Best AI skills for ecommerce teams
The best AI skills for ecommerce teams: daily revenue checks, promo margin, stockout risk, feed audits, reviews and LTV, run with Claude or Codex in Type.
By Type Team
What are the best AI skills for ecommerce teams?
The best AI skills for ecommerce teams are Daily Revenue Check, Promo Margin Guard, Inventory Stockout Risk and Product Feed Auditor. They cover the daily revenue number, promo margin, stockouts and Google Shopping feed errors, the recurring checks that cost a Shopify brand money when they slip.
An AI skill is a packaged set of instructions, scripts and sample data that Claude or Codex follows the same way every time a task comes up. Instead of re-explaining how your store counts net sales or which SKUs can carry a 30% discount, you install the skill once and run it on your own data.
Ecommerce teams benefit more than most because so much of the work is the same check against new data: yesterday's net sales across Shopify, Amazon and wholesale, last week's discount codes, this week's reorder list. The rules are specific to your store, and a mistake shows up as lost margin, a sold-out best seller, or a daily number leadership stops trusting.
To see how ecommerce teams run this work together, with Claude or Codex and their tools connected once, visit Type's ecommerce page. Every skill in this guide is published with its full files in the Type Skills Library.
| Skill | Best for | What you get |
|---|---|---|
| Daily Revenue Check | Finance and data leads, every morning before the revenue number goes to Slack | A PUBLISH or HOLD verdict, a bridge from each export to net sales, and a draft Slack message |
| Promo Margin Guard | Founders, finance and growth leads after every promo and before the next sitewide sale | A promo P&L by discount code, the codes that lost money, breakeven depth per SKU, and an exclusion list |
| Inventory Stockout Risk | Ops and purchasing, in the weekly or fortnightly review before cutting POs | Stockouts ranked by revenue at risk, reorder quantities by supplier, and capital tied up in overstock |
| Product Feed Auditor | Ecommerce and marketing ops, weekly and before any bulk catalog, price or sale change | Findings by severity with the exact item ids, the disapproval risk each carries, and attribute coverage |
| Landing Page Conversion Analyzer | Growth leads asking why CVR dropped, or wanting a recurring landing page report | Which page and source moved, whether traffic mix explains it, and which funnel step declined most |
| Review Insight Miner | CX, product and marketing, when a hero SKU's rating slips or before a listing rewrite | Themes ranked by star-rating drag, split into defects and expectation mismatches, each with an owned fix |
| Cohort LTV Analyzer | Finance, data and growth leads when CAC rises or before raising acquisition budget | Repeat rate and contribution-margin LTV per cohort by month, CAC payback, and cohorts trailing a reference |
| Ad Account Audit | Growth leads, monthly or whenever paid performance drops without an obvious cause | Severity-ranked findings with evidence and a fix, plus spend at issue in five buckets that are never summed |
| Failed Payment Recovery | Finance or ops at subscription brands, run hourly or from a webhook when payments fail | Classified failed charges, an approval-gated retry and outreach ladder, and every touch logged on the invoice |
| GEO / AI Search Audit | Growth and SEO leads, as a technical first pass when the store is missing from AI answers | A draft report with a scored, prioritized fix list, a draft llms.txt and draft Organization JSON-LD |
Which AI skills should ecommerce teams use?
These ten skills cover the daily number, promo margin, inventory, the product feed, landing page conversion, reviews, cohorts, paid media, subscription payments and AI search visibility.
The list runs roughly in the order we suggest starting. First come the daily number and the margin and inventory checks that protect cash, then the feed, landing page and review work that fixes what is costing conversions, then periodic reviews of cohorts, paid media, failed payments and AI search.
1. Daily Revenue Check
Daily Revenue Check combines Shopify, Amazon and wholesale invoices into one net-sales figure using finance's revenue definitions, runs seven checks, and returns PUBLISH or HOLD before anyone posts the number.
