Guest List Health Checker

What you get

• A readiness score out of 100, with a plain-language reason for every point it loses.
• The rows the import would lose, such as rows with no name and likely duplicates, listed first.
• The problems the import shrugs off, like bad emails and unknown RSVP values, listed separately.
• Which of your column headers it recognised, and which would be ignored.
• Fixes you can make on the page, then a cleaned CSV, a PDF report or a copy to paste.
• A starter template in CSV or Excel if you don't have a list yet.

What makes a guest list ready to import?

Most guest lists start as a messy spreadsheet. Names half-finished, three versions merged together, columns nobody agreed on. That is normal, and most of it does not matter. The problem is that you cannot tell by looking which bits matter and which do not, so people either spend an evening tidying things that were already fine, or import a file that quietly drops a dozen guests.

This checker runs rules copied from the MyGuestWork import and checked against its tests, on your own device, and sorts what it finds by what would actually happen.

What actually loses guests

• A row with no name. The import skips it. A guest with a table number and no name is not a guest it can create, so that row never arrives.
• Duplicates. Two rows that resolve to the same person get flagged, so somebody is counted twice or merged unpredictably.

Those are the two things worth fixing before you do anything else.

What looks alarming and is not

A typo in an email address does not stop the guest importing. The import drops the bad email, notes it, and creates the guest with everything else intact. An RSVP column reading "maybe" does not stop anything either; the status is left blank and the guest arrives. A note longer than the field allows is shortened, not rejected. All worth tidying, none of them worth panicking about, which is why this tool lists them separately instead of colouring your whole list red.

Names, emails and who counts as a duplicate

Duplicate matching uses email first and name second, and the order matters. Two rows sharing an email address are the same person even if one says "Sarah Chen" and the other says "Sara Chen", because that is almost always a corrected spelling. Two rows sharing a name but with different email addresses are two people, because that is a father and son, or two cousins named for the same grandmother. Only when there is no email to go on does it fall back to matching names alone.

Column headers it understands

Headers are matched loosely, so "First Name", "first_name" and "FirstName" all land in the same place. A single "Name" column is split on the first space. Table, Room, Booth and Section all read as seating. Dietary, Diet and Allergies all read as dietary needs. Phone, Mobile and Cell read as a phone number, which is what WhatsApp invitations go to on Premium. A column it does not recognise is ignored on import rather than guessed at, so the checker tells you which ones those are, and a column called "Meal" is worth renaming to "Dietary" before you lose it.

Is my file safe to check here?

Your file is never uploaded. It is read by your own browser using the file picker, parsed there, and analysed there. Nothing is sent to a server, nothing is stored, and nothing is kept when you close the tab. You can turn off your wifi after this page has loaded and the checker still works, which is the easiest way to prove it to yourself.

We also switch off session-replay analytics on every page in this tools section, because a replay would record the guest names on your screen. That would defeat the point of doing the work on your device in the first place.