القائمة
AI SEO domination system - operate at the retrieval layer where AI engines cite sources, not just the visibility layer
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Farrice/mes-knowledge-base --skill nathan-gotch-ai-seoيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| name | Nathan Gotch AI SEO |
| description | AI SEO domination system - operate at the retrieval layer where AI engines cite sources, not just the visibility layer |
| version | 3.0 |
| format | completion-engine |
| workflows | 14 |
Complete AI SEO system for dominating ChatGPT and AI search results. 34 prompts; primary-sourced
operational layer added 2026-07-19 from Gotch's 2026-07-15 video (the repo's first primary Gotch
source — see references/source-ledger.md).
Nathan Gotch's genius: AI SEO operates at the retrieval layer, not the ranking layer. Most optimize for what users see—Gotch optimizes for what AI sees before it generates answers.
Core Insight: Decisions happen at the retrieval layer, not the visibility layer — and the AI decides by consensus across its sources, so the unit of work is a third-party mention.
| # | Workflow | Produces | Use When |
|---|---|---|---|
| 01 | AI SEO Infrastructure & Audit | AI SEO Readiness Audit & Infrastructure Blueprint | Starting a new project or auditing an existing site for AI search engine compatibility |
| 02 | Strategic Retrieval & Narrative Mapping | Retrieval-Layer Strategic Narrative Map | Defining how a brand should be perceived and cited by AI models across the web |
| 03 | High-Velocity Content Production Engine | AI-Optimized Content Domination System | Executing a large-scale content strategy to flood the retrieval layer with high-quality, conversion-focused assets |
| 04 | Authority, Trust & Citation Domination | Retrieval Authority & Citation Domination Plan | Building off-page signals, trust, and mentions that force AI models to cite your brand as a top authority |
| 05 | Scaling, Automation & Omnichannel Blitz | Omnichannel AI SEO Scaling & Automation Framework | Scaling successful AI SEO tactics across multiple channels and automating workflows using AI coding and integration |
| # | Workflow | Tier | Produces | Use When |
|---|---|---|---|---|
| 06 | Category Domination Sprint | 1 | One-day category strategy: split diagnosis + citation autopsy + opportunity map + strategy doc | Starting a 90-180 day category push; the flagship operational workflow |
| 07 | AI Visibility Gap Audit | 1 | "Good SEO ≠ AI visibility" diagnosis with counted mention evidence | Brand ranks traditionally but isn't recommended by AI; prospect audits |
| 08 | Citation Opportunity Mining | 1 | Earned/owned/distribution classification of every citation URL + action list | After an audit; turning citation data into an outreach/build program |
| 09 | Competitor Alternatives Ladder | 2 | Alternatives → vs → vs-us page set per retrieval-cited competitor | Capturing competitor queries with retrieval-credible comparison pages |
| 10 | Topic Gap Map | 2 | Green-boxed coverage + granular gap inventory as one visual map | Coverage looks complete but granular intents (audience/attribute) are unbuilt |
| 11 | Benchmark-Annotate-Scan | 2 | Work-correlated tracking protocol | Installing tracking discipline; killing wasted tracking spend |
| 12 | Category Linkbait Engine | 2 | 25 grounded angles → top-5 → flagship asset via research→design chain | Building informational topic support that earns links, PR, and shares |
| 13 | Owned Media Echo | 3 | Topic × channel echo plan, format-native, retrieval-evidenced | Site topics tapped out; expanding to YouTube/FB/IG + influencer second front |
| 14 | AI-Search Shadow Receipts | 3 | Client-facing AI-visibility receipts deck on $0 tooling | Proof-to-Market Shadow module; pre-sale audits for brand buyers |
| Pair | With | For |
|---|---|---|
| 07 → 14 | proof-to-market offer, fact-verifier | Client receipts decks (verify before delivery — real brands, real counts) |
| 08 → outreach | jeremy-haynes-cold-offer, lulu-cheng | Earned-media asks that land |
| 09 + 12 | luke-iha / copy-engine, dara-denney | Comparison pages + linkbait assets written/designed at agency grade |
| 12 Phase 4 | fantastic-studio, creative_router pre-flight | The design-agent handoff inside Farrice's own creative stack |
| 13 | kallaway-content-os, jenny-hoyos-shorts, platform constitutions | Channel-native echo production |
| 06 | jensen-gotch-retrieval, ethan-smith-aeo | Platform intelligence + AEO structural layer under the sprint |
34 deterministic practitioner prompts — each carries an Output Contract, Output Skeleton, and Quality Gate. When a deliverable matches one, Read it and honor its contract instead of improvising the output shape.