A daily number that is wrong once stops being trusted. This removes tagged Shopify copies of Amazon MCF and wholesale orders, notes late refunds as a caveat, and returns HOLD with a fix when a feed is stale or a channel does not reconcile to its source report. It never posts anything or picks a revenue definition on finance's behalf.
- Best for
- Finance and data leads, every morning before the revenue number goes to Slack
- Works from
- Shopify, Amazon and wholesale invoice exports, each channel's source totals, and finance's revenue definitions
- You get
- A PUBLISH or HOLD verdict, a bridge from each export to net sales, and a draft Slack message
2. Promo Margin Guard
Promo Margin Guard prices a discount or promotion after COGS, shipping, payment fees, refunds and expected returns, names the codes that lost money, and solves the deepest discount each product can carry.
Promo revenue shows up right away, while the returns arrive weeks later. This separates booked margin from expected margin and turns the result into an instruction for the next sale: these SKUs out, these capped, the rest fine at the planned depth. It measures contribution margin only (no ad spend, overheads or incrementality), so pair it with cohort LTV before cutting a welcome code that buys repeat customers.
- Best for
- Founders, finance and growth leads after every promo and before the next sitewide sale
- Works from
- Shopify promotion orders and line items, plus a product cost file with landed cost and return rate per SKU
- You get
- A promo P&L by discount code, the codes that lost money, breakeven depth per SKU, and an exclusion list
3. Inventory Stockout Risk
Inventory Stockout Risk computes days of cover per SKU from sales velocity, ranks upcoming stockouts by the revenue they put at risk, and recommends reorder quantities against supplier lead times, MOQ and case pack.
A sold-out hero SKU stops earning while back-in-stock requests pile up. The report separates stockouts you can still prevent from ones the lead time has locked in, where the call is air freight, a partial PO or pulling paid spend. Its order quantities are a starting point for a buyer (velocity is history, not a forecast), and it shows the capital frozen in overstock that could fund the reorder.
- Best for
- Ops and purchasing, in the weekly or fortnightly review before cutting POs
- Works from
- Sales history, an on-hand snapshot from Shopify, a 3PL or ERP, and supplier lead times, MOQs and case packs
- You get
- Stockouts ranked by revenue at risk, reorder quantities by supplier, and capital tied up in overstock
4. Product Feed Auditor
Product Feed Auditor checks a Google Merchant Center or Shopping feed export offline for missing attributes, title and description policy problems, malformed image and landing-page URLs, price and sale-price conflicts, and duplicate ids.
Merchant Center flags a disapproval after the item has already stopped serving. This runs the same class of checks before the next feed fetch, groups problems by cause (twelve placeholder images is one broken asset sync), and points each fix at the system that owns the column. It reports only and never edits the feed.
- Best for
- Ecommerce and marketing ops, weekly and before any bulk catalog, price or sale change
- Works from
- A Google Merchant Center, Shopify Google & YouTube channel, or feed vendor export as CSV or TSV
- You get
- Findings by severity with the exact item ids, the disapproval risk each carries, and attribute coverage
5. Landing Page Conversion Analyzer
Landing Page Conversion Analyzer tracks conversion rate by landing page and traffic source against the store's own history, separates real changes from traffic-mix shifts, and alerts only when something genuinely moved.
When CVR drops, the question is whether a page broke or the traffic changed, and a blended rate cannot tell you. This compares each page and source with its own trailing history (no industry benchmarks), names the funnel step with the steepest decline, and flags conversion steps that collapsed while traffic held (possible broken tracking). It is read-only, the first run only builds a baseline, and causes come back labeled as hypotheses.
- Best for
- Growth leads asking why CVR dropped, or wanting a recurring landing page report
- Works from
- A daily CSV or Parquet session export by landing page, UTM source and funnel step, from GA4 or a warehouse
- You get
- Which page and source moved, whether traffic mix explains it, and which funnel step declined most
6. Review Insight Miner
Review Insight Miner clusters product reviews and support tickets into recurring themes, separates product defects from expectation mismatches, ranks each theme by volume and the star rating it drags down, and attaches a specific fix.
