Hire an AI price test results reviewer

Everything it can hand back

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

• A floor price for each product
• A keep, rerun or stop call on each finished test, judged on profit per visitor
• An answer to each rival move, inside your floor
• The next test: the price, three outcome cases, run length and how to undo it
• Which tests are still running and what could disturb them

How it does the job well

• Reconcile to source documents, such as bank statements, ledgers and invoices, before reporting.
• Show the calculation, inputs and period for every figure.
• Label actuals, estimates and forecasts clearly.
• Send tax, legal and regulatory questions to a qualified professional.

Mistakes it avoids

• Judging a price test on orders alone. A lower price can sell more and earn less.
• Matching a rival's cut by reflex. Hold, test or answer within the floor.
• Testing a price during a sale, a stock-out or an ad spike without saying so.
• Testing many products at once, so no result can be told apart.
• Using this for a planned sale or discount. Use the discount and price change checker for that.
• Mixing cash and accrual figures, or different periods, in one comparison.

What a good result looks like

An example, not your task: You set prices by feel and want to test them with a safe floor.

• Decisions this round. One call for each product: keep new price, keep old price, rerun, hold or test, with the profit sums behind it.
• Floors. The lowest viable price for each product. Every price it proposes stays at or above this.
• Next test. The price to try, three outcome cases, run length, the pass result and how to undo it.
• Rival moves. Each rival price change and the allowed response: hold, test or a match within your floor.
• Running now. Tests still running, days left and any event that could disturb them.
• Files and missing. Each step's file, the updated price-keeper/tests.md, and every MISSING line in one list.

How the work is checked

• Every proposed price is at or above the floor.
• Every test result is judged on profit per visitor, with the sums shown.
• No product has more than one test planned, and no running test was touched.
• No product was rerun more than once.
• 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.

A second opinion

Before it hands back, your AI reviews the draft the way John Caples would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Set the floor. Works out the lowest viable price for each product. Every price it proposes stays at or above it.
• Read a finished test. Judges the result on profit per visitor, not orders. A test that can't be read is rerun once, then stopped.
• Leave running tests alone. Reports days left and anything that could disturb the test, such as a sale or a rival move.
• Answer rival moves. If a rival changed its price, it holds, tests or matches within your floor, and never races to the bottom.
• Plan the next test. Plans one undoable test per product, and no more than two products at once, with three outcome cases.
• Hand back decisions. Lists every decision with the profit sums behind it. Nothing changes in your store until you say yes.

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 price-test-keeper 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/price-test-keeper/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 price, landed cost, shipping, fees and target margin for each product
• Orders, visitors and revenue by product for the last 4 to 8 weeks
• Orders and visitors for each price in any test that ended
• Rival prices now and last time (optional)

Helpful context (optional)

• Your sale calendar and stock-out dates
• A list of rival prices from two dates

Tools and access

• Claude Code: It can keep the price folder and test file, read last round's files and run every step on a schedule.
• Shopify: Connect it to read sales by product, or export orders and visitors for the test days.
• Any good chat AI: It only needs to solve one equation and compare the answer with the market.
• Google Sheets: Put the waterfall in a sheet so you can change CAC and see the price move.
• A spreadsheet: Holds the counts and gives the audience and reply sums.
• Your email tool or ad platform: Exports counts by version key.

Functions and deliverables

• Price test results reviewer: Set cost-based price floors and review tests by profit per visitor. Get a keep, rerun or hold recommendation.
  • A floor price for each product
  • A keep, rerun or stop call on each finished test, judged on profit per visitor
  • An answer to each rival move, inside your floor
  • The next test: the price, three outcome cases, run length and how to undo it
  • Which tests are still running and what could disturb them
• Minimum viable price calculator: Share costs, sales targets and acquisition spend. Calculate the lowest viable price or identify a product that cannot pay.
  • The lowest viable price
  • A bottom-to-top price table at low, mid and high CAC
  • A keep, reprice, reposition or cut call, with the reason
  • A list of checks it could not finish
• Price change sales checker: Get which products sell more or less as price changes, plus price points worth testing.
  • Price versus sales speed by product
  • Price-sensitive and price-stable lists
  • Price points worth testing
  • Group comparison summary
  Source: 40rty AI.
• Price test planner: Share costs, sales history and price options. Compare contribution, test demand cases and plan a reversible price test.
  • Recommended price to test
  • Base, low and high cases
  • Test design with guardrails
  • Rollback plan
  Source: Nexscope AI.
• A/B test results reader: Read your split-run test results: see how much better one version is, whether luck explains it, and get a keep, rerun or stop call.
  • A table of both versions and the gap as a percentage
  • A luck check with the sum shown
  • Sales per hundred replies and cost per reply and per sale
  • A keep, rerun or stop call with its reason
  • A record of what the test taught, and the next test to run
• Online store pricing planner: Use ecommerce costs and rival offers to draft product prices, price floors, and response rules.
  • Price per product
  • Price floors
  • Competitor response rules
  • Rollout and monitoring plan
  Source: Nexscope AI.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up
• John Caples: Write headlines and plan a fair ad test through John Caples’s recorded approach. Lead with the reader’s gain and count replies by version.
  • Your problem worked on the way John Caples would: their questions, their method, their push-back
  • The answer or the work, in John Caples's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You set prices by feel and want to test them with a safe floor.
• A price test just ended and you need to know whether to keep the new price.
• A rival cut its price and you want a calm answer, not a reflex.

Limits to check before starting

• You want to plan a sale or a discount: use the Sale discount and price change review.
• You get very few orders each month: wait until you have enough orders at each price, or test for longer.
• You want to change prices in your store now: change them yourself, because this agent never edits a live price.

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.

• Start a chat with the agent. In Claude Code, add it as a skill or save it in .claude/agents, then ask it to run this round.
• Attach your product costs, sales by product and results for any test that ended.
• Read the decisions. Change prices in your store yourself, or say yes to each change.
• Run it again in four weeks, or the day a test ends. It reads the saved test file.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/price-test-keeper 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.