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Interviews and field studies

Talk to people and watch them in their own setting.

For UX researcher · Knowledge pack

What are you preparing for?

One line is enough. Step 1 asks the rest in your AI app, and nothing is done until you approve the brief it writes.

In the case folder's materials/: Anything about the situation, with other people's names left out.

The person does the work live; the harness prepares and debriefs. Not found and out of scope are valid answers here: it says so instead of guessing.

How the field does this well: Give synthesis the study context: goals, research questions and prior findings, or trivial findings crowd out useful ones.
Keep participant words apart from interpretation, and tie each finding to participant, session and timestamp.
Code with a small set of described tags.
Critique only against a named checklist the agent can detect reliably, and mark each item observed or inferred.
Ground it first: Ask for, or look in materials/ for: the study goals and research questions, participant details, and the design principles or checklist to judge against. If there is none, say plainly that the result rests on general defaults, and name them.
What stays with the person: Interpretation, prioritization against the study goals, and design decisions. Prepare these; never decide them.
The check that makes it trustworthy: Each finding links to participant, session and quote, and critique is limited to a named checklist.
The person's own data: If the person has connected Figma, Miro, Notion, Lucid, read their data through that connector instead of asking for exports, and say which source each number or fact came from. Without one, work from materials/.

On your computer: Your files in materials/, every result in outputs/, the work itself. It runs in your AI app, on the plan you already pay for.
With Cheatcode: The steps, the gates and how far each case has come, so a case survives closing the app. See your cases.