This splits negative feedback into the product failing and the page over-promising, which have different owners: copy, specs and photos can often be fixed this week, while defects need a supplier conversation. Praise themes come back too, as language to reuse in marketing.
- Best for
- CX, product and marketing, when a hero SKU's rating slips or before a listing rewrite
- Works from
- A CSV of reviews and first support messages from tools like Judge.me, Okendo, Yotpo, Gorgias or Zendesk
- You get
- Themes ranked by star-rating drag, split into defects and expectation mismatches, each with an owned fix
7. Cohort LTV Analyzer
Cohort LTV Analyzer groups customers by the month of their first order and reports repeat rate, cumulative revenue and contribution-margin LTV at equal ages, CAC payback per cohort, and which cohorts trail a reference cohort.
A blended LTV hides whether this quarter's customers are worth what January's were at the same age. The triangles show whether a weak cohort is a mix problem (smaller or more discounted first orders) or a retention problem (customers not coming back), which changes the fix. It reports observed history only (no projected LTV) and blends CAC per month, which understates paid CAC when organic acquisition is heavy.
- Best for
- Finance, data and growth leads when CAC rises or before raising acquisition budget
- Works from
- An order export from Shopify, a warehouse or an ERP, monthly marketing spend, and finance's definitions if any
- You get
- Repeat rate and contribution-margin LTV per cohort by month, CAC payback, and cohorts trailing a reference
8. Ad Account Audit
Ad Account Audit runs six hygiene checks on a paid search or paid social export, covering conversion tracking gaps, budget pacing, disapproved ads, naming drift, negative keywords and stale creative, with dollars attached to each finding.
When paid performance falls, the first question is whether the account is broken or the market moved. This checks the account side from exports alone (it never calls an ad platform) and keeps wasted spend you can stop separate from spend it cannot measure. On paid social accounts like Meta, the search-term and negative-keyword checks are skipped and noted.
- Best for
- Growth leads, monthly or whenever paid performance drops without an obvious cause
- Works from
- Four CSV exports for one account and date range: campaigns, ads, keywords and search terms
- You get
- Severity-ranked findings with evidence and a fix, plus spend at issue in five buckets that are never summed
9. Failed Payment Recovery
Failed Payment Recovery finds failed subscription charges and past-due invoices, classifies each decline, and runs a retry and outreach ladder with approval gates, recording every touch so no customer is contacted twice.
Declined cards on a subscription program are churn nobody chose. Unlike the others here, this skill acts: it retries charges, sends outreach and writes recovery state to invoices. Until you confirm write access it only reports, and by default it holds every send and retry until its first draft is approved.
- Best for
- Finance or ops at subscription brands, run hourly or from a webhook when payments fail
- Works from
- A Stripe, Recharge or other billing connection with read and write access, plus an email or dunning send path
- You get
- Classified failed charges, an approval-gated retry and outreach ladder, and every touch logged on the invoice
10. GEO / AI Search Audit
GEO / AI Search Audit checks whether AI answer engines can reach, read and quote your site, scoring AI crawler access in robots.txt, passage citability, schema.org structured data, llms.txt and on-page rendering.
When a shopper asks an AI assistant what to buy, a store that blocks AI search crawlers or serves product pages as an empty JavaScript shell is hard for the answer engine to quote. This finds those blockers from robots.txt and raw HTML, checks structured data such as Product schema, and ranks fixes by priority and effort. It only reads public pages, and its scores measure readiness, not actual citations.
- Best for
- Growth and SEO leads, as a technical first pass when the store is missing from AI answers
- Works from
- The store's URL, fetched live or from saved pages; a competitor benchmark needs a client profile
- You get
- A draft report with a scored, prioritized fix list, a draft llms.txt and draft Organization JSON-LD
How should ecommerce teams choose which AI skills to start with?
