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Get Started Free →Pull a YouTube video transcript into a queryable markdown vault with yt-dlp subtitle discovery, VTT cleanup, metadata frontmatter, and capture-seed stubs.
.claude/skills/ingest-youtube/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | — | — |
| case-21 | ✗→✓ | ▲ Improved | — | — |
| case-09 | ✗→✓ | ▲ Improved | — | — |
| case-01 | ✗→✓ | ▲ Improved | — | — |
| case-13 | ✗→✓ | ▲ Improved | — | — |
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.
/ingest-youtube <url> for a single videoDo NOT use for:
yt-dlp directly with -f best)--days windows; this script ingests one video URL at a timeyt-dlp --write-auto-sub directly and pipe to stdout)yt-dlp is installed. If not, the script exits with install instructions: brew install yt-dlp (macOS) or pip3 install --user yt-dlp.yt-dlp --ignore-config --list-subs -- <url> to enumerate available subtitles.yt-dlp --write-sub --sub-lang <lang> --skip-download. Default language preference: en,es (English first, Spanish second).yt-dlp --print-json --skip-download.External Inputs/YouTube/<channel-slug>/<YYYY-MM-DD>-<video-slug>.md.Meta/Captures/<YYYY-MM-DD>-youtube-<channel-slug>-<video-id>.md so the seed lands in the captures aggregator.bashpython3 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 availableThe vault file at External Inputs/YouTube/<channel-slug>/<YYYY-MM-DD>-<video-slug>.md has frontmatter:
yaml--- 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.
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.
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.
--days windows are out of scope.yt-dlp; videos without subtitles produce a metadata stub, not a transcript.yt-dlp behavior determine whether ingest succeeds.Run against the first YouTube video ever uploaded:
bashpython3 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.
yt-dlp (required): install via brew install yt-dlp or pip3 install --user yt-dlpwhisper-cpp (optional for a manual fallback outside this script)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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-23 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-24 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +67 percentage points is the difference between those two pass rates over the 24 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.