6 Claude prompts to check if your Shopify store is AI-ready
One prompt scores your store. The next five fix it.
An AI shopping agent reads a product once and decides right there whether it can match it to what somebody asked for. It doesn't ask a follow-up question if something's missing, and it doesn't come back later to check.
Whatever fact it needed either sat in the product data already, structured in a way it could parse, or the agent picked a different brand to recommend instead.
I built 6 prompts that score exactly how legible a store is to that kind of read.
They're meant to run in order rather than picked one at a time.
Get the number first, find out where it comes from, turn what you find into a sequence you can work through, then the fixes or hand it to the team.
1/ Full-store agent-readiness scorecard
Page speed has a score. So does SEO, and sometimes a Merchant Center diagnostic gets run too. None of those check whether an AI agent, shopping on behalf of someone else, could read the store well enough to act on it.
That's a different question, and it doesn't have a standard answer yet.
This prompt builds that answer from scratch.
It looks across every dimension that decides whether an agent can read and match a product, weights the ones that matter most for legibility, attributes, structured data, taxonomy, more heavily than the rest, and tells you what weighting it used.
That way the final number is something you can check for yourself.
The prompt:
Score {store} for agent readiness across {dimensions}. Rate each dimension 0–100 with a one-line reason and the single highest-impact fix. Compute a weighted overall readiness score. An agent can only sell what it can read, so weight legibility dimensions (attributes, structured data, taxonomy) most heavily. State the weighting you used.
What comes back:
→ One overall score, 0 to 100
→ The weighting behind that score
→ Every dimension scored on its own, weakest first
→ The single highest-impact fix instead of a list of ten things to fix
Don't want to go through Claude? Our Shopify audit tool runs a readiness check on your store for free, no setup or login required.
2/ Structured data and schema audit
Structured data is the layer built for machines to read. JSON-LD markup embedded in the page states plainly what a product is, its type, its price, whether it's in stock, instead of leaving an agent to infer that from marketing copy.
A blank field leaves a hole an agent has to work around. A field that contradicts what the page shows is worse, because the agent ends up working from wrong information.
The prompt:
Audit {store} (or {product_url}) for structured data across {schema_types}. Report which schema types are present, which required properties are missing, and where markup contradicts the visible content. Contradictions are worse than absence because they mislead the agent. Separate missing from broken.
What comes back:
→ Which schema types are present
→ Which required properties are missing from each one
→ Anywhere the markup says one thing and the page shows something else
→ One line stating schema health as good, partial, or poor
3/ Search and filter coverage audit
A person browsing a category page scrolls, glances, maybe narrows things down with a filter if the page is big enough to bother.
An agent doesn't work that way.
It goes straight to a facet, price, size, material, whatever a shopper in that category would normally filter on, and it expects that facet to return something.
A facet that technically exists but only has the underlying attribute filled in on a small fraction of products behaves exactly like a facet that isn't there. Filtering by it returns nothing, and an agent reads that the same way it reads a missing feature.
The prompt:
For {store}, check on-site search and filter facets against {expected_facets}. Report which facets exist, which are missing, and which exist but are underpopulated (too many products lack the attribute for the filter to work). A filter that returns nothing is as bad as no filter. Prioritize facets by how many products they would make reachable.
What comes back:
→ Every facet buyers in that category would expect, checked one at a time
→ Which ones don't exist on the store at all
→ Which ones exist but barely work
→ The one facet fix that would make the most products reachable
4/ Prioritized remediation roadmap
Run the three audits above - you end up with three separate lists of problems. Turning that into something you can work through means deciding what comes first.
This prompt takes all three lists and puts them in an order that makes sense to work through, ranked by how much each fix moves the readiness number against how much effort it takes, and by what has to happen before what.
A taxonomy fix usually has to land before a smart collection can be built on top of it, for example - so sequence matters as much as priority does.
The prompt:
Turn {findings} into a remediation roadmap ranked by {capacity}. For each fix, estimate impact (how much readiness or revenue it recovers), effort (low/medium/high), and sequence (what must come first, e.g. taxonomy before smart collections). Group into Now, Next, Later so I can start today.
What comes back:
→ Every finding from the three audits above, folded into one ranked list
→ Impact and effort estimated for each fix
→ What has to happen first for each one, so the sequence makes sense
→ Everything grouped into now, next, and later, with the one thing to start
5/ Before/after readiness diff
The first time you run the scorecard, it's just a baseline. It's the second run, after the roadmap gets worked through, where the number starts meaning something, since now there's something to compare it against.
This prompt re-runs the scorecard and lines it up next to the baseline, dimension by dimension, so you can see exactly what moved and by how much.
It also says plainly if anything regressed while something else improved, since a fix in one area can easily shift something in another.
The prompt:
Re-run the agent-readiness scorecard for {store} and diff it against {baseline_scorecard}. Show the change per dimension and the overall score delta. Call out what improved most, what still lags, and whether any dimension regressed. Progress is the point, so make the movement obvious.
What comes back:
→ Every dimension, before and after, side by side
→ The overall score movement stated in one line
→ What improved the most
→ What's still lagging, so it doesn't quietly get dropped from the list
6/ Client-ready audit summary
Everything above is written for the person doing the fixing, somebody comfortable reading a dimension table who doesn't need it explained.
If you're running this for a client or handing it up to a founder who wants the outcome and not the mechanics, this last prompt repackages that audit for them.
It keeps the score and the roadmap unchanged, but changes the language it comes back in - out of dimension tables, into what the gaps are costing in revenue.
The prompt:
Package the audit of {store} into a summary a busy merchant reads in two minutes. Open with the readiness score and what it means in plain terms (what they are losing to unreadable products). Then the three biggest opportunities in revenue language, not jargon. Then the roadmap as Now/Next/Later. Mechanism, not marketing. No em dashes. End on the one line that distills the stakes.
What comes back:
→ The readiness score
→ The three biggest opportunities
→ The roadmap from above, still in three buckets
→ One line at the end that states what's at stake, nothing more
All 6 prompts are from one part of The Agentic Operator skill built for auditing the whole store rather than fixing one product at a time.
The other domains cover catalog and taxonomy, PDP content, scoring, segmentation, ads, retention, analytics, merchandising, and more.
Check the AI readiness of your store without Claude - no setup or login required.
- Ankit
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