Hire an AI store analytics report comparison reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Which tiles to trust and which to ignore, with the Shopify to GA4 gap
• This week against your four-week average, with the sums
• The main driver of any sharp drop, ranked
• The funnel step that leaks most and one or two things to check

How it does the job well

• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Reading one slow day as a trend. Use full weeks.
• Treating a tracking gap as lost sales.
• Reading a funnel on a store with very few orders a week. The rates move too much.
• Changing several things after one week's numbers. Say which can wait.
• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.

What a good result looks like

An example, not your task: You look at your numbers each week but the sources do not match.

• This week. Sales, orders and conversion against your four-week average, with the sums shown.
• Can you trust it. Which Shopify tiles to use and ignore, and the gap between Shopify and GA4 sessions.
• Path taken. Whether it traced a mismatch, looked into a drop, checked the funnel or found a quiet week, and why.
• What changed and why. The main drivers of any sharp drop, ranked, with the evidence for each.
• Where shoppers leave. The funnel step that leaks most, and the likely cause in plain words.
• Check next. One or two things to check, each with where to look and the number that triggered it.

How the work is checked

• The agreement check came before any drop was read.
• Every change is set against the four-week average, with the sums shown.
• A drop was not explained from numbers that disagree.
• Skipped checks are named, with the reason.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Read the recent weeks. Works out the four-week average for sales, orders and conversion from the saved weeks.
• Check the numbers first. Marks which Shopify tiles to trust and sets Shopify sessions against GA4.
• Read channels and pages. Works out conversion by channel and landing page and finds the worst funnel drop.
• Choose the path. If reports disagree by over a tenth, it traces the gap first. If numbers agree and something fell by a fifth, it investigates the drop. Then it checks the funnel and ad reports if the results call for it.
• Pick what to check next. Names one or two items, each tied to the number that triggered it.
• Save and hand back. Saves the week for next time and hands back the read, then stops.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id weekly-store-numbers-watcher only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/weekly-store-numbers-watcher/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Shopify analytics numbers for last week and the four weeks before
• GA4 exports by channel, landing page and funnel step for the same weeks
• What changed, such as a sale, a new theme or an edit to tracking (optional)
• Meta or Google conversion reports, if you want them checked against orders (optional)

Helpful context (optional)

• A calendar of sales, launches and price changes
• Ad platform conversion reports for the same weeks

Tools and access

• Claude Code: It can keep the numbers folder and run every Monday.
• Google Analytics 4: Export channels, landing pages and funnel steps from here.
• Shopify: Export the analytics numbers from the reports pages.
• Shopify Analytics: Source of every number the skill reads. Open the full cohort report, not the summary tile.
• Google Analytics 4: Gives the second session count used for the bot check.
• Google Analytics 4: Source of the traffic acquisition, landing page and funnel reports.
• A heatmap and recording tool (optional, often paid): Shows what shoppers do on the page the skill flags. You can read the report without it.
• Google Analytics 4: Has the purchase funnel, Path exploration, Tech overview and Landing pages reports this skill reads.
• Microsoft Clarity: Free heatmaps and recordings to answer the questions the odd spots raise.
• Shopify: Its order count is the check for your GA4 purchase events.
• Any good chat AI: It reads pasted tables and exports, so Claude, ChatGPT and Gemini all work.

Functions and deliverables

• Store analytics report comparison reviewer: Compare store and analytics totals before diagnosing a sales drop. Get data gaps, funnel questions and the next checks to run.
  • Which tiles to trust and which to ignore, with the Shopify to GA4 gap
  • This week against your four-week average, with the sums
  • The main driver of any sharp drop, ranked
  • The funnel step that leaks most and one or two things to check
• Shopify dashboard reader: Read your Shopify dashboard the safe way. Paste your analytics numbers to see which tiles to trust, your cash collected and a score out of 100.
  • Cash collected, with the sum shown
  • A verdict per tile: use, use with care, or ignore
  • Returns shown apart from the period they distort
  • A bot-traffic check and a funnel ratio, with a score out of 100 and how it was worked out
• Store page conversion analyst: Read your GA4 store reports to see which channels and product pages turn visits into purchases, where shoppers drop out, and how reliable the read is.
  • Channels ranked by purchase conversion and engaged share
  • Product pages above and below your store average, each with a first cause to test
  • The funnel step with the worst unexpected drop
  • Checks to run next, and a score out of 100 with its sum
• Metric mismatch tracer: Share two reports and their calculations. Compare the gap, trace where they differ and document a fix or unresolved cause.
  • The size of the gap
  • The step where the two diverge
  • The root cause by type
  • A corrected query or tolerance note
  • A completed reconciliation report
  Source: Nimrod Fisher.
• Metric drop investigator: Share historical and detailed data. Check the change, compare groups and rank supported drivers, hypotheses and next steps.
  • Confirmation the change is real
  • The date it started
  • Contributors ranked by impact
  • Accepted and rejected theories
  • A root cause report with next steps
  Source: Nimrod Fisher.
• Store funnel benchmark checker: Paste your GA4 funnel and device numbers. Get the step that leaks most, why, and what to test.
  • A table with each funnel step, your rate, the benchmark range and over, inside or under
  • The one step to fix first and the likely cause in plain words
  • Device gaps written as test ideas
  • A list of odd spots to look at in heatmaps and recordings, and a score out of 100 with how it was worked out
• Cross-platform conversion reconciler: See why Meta, Google and GA4 report different conversions. Get a side-by-side table, the reason for each gap and a fix plan.
  • A side-by-side table with each source's definition
  • The reason for each gap, marked confirmed or possible
  • A confidence rating and what would raise it
  • A list of evidence that is missing
  • A numbered plan to fix your measurement
  Source: Daniel Agrici.
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• You look at your numbers each week but the sources do not match.
• Sales or conversion dipped and you want to know why before changing anything.
• You want one weekly read of your funnel with the same checks every time.

Limits to check before starting

• Your store gets very few orders a week: rates swing too much, so look at a monthly total yourself instead.
• You want it to change tracking, ads or prices: it only names what to check, so make those changes yourself or ask a person you trust.
• You have no GA4 exports yet: set up GA4 first, as it can only check Shopify tiles without them.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the agent in Claude Code, or save it in .claude/agents.
• Put last week's Shopify numbers and GA4 exports in one folder, with the four weeks before on the first run.
• Ask it to read this week. Tell it what changed.
• Read what to check next and look at the page or report it names.
• Run it again next Monday. It compares with the saved weeks.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/weekly-store-numbers-watcher for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.