Youtube seo

YouTube SEO Orchestrator (Advanced)

Master skill for YouTube SEO work. Detect intent, delegate to the right sub-skill, and provide the shared advanced ranking model that every sub-skill references. If intent is ambiguous, ask once before proceeding.

Routing Table

User intent / input	Sub-skill
"audit my channel", channel URL alone, "full YouTube check"	youtube-seo-audit
Single video URL, "analyze this video", "why isn't this ranking"	youtube-seo-video
"optimize title/description/tags", "rewrite metadata", "improve CTR copy"	youtube-seo-optimize
"channel branding", "about page", "banner", "playlists", "channel trailer"	youtube-seo-channel
"YouTube keywords", "topic research", "what should I make a video about"	youtube-seo-keywords
"thumbnail review", "CTR thumbnail", "is my thumbnail good"	youtube-seo-thumbnail
"competitor analysis", "what are [channel] doing", "competing videos"	youtube-seo-competitor

Advanced Ranking Model

YouTube's recommender is NOT a keyword-match system. It is a multi-surface reinforcement-learning recommender that optimizes for long-term user satisfaction (the "Reinforce" paper, Covington et al. 2016 + DRL updates). Each surface has its own objective:

Surface	Primary objective	Dominant signal
Browse (Home feed)	Session watch time + return rate	CTR-on-impression, persona match, freshness
Suggested (sidebar / autoplay)	Next-video watch time	Topical adjacency, session continuation, co-view graph
Search	Query satisfaction	Keyword/entity match, APV for that query, click depth
Shorts feed	Swipe-through rate + watch loops	Hook in <1s, loopability, audio trend
Notifications	Open rate in first hour	Subscriber affinity, bell-on CTR history
External	Retention of new viewers	Intro strength, subscribe-from-external rate

A video can be strong on one surface and dead on others. Always ask which surface matters most for the user's goal before optimizing.

Tier 1 — Watch-time & session signals (dominant)

• CTR by surface (Browse CTR ≠ Search CTR ≠ Suggested CTR)
• APV (Average Percentage Viewed) — the single best retention metric for videos <20 min. Benchmarks:
  • Excellent: ≥55% APV
  • Good: 45-55%
  • Needs work: 35-45%
  • Bad: <35% (algorithm suppresses distribution)
• AVD (Average View Duration) — use for videos >20 min, target >8 min for mid-roll ad revenue floor
• Intro retention at 0:30 — target ≥70% of viewers still watching
• Retention cliff detection — any point where retention drops >10% in <5 seconds is a structural problem
• Session watch time contribution — does this video lead to another view on YouTube? Studio "Suggested videos" + "Browse" outbound CTR proxy this
• Returning viewer rate — % of viewers who come back within 7/28 days
• Relative performance — vs your own channel median and vs niche median for the same length bucket

Tier 2 — Metadata & semantic signals

• Title: 60-70 char sweet spot (mobile cutoff ~56 on small screens, ~70 on desktop). Keyword in first 40 chars for Search; emotional driver first for Browse.
• Description:
  • Above-fold (first 150 chars): restates title intent, includes primary keyword, gives a click-for-more reason
  • Full body (1,500-4,000 chars): semantic entity coverage (see below), natural density, not stuffed
  • Key Moments / chapters: first timestamp MUST be 0:00, ≥3 chapters, each ≥10s, descriptive labels. Triggers Google Search "Key Moments" rich result and VideoObject.hasPart[].Clip schema eligibility.
  • Links block: grouped, labeled, with FTC disclosure if affiliate
  • Hashtags: max 3 meaningful (shown above title); first is strongest
• Tags: de-emphasized in 2020 but still used for typo/spelling disambiguation and topic classification. 5-15, first = exact keyword, total <500 chars.
• Entity/semantic coverage: YouTube uses Knowledge Graph entities (people, brands, places, concepts). Mention 5-10 related entities in the description/captions to strengthen topic classification. Example: for a "Ryzen 9 review", include Intel, TDP, chiplet, AM5, Zen architecture.
• Captions: manually uploaded .srt/.vtt outrank auto-captions for Search. Translated captions unlock international impressions.
• Translated metadata: Custom Channel Translations for top 5 viewer languages typically lift international impressions 20-50%.
• Category: correct primary category
• Audio language tag + default language set correctly

