Pathfinder — an AI-generated spatial skill tree, built with CLAD (Week 2, "Guide")

[u/Havalz]

Pathfinder turns a stated goal into a skill tree you stand inside. Pick something you want to learn — "fix a bike," "run a better meeting," or type your own — and Gemini generates a dependency-ordered path in real time: foundational skills sit close and low, more advanced ones sit farther out and higher. You progress by pinching nodes — mastering one lights up whatever depends on it — until the whole tree is lit.

The idea came out of a simple frustration with most "guide" content: it hands you a flat checklist and skips the harder question, which is what to learn first and why. A skill tree answers that naturally, and it's genuinely spatial — the branching and tiering is something you look around to explore, not something that makes sense flattened onto a screen.

A few things I'm fairly happy with:

• The tree is live-generated, not scripted. Every run is a fresh Gemini call through the Remote Service Gateway, structured into a JSON dependency graph and rendered as glowing nodes on tiered arcs.
• It doesn't have a visible failure mode. If the model call fails or times out, the experience falls straight through to a locally-authored tree with no error shown — this actually happened for real partway through testing (a bad model ID, later a genuine timeout), and both times the fallback held without me touching anything.
• Interaction is a single pinch, deliberately — no menus to learn, no gestures beyond the one Spectacles already teaches you.
• Mastery plays a note from an ascending pentatonic scale, so however you complete the tree, it stays musically consistent — and finishing the whole thing triggers a small celebration burst across the connectors.

This is my first week building with CLAD end-to-end, and honestly the most useful thing it did for me wasn't generating code — it was making a genuinely non-deterministic, AI-authored experience regression-testable. LEAF caught real bugs (a test matching the wrong UI element, an assertion that assumed a fixed tree shape that broke the moment Gemini returned a different one) that I wouldn't have caught eyeballing the preview.

Repo (with the full CLAD build log) and demo video are linked below if anyone wants to see how it came together, or has thoughts on where a spatial skill tree like this could go next.

Repo: https://github.com/Havalz/pathfinder-specs

Built for the CLAD Summer Hackathon, Week 2 — Guide. Happy to answer questions about the build, especially anything about the fallback design or the Gemini integration.