Skill

Ingest YouTube Transcripts into Your Vault

Pulls YouTube video transcripts into a markdown vault as queryable typed-memory entries with structured metadata.

Works with youtubeyt dlp

46
Spark score
out of 100
Updated 3 days ago
Version 13.1.0

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Why it matters

Automatically pull transcripts and metadata from YouTube videos into your personal knowledge vault, making them easily searchable and actionable for downstream AI tasks.

Outcomes

What it gets done

01

Download YouTube transcripts and metadata.

02

Clean and format transcripts into markdown files.

03

Create knowledge graph seeds from transcript content.

04

Organize ingested content by channel and video.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-ingest-youtube | bash

Capabilities

What this skill does

Download YouTube transcripts

Download YouTube transcripts and metadata.

Clean and format

Clean and format transcripts into markdown files.

Create knowledge graph

Create knowledge graph seeds from transcript content.

Organize ingested content

Organize ingested content by channel and video.

Overview

ingest-youtube - YouTube-to-vault connector

What it does

A Python script that downloads YouTube video subtitles via `yt-dlp`, converts them to clean markdown prose, and stores them in a vault with structured frontmatter and optional writing-seed stubs.

How it connects

Use when a user pastes a YouTube URL and asks for a transcript or summary, says `/ingest-youtube <url>`, or asks to capture, sync, ingest, transcribe, or pull a talk/podcast/keynote into the vault. Do not use for downloading video files, channel-wide ingestion, live streams, non-YouTube sources, or one-off transcript reads where the user does not want a vault file.

Source README

ingest-youtube - YouTube-to-vault connector

Pulls YouTube transcripts into a markdown vault as queryable typed-memory entries that downstream skills (knowledge graph extraction, voice-fingerprint training, content repurposing, action-item extraction) can act on.

Same pattern as ingest-slack, ingest-whatsapp, ingest-notion, ingest-linear, ingest-github, ingest-gmail. Adding YouTube means a new normalizer, not a new architecture.

When to use

  • User pastes a YouTube URL and asks for a transcript or summary
  • User says /ingest-youtube <url> for a single video
  • User asks to capture, sync, ingest, transcribe, or pull a talk/podcast/keynote into the vault

Do NOT use for:

  • Downloading the actual video file (use yt-dlp directly with -f best)
  • Channel-wide ingestion or --days windows; this script ingests one video URL at a time
  • Live streams (transcripts are not stable)
  • Non-YouTube sources (Vimeo, Twitch, Twitter Spaces have their own connectors)
  • One-off transcript reads where the user does not want a vault file (run yt-dlp --write-auto-sub directly and pipe to stdout)

How it works

  1. Parse the input as one YouTube video URL.
  2. Verify yt-dlp is installed. If not, the script exits with install instructions: brew install yt-dlp (macOS) or pip3 install --user yt-dlp.
  3. Validate the URL as a single http(s) YouTube video and call yt-dlp --ignore-config --list-subs -- <url> to enumerate available subtitles.
  4. Subtitle priority: manual subs > auto-generated captions. Manual subs preserve creator-provided punctuation and speaker labels; auto-gen is uppercase + no punctuation.
  5. Download the highest-priority subtitle as VTT via yt-dlp --write-sub --sub-lang <lang> --skip-download. Default language preference: en,es (English first, Spanish second).
  6. Strip VTT timing markers and merge into clean prose paragraphs. Deduplicate repeated lines (auto-generated VTTs are line-doubled). Preserve speaker labels if the source had them.
  7. Pull video metadata (title, channel, upload date, duration, video_id, URL) via yt-dlp --print-json --skip-download.
  8. Slugify the channel name and video title. Write to External Inputs/YouTube/<channel-slug>/<YYYY-MM-DD>-<video-slug>.md.
  9. Scan transcript for trigger keywords (decision, framework, model, principle, "the lesson is", playbook, anti-pattern, case study). For each match, create a writing-seed stub at Meta/Captures/<YYYY-MM-DD>-youtube-<channel-slug>-<video-id>.md so the seed lands in the captures aggregator.
  10. Print summary: file path, transcript word count, language, seeds detected.

Invocation

python3 ingest.py <youtube-url> [--vault <path>] [--lang <code>]

Defaults:

  • --vault: $VAULT_ROOT env var or current directory
  • --lang: en,es (English first, Spanish second; matches a common bilingual default)
  • --whisper: accepted as a future fallback flag, but this version writes a stub when no subtitles are available

Output contract

The vault file at External Inputs/YouTube/<channel-slug>/<YYYY-MM-DD>-<video-slug>.md has frontmatter:

---
type: external-input
source: youtube
video_id: <11-char ID>
url: https://www.youtube.com/watch?v=<id>
channel: <channel-name>
channel_url: https://www.youtube.com/<handle>
title: <video title>
upload_date: <YYYY-MM-DD>
duration_seconds: <int>
language: <ISO code>
subtitle_source: manual | auto | whisper
word_count: <int>
ingested_at: <ISO 8601 timestamp>
---

Body is the cleaned transcript as paragraph prose. If the source had speaker labels, format as **<speaker>:** <text> per turn.

Idempotency

Re-ingesting the same video URL overwrites the same vault file. The seed stub filenames hash the video_id, so the same source video produces the same stub filename across re-runs. Re-runs refresh, never duplicate.

Missing subtitles

If yt-dlp --list-subs returns no manual or auto subtitles, the script writes a stub vault note with the video metadata and source URL instead of failing silently. The --whisper flag is reserved for a future local transcription fallback and currently reports that the fallback is not implemented.

For a manual fallback today, download audio with yt-dlp, transcribe it with your local Whisper workflow, and add captions or transcript text before rerunning the ingest.

Limitations

  • Ingests one YouTube video URL per run; channel handles, playlists, and --days windows are out of scope.
  • Depends on subtitles returned by yt-dlp; videos without subtitles produce a metadata stub, not a transcript.
  • Does not download video files or perform built-in Whisper transcription in this version.
  • Network availability, YouTube subtitle access, and local yt-dlp behavior determine whether ingest succeeds.

Acceptance test

Run against the first YouTube video ever uploaded:

python3 ingest.py "https://www.youtube.com/watch?v=jNQXAC9IVRw" --vault /tmp/test

Expected output:

Wrote 39 words to /tmp/test/External Inputs/YouTube/jawed/2005-04-24-me-at-the-zoo.md. Language: en. Subtitle source: manual.

The output file contains valid frontmatter and a clean prose body.

Dependencies

  • yt-dlp (required): install via brew install yt-dlp or pip3 install --user yt-dlp
  • whisper-cpp (optional for a manual fallback outside this script)

Source

Bundled in adelaidasofia/ai-brain-starter, a verification harness around an AI agent so memory compounds instead of corrupts. The skill is part of the ingest-* family of vault connectors.

Discussion

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