Tier 3 — Thumbnail / pre-click signals

• 1280x720, <2MB, JPG/PNG, 16:9
• Readable at 120px wide (mobile feed) — the binding constraint
• Face + strong emotion lifts CTR ~20-30% in most niches (exceptions: product, gaming, tutorial close-ups)
• ≤4 words text, ideally ≤3, bold sans-serif ≥80pt effective
• SERP differentiation: must pattern-break against the top-10 thumbnails for the target keyword (CLIP-embedding or manual grid check)
• Native A/B test via Studio "Test & Compare" (3 variants, 2-week window)
• No bait-mismatch with title or content — long-term CTR decay if broken

Tier 4 — Engagement & velocity signals

• Like ratio vs channel baseline (not absolute count)
• Comment velocity in first 60 min, creator reply rate
• Pinned comment engagement (replies to pinned)
• Shares, saves-to-playlist, playlist-add events
• End-screen CTR (slots filled; hotspot 60-70% through video)
• Card CTR at attention moments
• Subscribe-from-video rate (per 1,000 views)
• Bell-notification open rate (for subscribed audience)
• First-24h velocity — for trending/topical content this is 60-80% of lifetime distribution; for evergreen, 10-20%

Tier 5 — Channel-level signals

• Topical authority: concentration of topic clusters (YouTube classifies channels by topic IDs inherited from Knowledge Graph)
• Upload cadence consistency: predictable rhythm feeds the Browse surface
• Channel persona profile: who the algorithm believes watches you
• Playlist binge-ability (session chains)
• Cross-video retention (do viewers of video A also finish video B?)
• Subscriber growth slope (more signal than raw count)
• Community tab engagement (pre-upload momentum)
• Verification + monetization status
• Strike / community-guidelines standing

Tier 6 — Technical / safety signals

• Video format: 1080p minimum (4K bonus on supporting devices)
• Audio loudness: target -14 LUFS (YouTube's normalization target). Under -16 LUFS feels quiet and correlates with lower retention.
• Duration: >8 min unlocks mid-roll ads; >10 min is the legacy watch-time optimum; <60s (vertical) goes to Shorts feed
• Made-for-Kids flag: must match content truthfully; incorrect setting disables engagement features and suppresses discovery
• Altered/synthetic content disclosure for AI-generated video/voice
• Copyright claims (Content ID): block/monetize/track status affects revenue share and can cap reach
• Embedding enabled (third-party embed views count)
• Comments enabled with active moderation

Shorts-specific signals

• First 0.5-1.0s hook — swipe-away rate here is the #1 metric
• Loop rate — the video should end where it starts (narratively or visually)
• Vertical 9:16, 1080x1920
• Caption overlay for silent viewing (most Shorts are watched muted)
• Audio trend — use trending audio from the Shorts audio library (boost) OR original audio that can be remixed
• Title: ≤40 chars, emotional hook front-loaded
• #Shorts in description or title (no longer required but still de-risks classification)
• Length: 15-30s has highest loop rate; 45-60s has highest watch time. Pick based on goal (discovery vs watch time)

Data Sources

First-party only. These skills deliberately avoid third-party analytics tools (VidIQ, TubeBuddy, Ahrefs, SocialBlade, NoxInfluencer, HypeAuditor, etc.) — they rate-limit, change their HTML, or return errors. Every source below is an official Google/YouTube endpoint, a local CLI, or data the user provides directly.

Priority order (degrade gracefully from top to bottom):

