array. Fields that drive decisions:

• chapters_source — youtube (the uploader's own chapters, trustworthy structure), time-sliced (no chapters, so 5-minute buckets titled by range), or single (short video, one bucket). Only youtube reflects authorial intent; do not present time-sliced bucket titles as if they were chapter names.

• caption_kind — where the words came from, which sets how much to trust an exact quote:

  • manual — a human-authored track. Quote freely.
  • generated — YouTube's ASR. Mishears proper nouns, technical terms, and numbers.
  • whisper — local ASR, not anything YouTube served. Usually cleaner prose than generated, but still guesses at names, product names, and version numbers.

  For generated or whisper, say once that quotes are approximate, and flag specific proper nouns you suspect rather than silently correcting them into something plausible.

• word_count and reading_minutes — compare reading_minutes against duration_hms to tell the user whether reading beats watching. It usually does for interviews and usually doesn't for anything visual.

• chapters_total vs chapters_included — if these differ you are looking at a subset, so do not claim to have covered the whole video.

• chapters[].blocks[] — each block is { t, s, text } where t is a display timestamp and s is that timestamp in whole seconds.

Citation rule

Every claim you attribute to the video gets a deep link built from the block's s value:

<url>&t=<s>

So a claim from a block with "s": 754 on video abc12345678 cites as https://www.youtube.com/watch?v=abc12345678&t=754.

Never invent a timestamp. If you cannot locate a claim in a specific block, attribute it to the chapter and use the chapter's link, or leave it uncited.

Triage output

Short. The user is deciding whether to spend 40 minutes.

**[Title]** — Channel · 18:40 · ~3,400 words

[Two sentences: what it actually covers, and how it treats the subject.]

**Worth it if:** [who this serves]
**Skip if:** [who it wastes]
**Best chapters:** [Chapter name](link) · [Chapter name](link)

Give an actual verdict. "It depends on your goals" is not a verdict.

Digest output

Ordered so someone who stops reading after ten seconds still got the most valuable part.

# [Title]

**Channel:** [name] · **Duration:** [hms] · **Published:** [date]
**Source:** [url]

## TL;DR

[One or two sentences carrying the video's actual thesis.]

## Key takeaways

- [3-7 standalone insights. Each should survive being read alone.]

## Claims worth checking

- [Claim] — [[m:ss]](deep link)
- [Mark anything the speaker asserts without support, and anything you know to
  be contested.]

## Walkthrough

### [Chapter title] ([m:ss](link))

[What happens here, in a few sentences. Follow the video's own chapters when
`chapters_source` is `youtube`.]

## Notable quotes

> "[quote]" — [[m:ss]](deep link)

## Open questions

[What the video raises and does not answer.]

Skip sections that would be filler. A ten-minute tutorial does not need "Claims worth checking."

Extract mode

When the user asks one question, answer only that question. Do not emit a digest. Triage first to read the chapter titles, then pull just the plausible chapters with --chapters. Reply with the answer, the deep links backing it, and an explicit "the video does not address this" when it doesn't.

Saving to notes

Only write files when the user asks. If they do, look for a vault directory in .claude/sources.local.md:

---
vault_dir: /path/to/vault/Videos
---

Absent that setting, ask once where notes should live, then offer to record the answer there.

When writing into a vault, match the conventions already in it — read a neighboring note before inventing frontmatter. A reasonable default:

---
tags:
  - video
  - youtube
youtube_id: '<video id>'
source: '<url>'
channel: '<channel>'
date: <YYYY-MM-DD>
---

Quote youtube_id so it stays a string, and grep for it before writing to avoid duplicating a video already captured.

Honesty

• The transcript is the only evidence you have. You did not watch the video, so do not describe visuals, slides, or demos beyond what the captions state.
• Auto-generated captions garble names and numbers. Do not silently "fix" a term into something plausible; flag the uncertainty instead.
• Never search the web for a third-party transcript. A re-publication of unknown provenance cannot be verified, and --whisper-fallback gets you the real audio anyway.
• Do not retry a rate-limited fetch in a loop. It extends the block. Offer --whisper-fallback or a wait.
• Metadata and chapters survive a caption block, so a title-and-chapter-map answer is available even when the transcript is not. Say clearly that it comes from metadata and that you have not read the content.
• When caption_kind is whisper, the words are a local transcription of the audio. Do not present them as YouTube's captions.