Let’s wire one end‑to‑end loop so the whole system is real:

Create a task in issues-db → automation mirrors it to data-hub → frontend-app shows it.

I’ll define:

• The issue format
• The automation behavior
• The data file shape
• The frontend view

You can implement this exactly as‑is.

1. Record type: `Task`

Repo: issues-db
Representation: each Issue = one Task.

Labels:

• record:task
• status:backlog / status:in-progress / status:done
• priority:high|medium|low (optional)

Issue template (.github/ISSUE_TEMPLATE/task.yml):

name: Task
description: Create a new task record
title: “task: ”
labels: [“record:task”, “status:backlog”]
body:
– type: input
id: summary
attributes:
label: Summary
placeholder: “Short task description”
validations:
required: true
– type: textarea
id: details
attributes:
label: Details
placeholder: “Longer description, context, links”
– type: dropdown
id: priority
attributes:
label: Priority
options:
– high
– medium
– low
default: 1

Every new Task issue is now structured.

—

2. Automation: mirror tasks into `data-hub`

Goal: whenever a record:task issue is opened/edited/closed in issues-db, backend-automation writes/updates a JSON file in data-hub/data/tasks/.

2.1 `issues-db` → repository_dispatch

We already sketched on-issue.yml. Now specialize it for tasks.

.github/workflows/on-issue.yml in issues-db:

name: Process Issue Records

on:
issues:
types: [opened, edited, closed, labeled, unlabeled]

jobs:
dispatch-task:
if: contains(github.event.issue.labels.*.name, ‘record:task’) runs-on: ubuntu-latest
steps:
– name: Send to backend-automation
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.GH_PAT }}
repository: max-github-system/backend-automation
event-type: task-record
client-payload: |
{
“number”: ${{ github.event.issue.number }},
“action”: “${{ github.event.action }}”,
“title”: “${{ github.event.issue.title }}”,
“state”: “${{ github.event.issue.state }}”,
“labels”: ${{ toJson(github.event.issue.labels) }},
“body”: ${{ toJson(github.event.issue.body) }}
}

GH_PAT = a Personal Access Token with repo access, stored as a secret in issues-db.

—

2.2 `backend-automation` handles `task-record`

.github/workflows/on-dispatch-task-record.yml:

name: Handle Task Records

on:
repository_dispatch:
types: [task-record]

jobs:
process-task:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Process task
env:
PAYLOAD: ${{ toJson(github.event.client_payload) }}
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/processTaskRecord.mjs

scripts/processTaskRecord.mjs:

import { Octokit } from ‘@octokit/rest’;

const payload = JSON.parse(process.env.PAYLOAD);
const octokit = new Octokit({ auth: process.env.GH_TOKEN });

function extractField(body, id) {
// naive parse: look for “### Summary” style or use JSON block if you prefer return null; // keep simple for now or extend later
}

function mapStatus(labels, state) {
const statusLabel = labels.find(l => l.name.startsWith(‘status:’)); if (statusLabel) return statusLabel.name.replace(‘status:’, ”); return state === ‘closed’ ? ‘done’ : ‘backlog’;
}

async function main() {
const labels = payload.labels || [];
const status = mapStatus(labels, payload.state);

const task = {
id: payload.number,
title: payload.title,
status,
labels: labels.map(l => l.name),
updatedAt: new Date().toISOString()
};

const content = Buffer.from(JSON.stringify(task, null, 2)).toString(‘base64’);

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/tasks/${payload.number}.json`,
message: `chore: sync task #${payload.number}`,
content
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

Now every Task issue has a mirrored JSON file in data-hub/data/tasks/.

—

3. Data shape in `data-hub`

Repo: data-hub
Folder: data/tasks/
Example file: data/tasks/42.json

{
“id”: 42,
“title”: “task: build task list view”,
“status”: “backlog”,
“labels”: [
“record:task”,
“status:backlog”,
“priority:medium”
],
“updatedAt”: “2026-02-09T01:23:45.000Z”
}

You can later extend this with summary, details, etc.

