Shopify Geo Audit

shopify-geo-audit

Audit any Shopify store for AI-search readiness, then generate the fixes you actually paste in.

npx shopify-geo-audit https://your-store.com

No account, no API key, no config. It fetches the storefront, runs a set of checks for the signals that ChatGPT, Perplexity, Gemini and Google's AI Overviews read, scores the store 0-100, and writes paste-ready fixes to ./geo-audit-output/.

Want it on your PATH instead of npx? npm i -g shopify-geo-audit.

Why I built this

I do a lot of Shopify work, and "are we showing up in AI answers?" became a real client question fast. The field goes by a pile of names — GEO, AEO, AI SEO, LLM SEO, answer engine optimization — but the question is the same: when someone asks ChatGPT or Perplexity for product recommendations, is your store citable? The existing tools mostly hand you a dashboard that says you're invisible and stop there. The useful part, the part that takes an afternoon by hand, is producing the actual structured data and config. So I wrote the thing that does that part.

It's deliberately small and offline. One command, runs locally, nothing leaves your machine except the requests to the store you're auditing.

What it checks

Nine checks, weighted by how much they matter for getting cited:

The result is an SRO Score (Search Readiness for AI): 80-100 strong, 50-79 needs work, below 50 at risk.

The premise — that deliberate changes to your pages measurably move how often engines cite you — comes from the original GEO paper (Aggarwal et al., KDD 2024), which measured visibility gains of up to ~40% from exactly this kind of work.

What it generates

The output isn't a report you read and forget. It's files:

• product-jsonld.html — a valid Product block filled from your store's real data, ready for your theme
• llms.txt — a content manifest for your domain
• robots-fixes.txt — the exact lines to stop blocking AI crawlers
• priority-list.txt — every failing check, ranked, each with a one-line fix

Pass --html and you also get a self-contained report.html you can screenshot or send a client:

Example

$ npx shopify-geo-audit https://acme-coffee.com

────────────────────────────────────────────────────────────

Acme Coffee https://acme-coffee.com/

SRO Score: 41/100 ■ AT RISK

────────────────────────────────────────────────────────────

✗ HIGH Product structured data (JSON-LD) No valid Product JSON-LD found on 4 product pages. AI can't parse your products. ✗ MED llms.txt present /llms.txt not found — no content manifest for AI crawlers. ✗ MED Product description depth (answer-first content) Avg 38 words per product page — too thin. ✗ HIGH AI crawler access (robots.txt) robots.txt blocks: GPTBot, ClaudeBot. ✓ MED Title, meta description, canonical, Open Graph ✓ LOW Sitemap reachable (/sitemap.xml)

────────────────────────────────────────────────────────────

Generated fixes → ./geo-audit-output/ product-jsonld.html · llms.txt · robots-fixes.txt · priority-list.txt

Usage

shopify-geo-audit <url> [options]

-n, --products how many product pages to audit (default: 5) -o, --out where to write the fixes (default: ./geo-audit-output) --html also write a self-contained report.html --json print raw results as JSON (for CI); fixes still get written --min-score exit 1 if the score is below n (0-100)

--json keeps stdout clean so you can pipe it, and --min-score lets CI fail on weak audits:

shopify-geo-audit https://store.com --json --min-score 80 | jq '.score.value'

MCP server

The audit also runs as an MCP tool, so Claude, Cursor or any MCP client can audit a store mid-conversation:

{

"mcpServers": { "shopify-geo-audit": { "command": "npx", "args": ["-y", "shopify-geo-audit-mcp"] } } }

One tool, audit_shopify_store — takes a URL, returns the full results and generated fixes as JSON. Same pipeline as the CLI, nothing else running. The server also ships in the main package as the shopify-geo-audit-mcp binary if you'd rather not pull a second package.

How it works

It's a straight pipeline with the side effects pushed to the edges:

fetch → parse → checks[] → score → fixers[] → report

Checks are pure functions (store) => result, one per file, so each is trivially testable and adding a new one is a single module. Fetching is SSRF-guarded (no requests to private or reserved addresses, redirects re-validated each hop) and everything parsed off the network is validated with zod before it's trusted.

Development

git clone https://github.com/builtbyabs/shopify-geo-audit.git

cd shopify-geo-audit npm install npm run build node bin/cli.js https://some-store.com

npm test # vitest, one spec per check npm run typecheck # strict, no any

FAQ

Is this just SEO with a new name? Overlapping, not the same. A store can rank fine on Google and still hand ChatGPT nothing it can quote — no Product JSON-LD, thin descriptions, a robots.txt that blocks the crawlers. The checks here are specifically about what AI engines parse and cite.

Why Shopify only? Because Shopify is predictable. Product discovery uses /products.json (with a sitemap fallback), and the generated fixes assume Shopify's theme conventions, so they actually fit instead of being generic advice. It will run against any URL, but on a non-Shopify site the product-level checks won't find much.

Does any of my data leave my machine? No. The only network requests are to the store you're auditing. No telemetry, no account, nothing phoned home.

My score is low. Where do I start? priority-list.txt in the output folder — it's every failing check ranked by weight, each with a one-line fix. The two highs (Product JSON-LD, robots.txt) are usually an hour of work combined.

Contributing

Adding a check is the easiest contribution: drop a file in src/checks/, add a fixture and a test, wire it into src/index.ts. See CONTRIBUTING.md. Issues and PRs welcome; good first issue is tagged.

And if the tool saved you an afternoon, a star is how other Shopify folks find it.

License

MIT © Abhishek Chauhan