This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange
What I Built
As someone who is contributing for a good amount of time I noticed a problem as in bigger and legacy codebases a lot of issues are opened daily and honestly maintainers dont have time to give their full attention to every issue.
So I build Mento its an AI powered maintainer is copilot that fetches your GitHub issues generates plain English summaries with urgency scores and runs an intelligent triage pipeline that learns from past decisions to suggest P0âP4 priorities.
The more issues you triage the smarter it gets. Currently its in early development so you dont have to login via Github just bring your LLM key from any router (curently we Provide over 10+ router options )
Demo
Live demo: https://mainto-five.vercel.app/
Code
GitHub repository: https://github.com/AarishMansur/Mainto
My Build Process
I build Mento with OpenCode and Nextjs so I didnt use any wireframes or no planning docs I use Sanity Docs and couple of Prompts which worked for me
I use santity as the agents memory so that every fetched issue, AI summary, triage decision, and learned pattern gets stored as a structured document and the agent reads from them before making it next decision and not forgetting sanity Live colloboration for real time updates and the Presentation Tool to map Studio documents directly to app routes
The more you triage the better it gets
These are some of the prompts I used:
Prompt 1: Product Direction
Build a maintainer focused web app that connects to GitHub repositories and helps open source maintainers understand which issues need attention first. Include repository search, issue cards, AI summaries, urgency scores, suggested actions, and a clean dashboard experience.
Prompt 2: Issue Summaries
Create an AI summarization flow for GitHub issues. Return a structured result containing a concise summary, an urgency score from 1 to 10, three to five key points, and two to three suggested actions. The output should be easy for a maintainer to scan quickly.
Prompt 3: Sanity Data Modeling
Model the Mainto workflow in Sanity. Create document types for GitHub issues, generated issue summaries, historical triage patterns, and maintainer triage decisions. Use references between related documents and include fields for urgency, priority, reasoning, suggested actions, and resolution history.
Prompt 4: Triage Pipeline
Add a workflow pipeline with New, Summarized, Prioritized, In Review, and Resolved stages. Let the AI prioritize issues from P0 to P4 using historical patterns, matching keywords, labels, and previous maintainer decisions.
How I designed my Sanity Schema
GitHub Issue
import { defineField, defineType } from 'sanity'
export const issueType = defineType({
name: 'issue',
title: 'GitHub Issue',
type: 'document',
fields: [
defineField({ name: 'githubId', type: 'number', validation: (Rule) => Rule.required() }),
defineField({ name: 'repoOwner', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'repoName', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'title', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'body', type: 'text' }),
defineField({ name: 'state', type: 'string', options: { list: [{ title: 'Open', value: 'open' }, { title: 'Closed', value: 'closed' }] } }),
defineField({ name: 'labels', type: 'array', of: [{ type: 'string' }] }),
defineField({ name: 'commentsCount', type: 'number' }),
defineField({ name: 'workflowStatus', type: 'string', initialValue: 'new', options: { list: [
{ title: 'New', value: 'new' },
{ title: 'Summarized', value: 'summarized' },
{ title: 'Prioritized', value: 'prioritized' },
{ title: 'In Review', value: 'in_review' },
{ title: 'Resolved', value: 'resolved' },
]}}),
defineField({ name: 'agentPriority', type: 'string', options: { list: [
{ title: 'P0 - Critical', value: 'P0' },
{ title: 'P1 - High', value: 'P1' },
{ title: 'P2 - Medium', value: 'P2' },
{ title: 'P3 - Low', value: 'P3' },
{ title: 'P4 - Backlog', value: 'P4' },
]}}),
defineField({ name: 'agentReasoning', type: 'text' }),
defineField({ name: 'matchedPatternIds', type: 'array', of: [{ type: 'reference', to: [{ type: 'triagePattern' }] }] }),
defineField({ name: 'maintainerDecision', type: 'string', options: { list: [
{ title: 'Accepted', value: 'accepted' },
{ title: 'Overridden Higher', value: 'overridden_higher' },
{ title: 'Overridden Lower', value: 'overridden_lower' },
{ title: 'Pending', value: 'pending' },
]}}),
],
})
issueSummary â AI Generated Summary
export const issueSummaryType = defineType({
name: 'issueSummary',
title: 'Issue Summary',
type: 'document',
fields: [
defineField({ name: 'githubId', type: 'number', validation: (Rule) => Rule.required() }),
defineField({ name: 'summary', type: 'text', validation: (Rule) => Rule.required() }),
defineField({ name: 'urgencyScore', type: 'number', validation: (Rule) => Rule.min(1).max(10) }),
defineField({ name: 'keyPoints', type: 'array', of: [{ type: 'string' }] }),
defineField({ name: 'suggestedActions', type: 'array', of: [{ type: 'string' }] }),
defineField({ name: 'generatedAt', type: 'datetime' }),
],
})
triagePattern â Agent Memory
export const triagePatternType = defineType({
name: 'triagePattern',
title: 'Triage Pattern',
type: 'document',
fields: [
defineField({ name: 'name', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'keywords', type: 'array', of: [{ type: 'string' }] }),
defineField({ name: 'labels', type: 'array', of: [{ type: 'string' }] }),
defineField({ name: 'avgUrgency', type: 'number' }),
defineField({ name: 'typicalPriority', type: 'string' }),
defineField({ name: 'typicalResolution', type: 'string' }),
defineField({ name: 'patternCount', type: 'number', initialValue: 0 }),
],
})
triageDecision â Maintainer Feedback Loop
export const triageDecisionType = defineType({
name: 'triageDecision',
title: 'Triage Decision',
type: 'document',
fields: [
defineField({ name: 'issueId', type: 'number', validation: (Rule) => Rule.required() }),
defineField({ name: 'issueTitle', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'priority', type: 'string', validation: (Rule) => Rule.required() }),
defineField({ name: 'reasoning', type: 'text' }),
defineField({ name: 'matchedPatternIds', type: 'array', of: [{ type: 'reference', to: [{ type: 'triagePattern' }] }] }),
defineField({ name: 'resolution', type: 'string' }),
defineField({ name: 'agentAccuracy', type: 'string', options: { list: [
{ title: 'Correct', value: 'correct' },
{ title: 'Partially Correct', value: 'partial' },
{ title: 'Incorrect', value: 'incorrect' },
]}}),
defineField({ name: 'decidedAt', type: 'datetime' }),
],
})
Sanity Project Details
- Project ID: vni5slia
- Dataset: production (public read)
Agent Session
https://dev.to/agent_sessions/vibe-coding-sanity-hackthon-y8hbb0



Top comments (14)
Looks so Good!
Thanks Himanshu
Let's goo đĨŗ
Website looks amazing
Thanks rushu
Cfbr
Thanks Harshit
Lfg nice project
Thanks anish
can u teach me too ? lets connect
yes lets connect đĨ°
great bro . keep it up
Thanks Dacron
seems interesting to me as it could lower the amount of workload for me
next GSSOC preparation đ