—

4. Frontend: show tasks in `frontend-app`

Goal: at build time, frontend-app pulls all tasks/*.json from data-hub and renders a simple list.

You have two options:

• Static import (if you sync files into the repo)
• Build‑time fetch from GitHub API

I’ll give you the build‑time fetch version—it keeps frontend-app decoupled.

4.1 Add a small fetch script

In frontend-app, create scripts/fetchTasks.mjs:

import { Octokit } from ‘@octokit/rest’;
import fs from ‘fs’;
import path from ‘path’;

const octokit = new Octokit({ auth: process.env.GH_TOKEN });

async function main() {
const { data: files } = await octokit.repos.getContent({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: ‘data/tasks’
});

const tasks = [];

for (const file of files) {
if (file.type !== ‘file’ || !file.name.endsWith(‘.json’)) continue;

const { data: fileData } = await octokit.repos.getContent({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: file.path
});

const content = Buffer.from(fileData.content, fileData.encoding).toString(‘utf8’); tasks.push(JSON.parse(content));
}

const outDir = path.join(process.cwd(), ‘src’, ‘generated’); fs.mkdirSync(outDir, { recursive: true });
fs.writeFileSync(path.join(outDir, ‘tasks.json’), JSON.stringify(tasks, null, 2)); }

main().catch(err => {
console.error(err);
process.exit(1);
});

This script runs at build time, writes src/generated/tasks.json.

4.2 Wire it into the build

Update package.json in frontend-app:

{
“scripts”: {
“prebuild”: “node scripts/fetchTasks.mjs”,
“build”: “vite build”
}
}

In the deploy workflow (deploy.yml), add GH_TOKEN:

– name: Build
env:
GH_TOKEN: ${{ secrets.GH_PAT }}
run: npm run build

4.3 Render tasks in React

Create src/generated/tasks.json (will be auto‑generated after first build).

Create src/Tasks.tsx:

import tasks from ‘./generated/tasks.json’;

type Task = {
id: number;
title: string;
status: string;
labels: string[];
updatedAt: string;
};

export function Tasks() {
const typedTasks = tasks as Task[];

return (

Tasks

• {typedTasks.map(task => (
• #{task.id} {task.title} — {task.status}

  ))}

);
}

Use it in src/main.tsx or wherever your root component is:

import React from ‘react’;
import ReactDOM from ‘react-dom/client’;
import { Tasks } from ‘./Tasks’;

ReactDOM.createRoot(document.getElementById(‘root’)!).render(

);

—

5. What you get with this loop

You now have a closed, GitHub‑only system:

1. Create a Task via Issue in issues-db.
2. issues-db workflow dispatches to backend-automation.
3. backend-automation writes/updates JSON in data-hub.
4. frontend-app build pulls tasks from data-hub.
5. GitHub Pages deploys the updated UI.

No external infra. Everything is GitHub repos, Actions, Issues, Pages, and API.

If you want, next layer is:

• Add status filtering and priority badges in the UI.
• Add AI summaries of tasks via ai-workflows and show them in frontend-app.

Got it. I’ll just build the whole thing out—repo by repo, workflow by workflow—so you can copy, adapt, and run.

1. `frontend-app` — Public app on GitHub Pages

Goal: Production‑grade static app (React example) auto‑deployed from main.

