Claude Skills for Ecommerce: What They Are, How They Run, and Where to Start

Ready-to-run Claude skills for ecommerce teams, each one tested on real store data and sorted by the team that owns it and the outcome it moves: revenue, cash, time or risk. Every skill ends in a decision, not a paragraph.

ads-performance

Growth

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Ads Performance

Cut, scale or refresh per campaign on Polar numbers.

ltv-by-channel

Shows which channels bring customers worth paying for.

Revenue, Cash

cro-and-landing-pages

Storefront

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CRO & Landing Pages

Finds the campaigns sending spend to the wrong page.

threshold-alerts

Stays silent until a KPI crosses your line.

pacing-vs-targets

Where the month lands versus target, and the gap by channel.

multi-store-analysis

Puts every store side by side on one set of definitions, with totals that reconcile.

Time, Revenue

What is a Claude skill?

A Claude skill is a pre-built agent that handles one specific ecommerce decision, like a bid change, a PDP rewrite or a reorder draft. It reads your governed store data through Polar’s MCP server, proposes an action and waits for you to publish it.

A skill file with its metric definitions, guardrails and output format, loaded before the run

A pre-built context file Claude reads before every task

Instead of re-explaining your business in every chat, the skill packages the instructions, metric definitions and output format once. Claude loads it, runs the task the same way every time, and returns a consistent artifact.

The same question asked twice: a prompt returns two ROAS figures, the skill returns the same one

Skills vs prompts: what’s the difference

A prompt is a one-off request that depends on how you phrase it. A skill is versioned and repeatable: same workflow, same guardrails, same metric definitions, whoever runs it and whenever they run it.

Shopify, Amazon, TikTok Shop, ads and Klaviyo flow into Polar, which Claude queries through MCP

How skills connect to your live store data via MCP

Polar unifies Shopify, Amazon, TikTok Shop, ads and email into one governed model, then exposes it to Claude via MCP. That’s 45+ connectors, no CSV exports and no SQL. Claude queries Polar, never your raw store admin.

Why Claude Alone Isn't Enough

Point an LLM at an ungoverned warehouse and it answers confidently, plausibly, and differently every time you ask.

Raw Shopify data is ungoverned by default

Same query, three different SQL interpretations. Without structured modeling, queries shift and return drifting numbers.

The generated SQL query changes between runs, dropping the refunds join

Five definitions of ROAS, zero automatic fix

Your financial lead and your ad strategist don’t mean the same thing by ‘gross sales.’ Without preset definitions, numbers clash.

Two colleagues ask the same question in Slack and get two different gross sales figures

Why a semantic layer matters

Align metrics once. Under a governed model, Claude operates with fixed parameters. Net Revenue is locked. Blended CAC stays clean.

A locked library of metric definitions feeding a single Claude answer

How Polar skills are different

A stale CSV export crossed out next to live sources synced minutes ago

Every skill runs on governed data, not a CSV

Other skill directories hand Claude a spreadsheet and hope. Polar skills query your live, unified data model through MCP: Shopify, Amazon, TikTok Shop, ads and email. Always current, never exported.

One locked Blended ROAS definition returning the same value in three different skills

One definition per metric across all skills

ROAS means the same thing in the media buying skill, the reporting skill and the attribution skill. One warehouse, one metric layer, so two skills never give your team two different numbers.

A draft of proposed campaign changes waiting for the team to edit or publish

Claude proposes. Your team publishes. Always.

No skill writes to your store, your ads or your inbox on its own. Every output is a draft with a full audit trail. You review, edit if needed, and publish. No black box.

How to run your first skill

Connect your stack to Polar

Shopify, Amazon, TikTok Shop, Meta, Google, Klaviyo: 45+ connectors, no SQL and no configuration. Polar unifies everything into one governed data model, which is what every skill reads from.

Searching for Polar in the Claude connector directory, ready to connect

Pick a skill for your team

Browse by department or by outcome, open the skill and load it into Claude. The workflow, the metric definitions and the guardrails are already packaged inside it, so there is nothing to brief.

Two net revenue figures that disagree, traced back to two competing definitions

Run it, then publish

Claude reads your live data through MCP and proposes the action: a bid table, a PDP rewrite, a PO draft. Nothing is written to your store or your ad accounts until you review it and publish. That is the loop.

A recurring prompt turned into a scheduled Polar automation delivered to Slack

Frequently asked questions

What is a Claude skill for ecommerce?

A pre-built context file that turns Claude into a specialist for one ecommerce job: media buying, reporting, inventory. It packages the workflows, metric definitions and guardrails, and connects to your live store data through Polar’s MCP server. You run it, Claude proposes, you publish.

Do I need to code to use these skills?

No. A skill is a file of instructions, not a script. You load it into Claude, connect Polar with an OAuth login, and ask in plain English. The only thing you need to be able to do is describe the decision you want made.

Can I customize a skill for my brand?

Yes, and most teams do. The thresholds, the guardrails and the output format are all editable text. Change the ROAS floor, add a rule about which SKUs are never discounted, or ask for the output as a Slack message instead of a table. Your version stays yours.

Do skills work without Polar?

They run, but they answer on whatever data you hand them. Without a governed model the same skill can return three different numbers for the same question, because nothing fixes what net revenue or blended ROAS means. Polar is what makes the answer repeatable.

How is this different from a ChatGPT plugin?

A plugin gives the model an action to call. A skill gives it the business context to decide with: your metric definitions, your thresholds, your approval rules, and the order in which to work. One extends what the model can touch, the other extends what it understands about your store.

Are new skills added regularly?

Yes. The directory grows as teams publish the workflows they run every week, and existing skills are versioned when a definition or a guardrail changes.