Mot

Mot is a career assistant that answers with real occupational data instead of guesswork. It's built on O*NET 31.0, the U.S. Department of Labor's database of about 1,000 occupations, and it has three tools: a Career Explorer for chatting about careers, bookmarking and comparing occupations; a Mock Interview Coach that grades your practice answers against O*NET's own examples of each skill level; and an Interest Quiz based on the RIASEC model, matched against every occupation. The mascot — "a cross between Meta's Muse and OpenAI's Dots, made casual so it feels like a friend" — even got blinking-eyes animation after several rounds of prompts.

What elevates Mot beyond a chatbot demo is the half you don't see. Everything except the Astro front end and the Node agent service lives in Sanity: 1,500+ O*NET documents, coaching guides, quiz submissions, two review workflows, a knowledge base, MCP endpoints, and a weekly function that classifies chat transcripts. Career counselors write interview coaching guides in Sanity Studio, and a guide only reaches the AI coach after another counselor reviews it through a gated workflow. User-submitted career quizzes go through Draft → Ready to Review → In Review → Approved, with "Approve and publish" as a single custom action.

The author's build log is one of the most honest vibe-coding write-ups around: 47 agent chats and ~80 commits across four repos from September 19 to October 2, "I wrote very little code by hand." He documents what worked (an `AGENTS.md` in every repo listing cross-repo contracts), what didn't (vague taste prompts took four rounds; prompts naming the exact DOM element landed first try), and the bug the model couldn't see (a CORS failure in a workflow plugin's ACL request, found by opening DevTools himself). "When the model starts generating theories instead of evidence, give it evidence" — that's a line worth quoting.

Note: This project is curated by the VibeFix editors from public sources. All rights belong to the original creator.