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David Ibia
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- Move the vendored skills from .config/opencode/skills to .config/omo/skills, with provenance and local changes in its README - Rewrite Impeccable's script paths to ~/.omo/agent/skills/impeccable so its detector and live-server verbs resolve under omo - frontend-workflow hands screenshots to a visual-engineering task instead of opencode run - Move the notes commands and /impeccable to .config/omo/prompts as omo prompt templates; setup-mcp stays behind because it writes opencode.json - Archive DEVELOPMENT-WORKFLOW.md and UI-WORKFLOW.md under .config/omo/archive; nothing loads them - link-ai-configs.sh links the skills into omo and Claude Code only and stops linking OpenCode's config, TUI, plugins and workflow docs - setup-mac.sh and Brewfile.apps stop installing OpenCode; README and .withcontextconfig.jsonc follow the new layout ~/.config/opencode/AGENTS.md stays linked: omo's rules engine reads it before ~/.claude/CLAUDE.md and stops at the first hit, so it carries the shared preferences and keeps OMC's instructions out of omo.
67 lines
4.4 KiB
Markdown
67 lines
4.4 KiB
Markdown
# YouTube Videos That Get Cited by AI
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YouTube is one of the most-cited third-party surfaces in AI answers — Google AI Overviews and Gemini cite it heavily, and ChatGPT/Perplexity lift from it for how-to queries. The core insight that changes how you produce for it:
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**Models don't watch your video. They read everything around it.** The citation is earned by the text layer — title, transcript, captions, chapters, description, and comments — not the footage. A mediocre-looking video with a clean, structured text layer beats a beautiful one that's opaque to a crawler.
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## The anatomy
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Work through these in order of leverage:
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### 1. The transcript (the real content)
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This is what the model actually reads. Optimize the *spoken words*:
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- **Answer questions in complete, liftable sentences.** "The five steps to create an SOP are…" extracts cleanly; a rambling answer spread across three tangents doesn't.
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- Script or outline the key answers before recording so each core question gets a clear, structured spoken answer in one place.
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- Say the important terms out loud — the product name, the category, the entities you want associated. If it's only on a slide, the model may never see it.
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### 2. Accurate captions
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Auto-captions are messy — misheard product names, no punctuation, broken sentences — and messy captions are what the model reads if you don't fix them. Upload cleaned captions (or at minimum correct the auto-generated ones). This is the cheapest fix on the list.
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### 3. A question-shaped title
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Models match the title against the user's prompt. "How to Create SOPs That Scale Your Business" beats a clever title every time. Front-load the question or task; save the branding for the channel.
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### 4. Chapters and timestamps
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Chapters let the model (and viewers) jump to the exact answer. Structure = extractability: each chapter title is another labeled, liftable claim about what the video covers. Match chapter titles to the sub-questions people actually ask.
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### 5. A keyword-rich, structured description
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Restate the video's key points *as text* in the description — a short summary, then a bulleted list of what's covered, then resource links. This reinforces the topic and entities in plain crawlable text and gives the model a second, cleaner copy of the answer.
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### 6. A pinned comment with the summary
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An extra liftable text block: pin a comment with the core answer in numbered steps plus the key links. It's indexed, it's structured, and it survives even when viewers never open the description.
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### 7. Thumbnail and engagement
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Engagement isn't read directly by LLMs, but it drives the watch signals that lift YouTube ranking — and YouTube ranking feeds what AI systems surface and cite. The thumbnail's job is the click; the text layer's job is the citation.
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## Publishing checklist
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- [ ] Title is question- or task-shaped and matches a real query
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- [ ] Key answers spoken as complete, structured statements
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- [ ] Captions uploaded or corrected (product names spelled right)
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- [ ] Chapters added, titled by sub-question
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- [ ] Description restates the key points in text with a bulleted breakdown
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- [ ] Pinned comment carries the summary + links
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- [ ] Important entities (brand, category, product) spoken *and* written
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## Field notes (Sep 2026)
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- **The citation bar can be low.** In a live check, an 8-day-old video with under 100 views was the first source cited in Google's AI Overview for "best email marketing software." Fresh, well-labeled videos on head terms are worth testing.
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- **Ungate your demos.** Post product demos publicly with keyword-first titles ("[Category] software demo – [Brand]") instead of locking them behind a form.
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- **Sponsored creator videos are hard to attribute.** Practitioners quote $20–40K per sponsored video and no reliable way to prove the return. Budget them with an attribution plan (the self-reported field in [citations-vs-recommendations.md](citations-vs-recommendations.md)) or not at all.
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## Related
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- The same "models read the text layer" logic applies to podcasts: episodes get transcribed and show notes get published, so podcast guesting is earned media that compounds in AI answers — see the `public-relations` skill's podcast guest prep reference.
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- For producing the videos themselves, see the `video` skill.
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---
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*Anatomy pattern from Ross Simmonds / Foundation Inc. ("The Anatomy of a YouTube Video AI Cites," 2026), distilled and extended with credit.*
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