Check a GrowthBook draft before it goes live

GrowthBook stores a draft's hypothesis, variations, and metrics in one place, so a lint can run against the real fields rather than a summary someone retyped.

How it works

  1. Connect GrowthBook's MCP server to your AI assistant or coding agent following the GrowthBook MCP documentation, using an API key with read access.
  2. Install the ExpLint MCP at https://explint.com/mcp to enable your agent to audit tests.
  3. Run the prompt below to audit these plans before the work starts.

How the check runs

  1. It reads the draft's name, hypothesis, description, variations, metrics, and traffic split, then builds one text that keeps your team's headings so the draft and the lint report line up field by field.
  2. It sends that text to ExpLint with explint__lint, which returns errors, warnings, and passes, each finding naming the rule behind it.
  3. You edit the draft in GrowthBook and ask for a recheck, and the assistant confirms which findings cleared and which remain.

Copy and paste this prompt

Change the words in square brackets to name your own project, board, or test.

Prompt for your assistant
Use GrowthBook MCP tools to read draft experiment [experiment name]. Build one text from its hypothesis, description, variations, primary and guardrail metrics, traffic split, and run length. Lint it with explint.com against project [your ExpLint project name]. Show each error and warning with its GrowthBook field. Do not change GrowthBook yet. Suggest edits here, and on my update, read and lint the draft again.

What ExpLint looks for

ExpLint ships over 24 rules, and a project switches each one on or off or rewrites it in plain English. These are the rules the plans this guide reads break most often. An error means the test cannot answer its question as written, and a warning means the result will be hard to judge when it comes in.

RuleLevelIn plain words
hypothesis/names-metric-and-directionerrorThe hypothesis field has to name the metric and the direction it expects, since GrowthBook records metrics separately and the prose is where the expectation lives.
variants/control-is-describederrorGrowthBook names a baseline variation, and the draft still has to say what that baseline shows people today for a new reader to picture it.
power/has-a-sample-size-calculationerrorA draft that sets a 50/50 split with no headcount leaves the team unable to say when the test has seen enough traffic.
duration/covers-full-business-cycleserrorThe run setting should cover full weeks, because a Monday to Friday window reads the same traffic every day of its life.

Read more

Portrait of Kameron Tanseli

Kameron Tanseli

Founder, ExpLint

Kameron Tanseli is the founder of ExpLint and a growth engineer who has run more than 1,500 experiments across a nine-year career. As the founding growth hire at Fyxer he launched 540 experiments in a year while the company grew from $1M to $35M in annual revenue, and he now runs growth engineering at FLORA, where a one-person team ships six to eight experiments a week.

Don’t let a bad test make it to production

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