skills/nathan-gotch-ai-seo/references/prompts-v2/01-ai-seo-measurement.mdskills/nathan-gotch-ai-seo/references/prompts-v2/02-citation-intelligence.mdskills/nathan-gotch-ai-seo/references/prompts-v2/03-brand-narrative.mdskills/nathan-gotch-ai-seo/references/prompts-v2/04-knowledge-base.mdskills/nathan-gotch-ai-seo/references/prompts-v2/05-content-engine.mdskills/nathan-gotch-ai-seo/references/prompts-v2/06-retrieval-domination.mdskills/nathan-gotch-ai-seo/references/prompts-v2/07-ai-coding.mdskills/nathan-gotch-ai-seo/references/prompts-v2/08-retrieval-copy.mdskills/nathan-gotch-ai-seo/references/prompts-v2/09-market-narrative.mdskills/nathan-gotch-ai-seo/references/prompts-v2/10-voc-intelligence.mdskills/nathan-gotch-ai-seo/references/prompts-v2/11-content-velocity.mdskills/nathan-gotch-ai-seo/references/prompts-v2/12-conversion-narrative.mdskills/nathan-gotch-ai-seo/references/prompts-v2/13-viral-retrieval.mdskills/nathan-gotch-ai-seo/references/prompts-v2/14-signal-intelligence.mdskills/nathan-gotch-ai-seo/references/prompts-v2/15-creative-direction.mdskills/nathan-gotch-ai-seo/references/prompts-v2/16-force-multiplier.mdskills/nathan-gotch-ai-seo/references/prompts-v2/17-technical-audit.mdskills/nathan-gotch-ai-seo/references/prompts-v2/18-competitor-seeding.mdskills/nathan-gotch-ai-seo/references/prompts-v2/19-citation-mining.mdskills/nathan-gotch-ai-seo/references/prompts-v2/20-topic-domination.mdskills/nathan-gotch-ai-seo/references/prompts-v2/21-dream-100.mdskills/nathan-gotch-ai-seo/references/prompts-v2/22-youtube-influencer.mdskills/nathan-gotch-ai-seo/references/prompts-v2/23-entity-trust.mdskills/nathan-gotch-ai-seo/references/prompts-v2/24-retrieval-sales-copy.mdskills/nathan-gotch-ai-seo/references/prompts-v2/25-competitor-narrative.mdskills/nathan-gotch-ai-seo/references/prompts-v2/26-content-ecosystem.mdskills/nathan-gotch-ai-seo/references/prompts-v2/27-buyer-pathway.mdskills/nathan-gotch-ai-seo/references/prompts-v2/28-omnichannel-blitz.mdskills/nathan-gotch-ai-seo/references/prompts-v2/29-category-sprint.mdskills/nathan-gotch-ai-seo/references/prompts-v2/30-visibility-gap-audit.mdskills/nathan-gotch-ai-seo/references/prompts-v2/31-citation-opportunity-miner.mdskills/nathan-gotch-ai-seo/references/prompts-v2/32-alternatives-ladder.mdskills/nathan-gotch-ai-seo/references/prompts-v2/33-linkbait-engine.mdskills/nathan-gotch-ai-seo/references/prompts-v2/34-benchmark-annotate-scan.mdThe /generate front door — in-house, pay-as-you-go creative generation (Higgsfield replacement). One command for image, video, and audio: routes each request to the right model recipe (skills/generate/models/*.json), defers to binding creative_router lanes (people → fal-people, style-family → fantastic-posters; Higgsfield fully retired 2026-08-06), quotes paid video before running, honors prompt-level budgets ("total budget $3"), runs comparison batches across models, logs every prompt beside its asset, and auto-refreshes the Asset Command Center (/assets-board). Use when the user says "generate", "make an image/video/voiceover", "create ads/assets/creative", names a model (recraft, kling, seedance, nano banana, gpt image), or asks for a multi-model comparison. Source: RoboNuggets 9C4TRbucmhQ, rebuilt on our fal-first stack.
The Edit Bay — in-house conversational video studio (VOX-style explainers, documentaries, shorts, LinkedIn video). Transcript-driven cutting, B-roll ladder, motion graphics as code, captions, music, publish-ready packages — all agent-operated, free-first. Use when Farrice says "edit this video/footage", "make an explainer/documentary/short", "cut this recording", "add B-roll/captions/music", "turn this essay/post into a video", or any video production ask. Front door for three modes: talking-head, VO-only, zero-camera (his recorded VO + assembled visuals — NO voice clone, NO TTS narration, his ruling 2026-08-06). Source: Brad Bonanno agentic edit pipeline (extractions/brad-bonanno-edit-bay/), rebuilt on the house stack: WhisperX local + edit_bay.py ffmpeg assembly + HyperFrames/Remotion graphics + /generate (fal) — NO Higgsfield (retired 2026-08-06).
Architects technical explainer videos using Brad Bonanno's structural patterns — survives short attention spans, communicates complete systems (not just teasers), and functions as a channel-building artifact (one video pulls subscribers, not just views). Use when planning a long-form technical explainer (5-20 min) for YouTube, structuring an explanation of a complex system the user knows but can't teach yet, balancing depth vs accessibility for a technical audience, or building an evergreen video meant to compound subscriber growth over years rather than spike then die. Trigger proactively whenever the user says "explainer video", "technical explainer", "how do I explain X", "long-form YouTube", "evergreen video", or has a complex system that needs to land with a non-expert. For Instagram-format shareworthy content use brock-johnson-shareworthy-content; for short-form video patterns use a TikTok/Reels-specific skill.
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