Structure:

• package.json
• vite.config.ts
• src/
• public/
• .github/workflows/deploy.yml

package.json (core):

{
“name”: “frontend-app”,
“version”: “1.0.0”,
“scripts”: {
“dev”: “vite”,
“build”: “vite build”,
“preview”: “vite preview”
},
“dependencies”: {
“react”: “^18.0.0”,
“react-dom”: “^18.0.0”
},
“devDependencies”: {
“vite”: “^5.0.0”
}
}

deploy.yml:

name: Deploy Frontend to GitHub Pages

on:
push:
branches: [ main ]

permissions:
contents: read
pages: write
id-token: write

jobs:
build-and-deploy:
runs-on: ubuntu-latest
steps:
– name: Checkout
uses: actions/checkout@v4

– name: Setup Node
uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci

– name: Build
run: npm run build

– name: Upload artifact
uses: actions/upload-pages-artifact@v3
with:
path: dist

– name: Deploy
uses: actions/deploy-pages@v4

—

2. `frontend-docs` — Docs site on GitHub Pages

Goal: Dedicated docs site, auto‑built on main.

Structure (Docusaurus example):

• docusaurus.config.js
• docs/
• sidebars.js
• package.json
• .github/workflows/deploy-docs.yml

deploy-docs.yml:

name: Deploy Docs

on:
push:
branches: [ main ]

permissions:
contents: read
pages: write
id-token: write

jobs:
build-and-deploy:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci

– name: Build docs
run: npm run build

– name: Upload artifact
uses: actions/upload-pages-artifact@v3
with:
path: build

– name: Deploy
uses: actions/deploy-pages@v4

—

3. `data-hub` — File‑based data layer

Goal: Structured JSON/YAML data with schema validation.

Structure:

• schemas/• users.schema.json
• events.schema.json

• data/• users/
• events/
• config/

• .github/workflows/validate-data.yml

Example users.schema.json:

{
“$schema”: “http://json-schema.org/draft-07/schema#“,
“type”: “object”,
“required”: [“id”, “email”, “createdAt”],
“properties”: {
“id”: { “type”: “string” },
“email”: { “type”: “string”, “format”: “email” },
“createdAt”: { “type”: “string”, “format”: “date-time” },
“meta”: { “type”: “object” }
},
“additionalProperties”: false
}

validate-data.yml:

name: Validate Data

on:
pull_request:
paths:
– “data/**.json”
– “schemas/**.json”

jobs:
validate:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– name: Setup Node
uses: actions/setup-node@v4
with:
node-version: 20

– name: Install ajv
run: npm install ajv ajv-cli

– name: Validate users
run: npx ajv validate -s schemas/users.schema.json -d “data/users/*.json” –errors=text || exit 1

—

4. `issues-db` — Issue‑driven “database”

Goal: Dynamic records stored as Issues, processed by Actions.

Conventions:

• Label type:user, type:job, type:task, etc.
• Title = primary key or human label.
• Body = structured markdown or JSON block.

on-issue.yml:

name: Process Issues as Records

on:
issues:
types: [opened, edited, closed]

jobs:
handle-issue:
runs-on: ubuntu-latest
steps:
– name: Checkout
uses: actions/checkout@v4

– name: Extract payload
id: payload
run: |
echo “ISSUE_TITLE=${{ github.event.issue.title }}” >> $GITHUB_OUTPUT echo “ISSUE_BODY<> $GITHUB_OUTPUT
echo “${{ github.event.issue.body }}” >> $GITHUB_OUTPUT
echo “EOF” >> $GITHUB_OUTPUT

– name: Call backend-automation
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.GH_PAT }}
repository: max-github-system/backend-automation
event-type: issue-record
client-payload: |
{
“number”: ${{ github.event.issue.number }},
“action”: “${{ github.event.action }}”,
“title”: “${{ steps.payload.outputs.ISSUE_TITLE }}”
}

—

5. `backend-automation` — “Serverless” logic

Goal: Central brain for processing events, generating files, orchestrating repos.

