# Vault Agentic Geo > As of: 2026-05-16 - Skill: `ferroxlabs/vault-agentic-geo` (Agent Skill) - Install (CLI): `npx skillmds@latest add ferroxlabs/vault-agentic-geo` - Raw SKILL.md: https://api.skillmd.com/api/skills/ferroxlabs/vault-agentic-geo/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: FerroxLabs (https://skillmd.com/u/ferroxlabs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ferroxlabs/vault-agentic-geo --- As of: 2026-05-16 # agentic-geo **Mode skill.** Default-enabled on the Storefront specialist. ## When to use Use when the user wants to be found by AI shopping agents and generative answer engines — ChatGPT shopping, Perplexity, Gemini, and agentic-checkout flows that became material traffic sources in late 2025 / early 2026. Use alongside `seo-organic` (Beacon); this is the storefront-side complement. Trigger phrases: - "How do we show up in ChatGPT shopping results?" - "Optimize our products for AI search." - "We need to be discoverable to AI agents." - "Buyers ask AI for recommendations — how do we land in those answers?" ## Procedure **1. Establish the agentic baseline.** Probe the surfaces first. Run 15-30 buyer-language queries through ChatGPT, Perplexity, and Gemini shopping. Per query, log: did the brand appear? Position? Framing? Against which competitors? This is rank-tracking for generative surfaces. **2. Make the catalog machine-readable.** Agents read structured data more reliably than rendered HTML. Per priority SKU: - Product schema (JSON-LD): name, description, brand, sku, gtin, offers (price, currency, availability), aggregateRating, review. - Plain-prose description — declarative, specific, free of marketing fluff. Lead with what the product *is*. - Explicit "best-for" framing: who it serves, what use case, what it is *not* for. Agents extract these as filters. - Honest named comparisons ("comparable to X in feature Y; differs in Z"). Agents reason comparatively. **3. Optimize the product feed.** Agentic checkout flows (Shopify's March 2026 agentic-storefront release; Amazon Rufus; Walmart Sparky) route via feeds — Google Merchant Center, Shopify's agent feed, Meta's shop feed. Audit feed fields: title (front-load brand + product type + key spec), description (200-500 words, plain language), price, availability, GTIN, high-res image URL, category, attribute fields (color, size, material, age group). Missing fields silently exclude you. **4. Earn citation surface outside your domain.** Generative engines weight third-party signals heavily. Priority sources: published reviews on category authority sites, Reddit and category-forum mentions, comparison articles, YouTube reviews with transcripts, podcast mentions with show notes. A Reddit thread where a buyer asks "what's the best X for Y" and your brand is named with a clear reason often outweighs any on-site move. **5. Measure visibility, not rank.** Generative surfaces lack stable position. Per query, track appearance rate across N runs, framing quality (positive / neutral / negative), citation source. Re-run monthly. Report trend, not point estimates. ## Decision rules - **Plain prose beats marketing copy on agentic surfaces.** "Wool mid-layer designed for sub-zero hiking" beats "Conquer the elements with our premium wool mid-layer." - **Structured data is the first move, always.** No JSON-LD product schema = invisible to most agents. Fix this before any other GEO work. - **Reddit and category forums are part of your SEO surface now.** Engineering authentic presence in buyer communities is the highest-payoff GEO move for most brands. - **As of 2026-05-16:** Shopify's agentic-storefront layer (released March 2026) exposes a dedicated agent feed separate from the standard product feed. Brands not opted in are invisible to Shopify-mediated agent traffic. Check opt-in status before any other GEO work on Shopify stores. - **Do not chase every agent.** Prioritize the surfaces where your buyers actually research. If your category has zero presence on Perplexity but heavy presence on ChatGPT shopping, allocate accordingly. ## Anti-patterns - Stuffing the product description with keywords the way 2015-era SEO did. Generative engines penalize that pattern and your description reads badly to humans too. - Treating GEO as a one-time project. Surfaces change monthly; this is operating cadence, not a launch. - Optimizing for AI surfaces while letting the human-readable PDP rot. The buyer still has to convert after the agent surfaces you. - Buying "AI SEO" services that promise rank guarantees on generative surfaces. There is no stable rank to guarantee. - Quoting any tactic from before mid-2025 without dating it as potentially stale. The surfaces did not exist in their current form then. - Ignoring Reddit and category forums because they "feel off-brand." That is where the agent's training data lives. ## Before / after **Brief:** "We want to show up when people ask ChatGPT for products like ours." **Before** (vague): > *Add AI keywords to descriptions and write a blog post.* **After** (probe-first, structured, 2026-05-16): > *Step one: run 20 buyer queries through ChatGPT shopping, Perplexity, and Gemini; log appearance rate as baseline. Step two: audit Product JSON-LD on top 10 SKUs — confirm name, description, gtin, offers, aggregateRating populate. Rewrite descriptions in declarative prose with "best-for" framing. Step three: confirm Shopify agentic-storefront opt-in (released March 2026; many stores default off). Step four: identify three forums and one subreddit where buyers ask comparison questions; build a 90-day authentic-presence plan with Copy. Measure: re-run the probe set at days 30, 60, 90; report appearance-rate and framing-quality deltas per query.*