The Programmatic SEO Expert — a Claude Skill

A Claude Agent Skill for building programmatic SEO (pSEO) page systems that survive Google's 2026 quality bar AND get cited by AI search engines (ChatGPT, Perplexity, Gemini, AI Overviews, Claude).

The 2026 reality in one line: the bar moved from "pages at scale" to "useful products at scale." AI, templates, and automation are method-neutral — Google and LLMs both enforce and reward the same thing: unique value per page backed by a data moat. A page built right for ranking is simultaneously built right for RAG retrieval. This skill encodes that playbook.

What it covers

• The 5 pillars — data moat · server-rendered HTML · answer-first semantic chunking · disciplined index management · dual (organic + AI) measurement.
• Is pSEO even a fit? — good/bad-fit gating, the destination & bookmark tests.
• A staged, stack-agnostic pipeline — qualify → data layer → templates → generate+QA → technical → AI-search → index management → measure, with a 4-stage rollout (don't launch 50,000 pages on day one).
• GEO/AEO/LLMO — how RAG engines retrieve passages, the peer-reviewed KDD 2024 GEO levers (with misquote warnings), AEO formatting, the schema debate, the llms.txt reality, robots.txt for AI bots, and AI-visibility measurement.
• Real case studies — Zapier, Zillow, NerdWallet, Tripadvisor, G2, Canva, and the failure patterns.

Install

Via the skills.sh CLI:

npx skills add thevrus/Programmatic-SEO-Expert

Or manually — clone into your agent's skills directory:

git clone https://github.com/thevrus/Programmatic-SEO-Expert.git \
  ~/.claude/skills/programmatic-seo-expert

Then invoke it by asking Claude anything about programmatic SEO, pSEO, pages at scale, GEO/AEO/LLMO, or getting cited by AI search engines.

Structure

.
├── SKILL.md                       # Orchestrator: pillars, fit test, pipeline, rollout, hard rules
├── references/                    # Progressive-disclosure detail (loaded on demand)
│   ├── algorithm-and-quality.md   # Google HCU→core, scaled-content-abuse, 2024–26 updates, thresholds, risk
│   ├── keyword-data-templates.md  # head-term+modifier patterns, SERP-overlap clustering, the data moat, E-E-A-T
│   ├── technical-seo.md           # rendering/SSR (#1 AI move), crawl budget, index bloat, sitemaps, linking
│   ├── ai-search-optimization.md  # GEO/AEO/LLMO, the GEO paper, schema debate, llms.txt, robots.txt, measurement
│   ├── measurement-and-iteration.md # monitoring, GSC at scale, log analysis, pruning, attribution, KPIs
│   └── case-studies.md            # wins and failures
├── scripts/
│   └── check-ai-rendering.sh      # fetch a URL as Googlebot/GPTBot/ClaudeBot; flag client-side rendering
└── assets/
    └── robots-ai.txt              # ready-to-customize 2026 "block training, allow retrieval" robots.txt

Quick taste of the tooling

# The single highest-leverage AI check: is your content in the raw HTML
# that JS-blind AI crawlers actually see?
bash scripts/check-ai-rendering.sh https://yoursite.com/some/programmatic/page

A note on sources

The rigorous anchors are the GEO paper (Aggarwal et al., KDD 2024, arXiv:2311.09735), SparkToro/Similarweb zero-click data, the Zhao/Berman SSRN paper, Pew, Gartner, and Google's own documentation. Many headline figures come from vendor/agency studies and are flagged in-skill as directional. AI search changes monthly — re-verify crawler names, schema's role, and llms.txt adoption before acting.

License

MIT — see LICENSE.