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Youtube summary skill

Skill veryCoolTimo/youtube-summary-skill

Claude Code skill that turns YouTube links into a searchable knowledge base: captions or local Whisper, LLM-distilled summary cards with timecoded links and screenshots, topical auto-filing, hybrid RAG search, optional git sync. MIT.

Install
npx -y skills add veryCoolTimo/youtube-summary-skill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when the user shares one or more YouTube links to save/summarize, or asks about previously saved YouTube videos (what was in video X, which video covered Y, find the one about Z). Covers ingesting links into the knowledge base and answering questions from the saved cards.

SKILL.md

4.9 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

youtube-summary

Single entry point for the YouTube knowledge base. The pipeline is deterministic (scripts in this repo) — you pick the engine and read results; you never write cards or edit the index by hand.

REPO (run everything from here, it has its own venv with all deps): cd ~/.claude/skills/youtube-summary then use .venv/bin/python. (This is the install path from the README; adjust if the repo lives elsewhere.)

When to use

  • The user shares YouTube link(s) → INGEST them.
  • The user asks about saved videos → QUERY the knowledge base.

INGEST — links → knowledge base

Pass ALL the user's URLs in one command (one venv start, one git commit):

cd ~/.claude/skills/youtube-summary && .venv/bin/python -m scripts.yt_core "<url1>" "<url2>" --config config.yaml
  • Default engine openrouter. Add --engine local (private, via ollama — distill AND classify stay offline) or --engine self (you summarize, see below) only if asked.
  • Force a folder with --category top/sub only if the user specifies it; otherwise it auto-classifies. Tops come from taxonomy.yaml in the KB repo (default: skills, reviews, startup, random).
  • Output: one JSON line per video, then always a final {"git": ..., "videos": N} line (videos = how many were saved/refiled). Statuses:
    • ok — saved; title, tldr, file, top/sub are in the line — report from them, no need to read the card.
    • exists — already in the KB; nothing re-run. Tell the user; add --force to re-process, or pass --category top/sub to move it (cheap re-file, no LLM).
    • refiled — an existing card was moved to the requested category.
    • need_card — engine=self only, see protocol below.
    • failed — report reason to the user (this includes a typo'd --category: the pipeline refuses rather than guessing).
  • git in the final line: pushed is full success; committed = saved locally, push disabled (--no-push); clean = nothing changed (e.g. --force produced identical content) — fine, not an error; push_failed = committed locally but not pushed — tell the user; failed = the commit itself failed; skipped = nothing was saved so no commit was attempted; disabled = the KB folder is a plain directory (default no-git setup) — normal, but if the user asks about syncing/versioning, offer to git init it.
  • If something landed in <top>/_inbox (no taxonomy match), say so and offer to add a subfolder to taxonomy.yaml.

engine=self protocol (you are the summarizer)

  1. Run the ingest command with --engine self → each video answers {"status":"need_card","prompt_file":...}.
  2. Read prompt_file (SYSTEM + USER sections), write the card strictly to that JSON schema, including the category field, into a temp file.
  3. Rerun with --card-file <path> and that video's SINGLE url — the pipeline continues (classify → write → index → commit). With several videos, do one rerun per video.

QUERY — questions → grounded answer

Never answer from memory. Follow every step:

  1. Retrieve: cd ~/.claude/skills/youtube-summary && .venv/bin/python -m scripts.kb_query "<question>" --config config.yaml
    • Hybrid search: vector + keyword over the cards; returned file/media_dir paths are absolute.
    • "что я сохранял недавно / на этой неделе" → use --recent N (newest first) instead of a question; --top skills filters by category.
  2. Read the actual card files (the file paths returned) — the full card, not just the snippet.
  3. Verify every claim you make is present in a card you read. Not found → say so, don't invent.
  4. Answer concisely: video title, timecoded deep-links from takeaways, and the screenshot (media_dir) when the question is visual.

Output rules

  • "Только важное / без воды" → use tldr + key takeaways, not everything.
  • "Не повторяй прежнее" → don't resend a card already shown earlier in this conversation.
  • "Подробнее про это видео" → read its full card and expand only from there.

Maintenance

  • New machine / empty search results but cards exist → rebuild the vector index without any LLM calls: .venv/bin/python -m scripts.reindex --config config.yaml

Config & secrets

config.yaml (gitignored, per-machine) sets kb_repo (empty = knowledge-base/ folder next to the skill, no git; a git clone path = auto commit+push), distill engine/models and card language, caption language priority, Whisper model, vector store, embedder. OPENROUTER_API_KEY comes from env_file or the environment. See README.md for setup.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.