Why is grade conversion the hard part of a climbing app?

Because the systems are not linearly comparable and climbers disagree about the mapping. Store every climb with both its raw grade and the system it was entered in, then convert only for display. If you normalise on write, you permanently lose what the user actually typed and every future conversion change rewrites their history.

Does a climbing log need to work offline?

Yes, more than most apps. Outdoor crags routinely have no signal and gym basements are not much better, so logging has to work fully offline and sync later. Build it local-first from the start — retrofitting offline support onto a network-first app is one of the more painful rewrites.

How to Build a Climbing Log App with AI (2026)

What a climbing log app needs

Log boulder and route attempts with grade and style
Track projects across multiple sessions until sent
Grade pyramid and send rate over time
Separate gym and outdoor logging
Session notes for beta and conditions

The screens

Screen	What it does
Sessions	Recent sessions with duration, location, and climbs logged
Log Climb	Grade picker, style (flash, redpoint, attempt), angle, notes
Projects	Unsent climbs with attempt counts and last-tried date
Progress	Grade pyramid and send rate trend
Settings	Grade system (V-scale, Font, YDS, French), home gym

The data model

Session: date, location, type (gym | outdoor), durationMins, notes
Climb: sessionId, name, grade, gradeSystem, discipline (boulder | sport | trad), style (flash | onsight | redpoint | attempt), angle
Project: name, grade, location, firstTriedAt, sentAt, attemptCount
User: preferredGradeSystem, homeGym

The build prompt (copy this)

Paste this into ShipNative and it becomes a working React Native app with these screens, navigable in a live preview, in about two minutes:

“A climbing log app. Users start a session at a gym or crag, then log each climb with a grade, discipline (boulder, sport, or trad), and style — flash, onsight, redpoint, or attempt. Unsent climbs automatically become projects that accumulate attempts across sessions, and sending one moves it to the log with the total attempt count preserved. The progress screen shows a grade pyramid (how many climbs sent at each grade) and a send rate trend over the last few months. Grades display in the user’s preferred system — V-scale, Font, YDS, or French — with conversion handled in one place so switching systems re-renders history rather than corrupting it. Logging must be fast enough to do between burns: grade picker first, everything else optional. Works offline, because crags have no signal. Screens: Sessions, Log Climb, Projects, Progress, Settings.”

Then iterate with small, screen-specific prompts (“make the primary button bigger”, “add an empty state”), preview on your own phone, and export the full Expo project whenever you want. The full method: how to build an app with AI.

How it makes money

Subscription ($3-4/mo) for the progress analytics and unlimited history, with the basic log free forever. Be honest about the ceiling: this is a small, loyal audience, and the strongest versions are niche — a bouldering-only app, or one built around a single gym chain. Sponsorship or a gym partnership tends to beat consumer subscriptions here.

Implementation and the revenue math for every model: how to make money from an app. When you’re ready to ship: App Store and Google Play publishing guides.

Build it now

The prompt above is free to run at shipnative.dev — working climbing log app on your phone today, full code export, no credit card.

How to Build a Climbing Log App with AI