| name | ingest-youtube |
| description | Use when the user says /ingest-youtube <url-or-channel> [--days N], pastes a YouTube URL (youtube.com or youtu.be) wanting a transcript or summary in the vault, asks to ingest, capture, sync, transcribe, or pull a YouTube video, channel, talk, podcast, or keynote into the vault, or wants a video's captions or content available to the knowledge graph. Not for downloading video files, live streams, or non-YouTube sources (Vimeo, Twitch). |
ingest-youtube — YouTube-to-vault connector
{SKILL_DIR} = this skill's own folder (locally: the directory this SKILL.md lives in; a served brain substitutes the real absolute path before you read this). If a path does not resolve, name the missing file and stop — never guess another location.
Ingests YouTube transcripts into the vault as markdown the graphify pipeline can read and the rest of the AI Brain Starter substrate (decision log, session-close cascade, hooks) can act on.
Same connector pattern as ingest-github: adding a new source means a new normalizer, not a new architecture.
When to use
- User says
/ingest-youtube <url> for a single video
- User says
/ingest-youtube <channel-handle> [--days N] for a channel's recent uploads
- User asks to capture, sync, ingest, transcribe, or pull a talk/podcast/keynote into the vault
- User pastes a YouTube URL and asks for a transcript or summary
- User mentions wanting a video's content available to the knowledge graph
Do NOT use for:
- Downloading the actual video file (use
yt-dlp directly with -f best)
- Live streams (transcripts are not stable)
- Non-YouTube sources (Vimeo, Twitch, Twitter Spaces get 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
- Parse the input: single URL → single-video mode. Channel handle (e.g.
@channelname) → channel mode (last N days, default 14).
- Verify
yt-dlp is installed. If not, attempt brew install yt-dlp (macOS) or pip3 install --user yt-dlp and surface the install path. Abort if neither works.
- For each video, call
yt-dlp --list-subs <url> to enumerate available subtitles.
- Subtitle priority: manual subs > auto-generated > Whisper fallback. Manual subs preserve creator-provided punctuation and speaker labels; auto-gen is uppercase + no punctuation; Whisper is the floor.
- Download the highest-priority subtitle as VTT via
yt-dlp --write-sub --sub-lang <lang> --skip-download. Default language preference: en,es (so non-English content is captured in its original language without forcing English).
- Strip VTT timing markers and merge into clean prose paragraphs. Preserve speaker labels if the source had them.
- Pull video metadata (title, channel, upload date, duration, video_id, URL) via
yt-dlp --print-json --skip-download.
- Slugify the channel name and video title. Write to
External Inputs/YouTube/<channel-slug>/<YYYY-MM-DD>-<video-slug>.md.
- 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.
- Print summary: file path, transcript word count, language, seeds detected.
Voice rules
- No em dashes (use commas, colons, periods, parentheses)
- Spanish-language transcripts stay in Spanish; English stays in English; bilingual transcripts preserve language code-switches
- Speaker labels quoted verbatim from the source subtitle file
- No paraphrasing or summarization at ingest time. Summaries happen downstream via
/note-todos or /repurpose-talk.
Invocation
The skill is a thin orchestrator. The actual ingestion runs in Python at {SKILL_DIR}/ingest.py.
When invoked:
- Parse arguments: URL (single-video) or channel handle (channel mode),
--days N (channel mode only, default 14), --lang <code> (override default en,es).
- Single-video mode: hand the URL to
ingest.py. The script handles yt-dlp calls, VTT cleanup, vault write, and seed stub creation.
- Channel mode: enumerate the last N days of uploads via
yt-dlp --flat-playlist --print-json --dateafter <date> https://www.youtube.com/<channel>. Pass each video URL to ingest.py in sequence.
- Surface the summary to the user. If seed stubs were created, list the file paths.
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.
Capture seed stubs at ⚙️ Meta/Captures/<date>-youtube-<channel-slug>-<video-id>.md carry frontmatter that matches the existing capture schema so the captures aggregator picks them up cleanly.
Idempotency
Re-ingesting the same video URL overwrites the same vault file (the path is deterministic from channel-slug + upload-date + video-slug). 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.
Acceptance test
A successful run produces:
- One new (or refreshed) file at
External Inputs/YouTube/<channel>/<date>-<title>.md with valid frontmatter and clean prose body
- Zero or more new capture stubs at
⚙️ Meta/Captures/<date>-youtube-<channel>-<video-id>.md
- A stdout summary:
Wrote N words to <path>. Language: <code>. Subtitle source: <source>. Seeds at: <paths>.
If the video has no available subtitles and Whisper is not installed locally, write a stub file with subtitle_source: none and a note in the body explaining the gap, so re-runs are still idempotent and the absence is recorded.
Whisper fallback
If yt-dlp --list-subs returns no manual or auto subtitles AND whisper-cpp is installed locally, fall back to:
yt-dlp -x --audio-format mp3 -o <tmp>/<video-id>.mp3 <url> to download audiowhisper-cli <tmp>/<video-id>.mp3 --model ggml-large-v3.bin --output-vtt to transcribe- Continue with the VTT cleanup pipeline
Whisper fallback is OFF by default for cost reasons. Enable per-call with --whisper. Local Whisper has zero per-minute cost but takes ~real-time on CPU.
Cross-references
- For turning the transcript into LinkedIn/Substack content →
repurpose-talk after ingest
- For pulling action items out of the transcript →
note-todos after ingest
- For knowledge-graph extraction across many transcripts →
graphify on External Inputs/YouTube/ directly
