Hire an AI Meta budget scaling reviewer

Everything it can hand back

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

• A return ladder for each platform, from what it reports down to new-customer return, with the traps found
• Your break-even return and the highest cost per sale you can pay
• A spend ladder for each ad set, with the step, how long to hold and when to stop
• A call on whether a separate scaling campaign pays off yet
• A ranked split of the next $10,000, with the stop point for each channel
• After the scale test, the delivery step that is failing and what to change first

How it does the job well

• Write for one named audience and one action. Each asset has a single job.
• Use the customer's own words from reviews, calls or surveys before inventing language.
• Every claim needs proof the business can show. Leave unproven claims out or mark them.
• Judge results against the business goal, such as profit or qualified leads, not clicks alone.

Mistakes it avoids

• Generic copy that could fit any brand.
• Comparing periods or campaigns that are not comparable, such as different dates, budgets or tracking.
• Calling a test winner before there is enough data.

What a good result looks like

An example, not your task: Your Meta ads are profitable and you want to spend more without losing the profit.

• Step 1: Platform ROAS reality check. Reported return set against your store totals, with the evidence, the limits and the traps found.
• Step 2: Meta scaling campaign plan. Your break-even return, a scale or hold call, and spend steps with hold times and stop rules.
• Step 3: Next-dollar budget split. A ranked split of the extra budget, a stop point for each channel and a retargeting check.
• Step 4: Meta delivery check. Your stall or cost symptoms, the delivery step that is failing and what to change first.
• HAND-BACK SUMMARY. Each step's file, any step not run and why, and every MISSING line in one list.

How the work is checked

• Each step passed its own checks, or the run stopped at the step that failed and says why.
• The steps ran in order, and each one used the result of the one before.
• Nothing was invented to fill a gap. Every gap is a MISSING: line.
• Nothing was sent, published or paid for without a clear yes.
• 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

• Find the real return. Sets your store's own base, then steps each platform's return down from reported to 7-day click to new-customer. It also checks the top ad alone and looks for spend on existing customers.
• Build the scaling ladder. Works out your break-even return, finds each ad set's ceiling and sets spend steps with stop rules. It says if a separate scaling campaign is not worth it yet.
• Split the next dollar. Ranks channels by new-customer revenue per extra dollar, checks that retargeting is not taking the budget, and finds each stop point.
• Fix delivery. Walks your symptoms through how Meta serves ads, checks the sums behind your cost per sale and names the step that is failing.

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 scale-meta-spend-while-it-still-pays 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/scale-meta-spend-while-it-still-pays/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

• Your Meta spend, platform return and 7-day click return for the dates you are checking
• Store totals for the same dates (new-customer revenue, new-customer count and total ad spend)
• Your average order value, product cost, shipping, fees and returns rate
• Your daily sales and the cash you can put into ads
• A breakdown of Meta spend by new, engaged and existing audiences (optional)

Tools and access

• Any good chat AI: Any chat AI can run the ladder and the score. Check the store base by hand.
• Meta Ads Manager: Lets you compare attribution settings and break results down by new, engaged and existing audiences.
• Shopify: Gives new-customer revenue and orders, and a cohort report for your own repeat data.
• A post-purchase survey app: Collects where buyers say they first heard of you, so you can compare it with platform data.
• Any good chat AI: It works from numbers you paste, so any current chat AI will do.
• Shopify: Its reports show new-customer sales and sales by product, which you need for the stop rules.
• Any good chat AI: The ranking is arithmetic and reasoning on numbers you paste, so any current chat AI can do it.
• Meta Ads Manager: Its audience breakdown shows your CPM and frequency on existing customers, and it runs lift studies.
• GA4: Gives a second view of new-customer revenue to set beside the platform reports.
• Any good chat AI: It reasons over numbers and descriptions you paste, so any current chat AI will do.
• Meta Ads Manager: Its breakdowns by ad, placement and audience segment hold the evidence.

Functions and deliverables

• Meta budget scaling reviewer: Share ad results, store totals and product costs. Get return checks, staged budget options, a split plan and later delivery findings.
  • A return ladder for each platform, from what it reports down to new-customer return, with the traps found
  • Your break-even return and the highest cost per sale you can pay
  • A spend ladder for each ad set, with the step, how long to hold and when to stop
  • A call on whether a separate scaling campaign pays off yet
  • A ranked split of the next $10,000, with the stop point for each channel
  • After the scale test, the delivery step that is failing and what to change first
• Ad return evidence checker: Share ad reports and store totals. Get matched comparisons, evidence gaps and a custom review score, without a causal sales claim.
  • A comparison of reported measures, definitions, periods and missing coverage
  • A supported top-ad breakdown with audience and attribution limits
  • The evidence basis and next check for each proposed budget decision
  • A bounded custom checklist score with missing checks shown
• Meta campaign growth planner: Plan how to scale winning Meta ads without losing money. Give your costs and results. You get a break-even ROAS, a spend ladder and stop rules.
  • Your break-even ROAS and the most you can pay for a customer
  • A spend ladder with stop rules
  • A scaling campaign design, or a clear "not yet", and an ABO or CBO choice
  • A monthly review checklist
• Marketing budget allocation planner: Share channel spend, new-customer sales and test results. Rank small budget changes by evidence, then set stop points and a fallback.
  • A ranking of where the next $10,000 goes, and what a $10,000 move between channels would do
  • A stop point for each channel
  • A test plan to measure the return on the next dollar
  • A rule for how much to put into brand content and how to judge it, if you want that shift
  • A fallback rule and a loss limit
• Meta delivery troubleshooter: Share at least seven days of ad data. Get a cautious diagnosis, one change to test and a comparison plan.
  • A diagnosis against the delivery steps, with the failing step named
  • What to change first and what to leave alone
  • A 7-day broad against interest comparison plan
• Brand profile builder: Share your website. Get a reusable profile covering your products, customers, voice and approved claims, with sources.
  • A folder of eight plain files, from a start file for your AI to a claims list and a change log
  • A tag on every fact, and every claim marked approved, pending or banned
  • A two-sentence diagnosis of your biggest gap, and a list of what's missing and who can answer it
  • A short slice for your next step, if you name one

Tasks you can use it for

• Your Meta ads are profitable and you want to spend more without losing the profit.
• Meta shows a strong return, but your bank balance does not match it.
• You raised the budget, spend stalled or cost per sale jumped, and you do not know why.

Limits to check before starting

• Your Meta account is new and has no closed-date results yet: use the Ad test brief writer to gather data first.
• Your campaigns are below break-even: fix the offer or ads first, then come back to scale.
• You only want to know why sales dropped: use the Ad sales drop analyst.

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.

• Download the handoff pack. It's a zip with HANDOFF.md and the 4 skill files in the order they run: Platform ROAS reality check, Meta scaling campaign planner, Next-dollar budget allocator and Meta delivery troubleshooter.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Paste your ad results and store totals for the same dates. Use dates that have closed.
• Write down the return you need to break even, and pick the campaign to scale.
• Scale in the steps of the ladder for one to two weeks. Export the results and bring them back for the delivery check.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/scale-meta-spend-while-it-still-pays 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.