YouTube Citation Playbook Skill
SkillSearchYouTube citation playbook for AI search. YouTube is the most-cited domain in Google AI Overviews and the strongest single brand-visibility signal measured. Optimizes videos for AI citation through transcripts, chapters, titles, and cross-posted text claims -- not views or subscribers. Use when planning video content for AI visibility or auditing an existing channel for citation readiness.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the YouTube Citation Playbook Skill skill
What this skill tells your AI
The instructions your AI receives, as published by thesmokedev/geo-skills in skills/geo-youtube/SKILL.md and read by ahel’s review.
Purpose
This skill turns a YouTube channel into an AI citation source. AI engines do not watch video -- they read the transcript, chapters, title, and description. That makes YouTube a rank-free bypass lane: a channel with no domain authority and few views can earn AI citations that the site's text pages cannot. This skill audits existing videos for citation readiness and produces a build spec for new ones.
Core Insight
YouTube completed the flip in 2026 and is now the dominant non-text citation source:
- Most-cited domain in Google AIOs overall, with share up +34% over 6 months (Ahrefs Brand Radar, 2026).
- #1 social citation source at 38.1% of social citations, overtaking Reddit (5WPR State of AI Citations, May 2026).
- ~23% of finance-vertical citations -- ahead of Wikipedia (7.3%) and LinkedIn (6.8%) (Surfer 46M-citation dataset, 2026).
- 18.2% of AIO citations from beyond the organic top 100 are YouTube URLs (Ahrefs, 863K SERPs / 4M URLs, Mar 2026) -- the clearest rank-free lane in AI search.
- YouTube mentions correlate r=0.737 with AI visibility -- the strongest single signal ever measured, ahead of branded web mentions (0.664) (Ahrefs 75K-brand study via MachineRelations, Jul 2026).
The mechanism that matters: AI reads the transcript, chapters, title, and description -- NOT the video itself. Views and subscriber counts show near-zero correlation with citation (⚠️ single source, AIOCopilot Apr 2026 -- but mechanism-consistent, since engines have no visibility into watch metrics at retrieval time).
Citation-Readiness Rubric (per video)
Score each video 0-100 across five components:
| Component | Weight | Full Marks | Zero Marks |
|---|---|---|---|
| Transcript quality | 30% | Human-corrected transcript with citable numeric/definitional sentences | Auto-captions left uncorrected, or no captions |
| Chapter structure | 25% | 6-12 chapters with descriptive, query-matching titles | No chapters, or generic titles ("Part 1", "Intro") |
| Title match | 20% | Title is verbatim (or near-verbatim) the target query | Clever/branded title with no query language |
| Length & depth | 15% | 10+ minutes, substantive per-chapter content | Under 3 minutes, or a Short |
| Cross-posting | 10% | Video's claims exist as a text page + community answer | Video exists in isolation |
Shorts are never cited -- exclude them from the audit entirely (AIOCopilot, Apr 2026).
Audit Procedure (Existing Channel)
- List the channel's long-form videos (10+ min candidates first).
- For each video, fetch title, description, chapter list, and transcript (YouTube timedtext API or transcript endpoints).
- Check the transcript for:
- Auto-caption errors (numbers, brand names, and technical terms mangled) -- flag for human correction.
- Citable sentences: numeric claims and definitional statements, especially within ~30 seconds of chapter boundaries (citations attach to timestamps).
- Check chapter titles: each should read like an H2 that matches a fan-out sub-query (see
skills/geo-fanout/for sub-query mapping). "How much does SR-22 cost in California?" beats "Pricing section". - Check title against the target query: verbatim or near-verbatim match is the goal.
- Check cross-posting: do the video's core claims exist as a text page on the site and as a community answer (Reddit/Quora/forum)? ChatGPT cites YouTube mainly when the video is discussed in text elsewhere (AIOCopilot, Apr 2026).
- Score per the rubric; produce the fix list ordered by transcript quality first.
Build Spec (New Videos)
For each target query, spec a video as follows:
- Title: verbatim the target query. "How Much Does SR-22 Insurance Cost in California? (2026)" -- not "Everything You Need to Know About SR-22!".
- Length: 10+ minutes. Shorts never cited; long-form gives chapters room to work.
- Chapters: 6-12, each titled like an H2 matching a fan-out sub-query (cost, eligibility, process, location, language variants -- see
skills/geo-fanout/). Citations attach to timestamps, so chapter titles are the retrieval surface. - Script for citable sentences: place numeric and definitional claims near chapter boundaries. "The average SR-22 filing fee in California is $25, and SR-22 insurance raises premiums by 40-80% depending on the violation" is citable; "it can get pretty pricey" is not. Apply the
skills/geo-citability/passage rules to the script. - Transcript: upload a human-corrected transcript. Do not rely on auto-captions -- mangled numbers destroy citability.
- Description: front-load the same numeric/definitional claims; engines read it.
- Production: a faceless explainer with a corrected transcript qualifies -- production value is not the ranking lever here (AIOCopilot, Apr 2026).
- Cross-post (mandatory): publish the video's claims as (a) a text page on the site and (b) an authentic community answer (Reddit, Quora, niche forum) linking or naming the video. Text discussion is what makes ChatGPT surface the video.
Caveat: Treat YouTube Citation Share as a Live Experiment
YMYL video-citation share swung 50+ percentage points in 3 months in BrightEdge's healthcare tracking (Jan 2026). Platforms are actively A/B testing how heavily they cite video. Do not promise a stable citation share to clients; track the channel's actual citation rate over time with skills/geo-measurement/ and rebalance if the lane narrows.
Output Format
Generate a file called GEO-YOUTUBE-CITATION.md:
# YouTube Citation Audit: [Channel/Domain]
**Analysis Date:** [Date]
**Videos Audited:** [N] long-form (Shorts excluded -- never cited)
**Average Citation-Readiness Score:** [X]/100
---
## Per-Video Scores
| Video | Transcript (30) | Chapters (25) | Title (20) | Length (15) | Cross-Post (10) | Total |
|---|---|---|---|---|---|---|
| [Title] | [X] | [X] | [X] | [X] | [X] | [X]/100 |
## Priority Fixes
1. **[Video]** -- [e.g., "Auto-captions mangle every dollar figure; upload corrected transcript"]
2. **[Video]** -- [e.g., "Retitle to verbatim target query: '...'"]
3. **[Video]** -- [e.g., "Add 8 chapters titled as fan-out sub-queries"]
## New Video Build Specs
| Target Query | Title | Chapters | Key Citable Claims | Cross-Post Plan |
|---|---|---|---|---|
| [query] | [verbatim title] | [6-12 chapter titles] | [numeric/definitional sentences] | [text page + community answer] |
## Volatility Note
[Reminder: YMYL video-citation share swung 50+ points in 3 months (BrightEdge, Jan 2026).
Track actual citations with geo-measurement; do not promise stable share.]
Related Skills
skills/geo-fanout/-- supplies the sub-queries that chapter titles and video titles should match.skills/geo-citability/-- passage rules apply verbatim to scripts, transcripts, and descriptions.skills/geo-brand-mentions/-- YouTube mentions are the strongest measured brand signal (r=0.737); the mention strategy and the citation strategy reinforce each other.skills/geo-measurement/-- track YouTube citation share over time given the documented volatility.
Signals
- GitHub stars
- 22
- Forks
- 6
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
geo-youtube- Source
- github.com/thesmokedev/geo-skills