Source	Use	How
YouTube Data API v3	Full snippet, tags, statistics, topicDetails, contentDetails, playerCaptions, commentThreads, search, channels, playlistItems	YOUTUBE_API_KEY env var + scripts/fetch_video.py / scripts/fetch_channel.py
YouTube Studio CSV exports (user-provided)	Real CTR, APV, retention curves, traffic sources, audience, impressions — the only source for Tier 1 signals	Ask user to export from Studio → Analytics → Advanced Mode → download CSV
yt-dlp	Full metadata, auto + manual captions, chapters, transcript, audio extract, ytsearch: SERP, channel playlists — works without an API key	yt-dlp --dump-json --skip-download URL, yt-dlp "ytsearch50:query" --flat-playlist -J
YouTube suggest API	Keyword expansion (no key, no rate limit in practice)	suggestqueries.google.com/complete/search?client=firefox&ds=yt&q={seed}
Google Trends (WebFetch)	Trending vs evergreen, seasonality, breakout topics	trends.google.com/trends/api/explore...
WebFetch on watch / channel / results page	Public title, view/like counts, upload date, visible description, SERP results — last resort, structure can change	youtube.com/results?search_query=..., youtube.com/@handle
Whisper (local or API)	Transcript fallback when captions unavailable	whisper --model base --language en
OpenCV + CLIP	Face detection, emotion, thumbnail-similarity vs SERP	scripts/analyze_thumbnail.py
FFmpeg / loudnorm	Audio loudness (LUFS), true peak, loudness range	ffmpeg -i IN -af loudnorm=print_format=json -f null -
seo-dataforseo MCP (OPTIONAL — only if explicitly available and not erroring)	Extra YouTube SERP positions and volume estimates	serp_youtube_organic_live_advanced, keywords_for_youtube — skip on any error, do not retry

Data Source Matrix (what you need for what)

Analysis	Minimum data	Ideal data
Metadata score	WebFetch only	API + Studio CSV
Retention diagnosis	Studio CSV	Studio CSV + transcript
Thumbnail score	Image URL	Image + SERP grid + CLIP embeddings
Competitor intel	API + WebFetch	API + yt-dlp transcripts + Studio benchmarks
Audio/loudness	yt-dlp audio extract	FFmpeg loudnorm pass

If critical data is missing, ASK for it before analyzing — do not guess Tier 1 signals.

Niche Benchmarks (APV, CTR, like ratio)

Benchmarks vary. When possible, compute live from top-10 SERP for the target keyword. Use these as fallback medians:

Niche	CTR (Browse)	APV (long-form)	Like/view ratio
Tech review	4-8%	40-50%	3-5%
Gaming	5-10%	45-55%	4-6%
Education / how-to	4-7%	40-50%	4-6%
Vlog / lifestyle	3-6%	35-45%	3-5%
Finance / business	5-9%	45-55%	3-5%
Music / entertainment	6-12%	50-65%	5-8%
Kids (MFK compliant)	8-15%	55-70%	N/A (disabled)
Shorts (any)	8-20%	80-100%+ (loops)	5-10%

If the user's numbers are 1.5x median, recommend scaling (more similar content); if <0.7x median, recommend structural change.

Output Conventions (all sub-skills must follow)

1. Score card (0-100) with weighted sub-scores
2. Issues organized Critical → High → Medium → Low, each with an estimated impact (CTR%, APV%, impressions%)
3. Paste-ready artifacts (titles, descriptions, tags, schema) in fenced code blocks
4. Rationale block citing which Tier signal each fix targets
5. Measurement plan: which Studio metric to watch after the change, over what window, with what success threshold

Error Handling

Fail loud, degrade gracefully, never fabricate. If a third-party tool (DataForSEO MCP, any scraper, or an optional service) errors, skip it and continue with native sources — do not retry, do not block the run.

Scenario	Action
Video private/unlisted	Ask user to make unlisted-shareable or paste raw metadata + Studio CSV
No API key AND WebFetch blocked	Fall back to yt-dlp (--dump-json, ytsearch:); if still failing, ask user for a metadata paste — never fabricate tags/views
DataForSEO MCP or any optional tool errors	Log it, skip that source, continue with YouTube Data API + yt-dlp
yt-dlp fails on a single video	Retry once with --extractor-args "youtube:player_client=web"; on second failure, skip that video and note it
API quota exhausted	Switch to yt-dlp + suggest API; report which checks are degraded
Age-restricted / region-blocked	Note limitation; analyze what is accessible
Channel <10 videos	Focus on channel setup, keyword research, and format selection — not ranking diagnosis
Studio CSV not provided but user asks for retention diagnosis	Explicitly refuse to guess; ask for the export
MFK channel	Skip engagement/comment analysis (disabled); focus on thumbnail, title, playlist binge
User asks for SocialBlade/VidIQ/TubeBuddy/Ahrefs data	Explain these are not used (rate-limit / error-prone); offer the native equivalent (API + yt-dlp) instead