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 validProductblock filled from your store's real data, ready for your themellms.txt— a content manifest for your domainrobots-fixes.txt— the exact lines to stop blocking AI crawlerspriority-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 <n> how many product pages to audit (default: 5)
-o, --out <dir> 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 <n> 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