Start with the recurring check that has a clear right answer and a visible cost when it is wrong, usually the daily revenue number, promo margin or the reorder list, then add more as each one earns trust.
The skills that pay off fastest have a definite answer. Daily Revenue Check either reconciles to each channel's source report or it does not. Promo Margin Guard solves a breakeven discount per SKU from your own costs and return rates.
Check what each skill needs before you pick it. Most work from exports shaped to a published data contract: order and line-item files, an inventory snapshot, a feed file, ad account CSVs. Daily Revenue Check will not run without finance's revenue definitions file. Failed Payment Recovery works against a live billing connection and needs write access to retry charges.
Most of these skills ship sample data and an expected output, and their instructions say to run the sample first and stop if it does not match. Then run the skill on a recent period you already know the answer to. If it agrees with what your team found by hand, schedule it; if not, the gap is often a definition worth settling.
- Start with work that repeats every week and has a clear right answer.
- Give each skill one owner who reviews its output and improves it.
- Run the bundled sample first, then a period you already know the answer to.
- Check the inputs: most use exports, and a few need finance's definitions or write access.
- Add judgment-heavy skills once the numbers underneath them are trusted.
How do ecommerce teams run these skills together in Type?
In Type, an ecommerce team connects its tools once, installs skills from the library into shared Spaces, and runs them in Slack or the Type app with Claude or Codex, on demand or on a schedule.
A team might set up a Space per function: finance, growth, purchasing, CX. Tools are connected once and assigned to the Spaces that need them, with team and individual permissions. For example, the billing connection Failed Payment Recovery uses could be assigned only to the finance Space.
Install a skill from the library, run it in Slack or the Type desktop and mobile apps, and schedule the recurring ones as automations. For example, a team might schedule Inventory Stockout Risk before the weekly PO review and Product Feed Auditor every week, and run Daily Revenue Check each morning once someone has entered each channel's source total. Each person can work with Claude or Codex on the subscriptions the team already pays for.
When one person improves a skill, like raising the stockout report's service level for hero SKUs, the whole team gets the improvement. See how that works for store operations on Type's ecommerce page.
Frequently asked questions
What is an AI skill for ecommerce?
An AI skill is a reusable package of instructions, often with scripts and sample data, that Claude or Codex follows the same way every time. For an ecommerce team, that means checks like reconciling daily net sales or pricing a promotion's true margin follow your store's rules instead of being re-explained in a new chat.
How is an AI skill different from a prompt?
A prompt is interpreted fresh each time, so results drift from person to person. A skill packages the instructions, often with scripts that do the math, a data contract for the inputs and sample data with an expected output, so the same export produces the same answer. When someone improves a skill, everyone who runs it gets the improved version.
Do these skills work with Claude and Codex?
Yes. In Type, each person can run skills with Claude or Codex using the subscriptions the team already pays for. The skill packages are plain SKILL.md folders, so they also work in other agent environments that read SKILL.md, such as Claude Code and Codex.
Do these skills change anything in our Shopify store or ad accounts?
Nine of the ten are read-only: the analysis skills work from exported files and never write back to Shopify, Amazon, Merchant Center or ad platforms, and GEO / AI Search Audit only reads public pages. The exception is Failed Payment Recovery, which can retry charges and send outreach. It treats the billing connection as read-only until someone confirms write access, and by default nothing reaches a customer or their card until its first draft is approved.
Can we customize a skill for our store?
Yes. Each skill exposes settings for what differs between brands, such as fulfillment cost and planned discount depth in Promo Margin Guard, service level in Inventory Stockout Risk, or the cohort age used for comparison in Cohort LTV Analyzer. If you run several stores or brands, keep a separate set of exports and definitions for each.
Where can I find more AI skills for ecommerce?
The Type Skills Library at type.com/library/skills publishes every skill with its full files, including the SKILL.md, scripts and sample data. You can read what each ecommerce and DTC skill does and what it needs before installing it into Type.