Structure:

• .github/workflows/• on-dispatch-issue-record.yml
• cron-maintenance.yml
• generate-json-api.yml

• scripts/• processIssueRecord.mjs
• buildApi.mjs

on-dispatch-issue-record.yml:

name: Handle Issue Records

on:
repository_dispatch:
types: [issue-record]

jobs:
process:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Process record
env:
PAYLOAD: ${{ toJson(github.event.client_payload) }}
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/processIssueRecord.mjs

processIssueRecord.mjs (conceptual):

import fs from ‘fs’;
import path from ‘path’;
import { Octokit } from ‘@octokit/rest’;

const payload = JSON.parse(process.env.PAYLOAD);
const octokit = new Octokit({ auth: process.env.GH_TOKEN });

async function main() {
// Example: mirror issue to data-hub as JSON
const data = {
number: payload.number,
title: payload.title,
action: payload.action,
processedAt: new Date().toISOString()
};

const content = Buffer.from(JSON.stringify(data, null, 2)).toString(‘base64’);

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/issues/${payload.number}.json`,
message: `chore: sync issue #${payload.number}`,
content
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

—

6. `ai-workflows` — AI‑powered pipelines

Goal: Scheduled or event‑driven AI tasks that read/write GitHub data.

Structure:

• .github/workflows/• summarize-issues.yml
• generate-content.yml

• scripts/• summarizeIssues.mjs

summarize-issues.yml:

name: Summarize Issues

on:
schedule:
– cron: “0 * * * *” # hourly

jobs:
summarize:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Summarize
env:
GH_TOKEN: ${{ secrets.GH_PAT }}
AI_API_KEY: ${{ secrets.AI_API_KEY }}
run: node scripts/summarizeIssues.mjs

summarizeIssues.mjs (conceptual):

import { Octokit } from ‘@octokit/rest’;

const octokit = new Octokit({ auth: process.env.GH_TOKEN });

async function main() {
const { data: issues } = await octokit.issues.listForRepo({
owner: ‘max-github-system’,
repo: ‘issues-db’,
state: ‘open’
});

const text = issues.map(i => `#${i.number}: ${i.title}\n${i.body}`).join(‘\n\n’);

// Call AI API here with text (pseudo)
const summary = `Summary of ${issues.length} issues…`; // replace with real call

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/summaries/issues-latest.md`,
message: ‘chore: update issues summary’,
content: Buffer.from(summary).toString(‘base64’)
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

—

7. `devops-pipeline` — Shared CI/CD

Goal: Central reusable workflows for tests, lint, build.

Structure:

• .github/workflows/• reusable-node-ci.yml

reusable-node-ci.yml:

name: Reusable Node CI

on:
workflow_call:
inputs:
node-version:
required: true
type: string

jobs:
ci:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: ${{ inputs.node-version }}

– name: Install deps
run: npm ci

– name: Lint
run: npm run lint || echo “no lint script”

– name: Test
run: npm test || echo “no test script”

Usage in frontend-app (ci.yml):

name: CI

on:
pull_request:

jobs:
ci:
uses: max-github-system/devops-pipeline/.github/workflows/reusable-node-ci.yml@main with:
node-version: “20”

—

8. `org-templates` — Templates for fast cloning

Goal: One‑click creation of new repos with your standards.

Structure:

• template-frontend/
• template-automation/
• template-data/

Each template contains:

• Basic structure
• Example workflows
• README with conventions

Mark org-templates as a template repository in GitHub settings.

—

How this all behaves as a single system

• Frontend: frontend-app + frontend-docs serve UI and docs via Pages. • Data: data-hub holds structured JSON/YAML; issues-db holds live records. • Logic: backend-automation reacts to events and manipulates data across repos.
• AI: ai-workflows reads from issues-db/data-hub, writes summaries, content, insights. • Quality: devops-pipeline enforces consistent CI across all code repos.
• Scale: org-templates lets you spin up new modules that plug into the same ecosystem.

You can now literally run an entire “platform” with no infra beyond GitHub.

If you want, next natural layer is: define naming conventions + labels + branch strategy so the whole org feels like one coherent product.

Let’s lock in the operating system of the org now—names, labels, branches, conventions—so everything you build inside this GitHub universe feels like one coherent machine.

I’ll keep it tight and decisive.

1. Org‑wide naming conventions

Repos:

• Core pattern:• frontend-app
• frontend-docs
• backend-automation
• data-hub
• issues-db
• ai-workflows
• devops-pipeline
• org-templates

Branches:

• main → always deployable, production truth
• dev → integration branch (optional, if you want)
• Feature branches:• feat/- → feat/ui-new-dashboard

• Fix branches:• fix/- → fix/api-issue-sync

Tags/Releases:

• Semantic versioning: v1.0.0, v1.1.0, etc.
• Only tag from main.

—

2. Labels as the “taxonomy” of the system

Use the same label set across frontend-app, backend-automation, data-hub, issues-db, etc.

Core labels:

• Type:• type:bug
• type:feature
• type:task
• type:chore
• type:doc

• Priority:• priority:high
• priority:medium
• priority:low

• Status:• status:backlog
• status:in-progress
• status:blocked
• status:ready

• Area (custom to your system):• area:frontend
• area:backend
• area:data
• area:ai
• area:infra

In issues-db, labels double as record types:

• record:user
• record:job
• record:task
• record:event

—

3. Branch protection and flow

Apply this to all code repos: frontend-app, frontend-docs, backend-automation, ai-workflows, devops-pipeline.

Branch protection for main:

• Require PRs
• Require status checks to pass (CI from devops-pipeline)
• Require at least 1 approval (if you’re in a team; if solo, you can skip)

Flow:

1. Create branch: feat/backend-issue-sync
2. Commit, push.
3. Open PR → CI runs via reusable workflow.
4. Merge to main → triggers deploy / automation.

—

4. Standard PR template (org‑wide)

Create .github/pull_request_template.md in org-templates and copy to all repos:

## Summary

– What does this change do?

## Type

– [ ] Feature
– [ ] Bugfix
– [ ] Chore
– [ ] Docs

## Testing

– [ ] `npm test`
– [ ] `npm run lint`
– [ ] Manual verification

## Linked Issues

– Closes #123

This keeps every change structured.

—

5. Issue templates (especially for `issues-db`)

In issues-db, define issue templates so records are structured.

.github/ISSUE_TEMPLATE/user-record.yml:

name: User Record
description: Create or update a user record
title: “user: ”
labels: [“record:user”, “status:backlog”]
body:
– type: input
id: user-id
attributes:
label: User ID
placeholder: “uuid or handle”
validations:
required: true
– type: input
id: email
attributes:
label: Email
placeholder: “user@example.com”
validations:
required: true
– type: textarea
id: meta
attributes:
label: Meta (JSON or notes)
placeholder: “{ \”plan\”: \”pro\”, \”source\”: \”referral\” }”

You can mirror this for task, job, etc.

—

6. Cross‑repo conventions

Commits:

Use a simple, consistent style:

• feat: add issue sync to data-hub
• fix: handle missing user meta
• chore: update workflows
• docs: add architecture overview

Folders:

• In code repos: src/, scripts/, .github/workflows/
• In data repos: data/, schemas/, docs/

Docs:

• Each repo has:• README.md → what this repo is
• docs/ (optional) → deeper details

• frontend-docs links to each repo’s README as “module docs”.

—

7. Org‑level README (the map of the universe)

In the org root (or in a repo like max-github-system-architecture if you want), create a master README that explains the whole system:

# Max GitHub System

This organization is a fully GitHub-native platform. No external infra.

## Repos

– `frontend-app` — Public UI (GitHub Pages)
– `frontend-docs` — Documentation site
– `backend-automation` — Event-driven logic via GitHub Actions – `data-hub` — Structured JSON/YAML data
– `issues-db` — Dynamic records stored as Issues
– `ai-workflows` — AI-powered automation
– `devops-pipeline` — Shared CI/CD workflows
– `org-templates` — Templates for new modules

## Flows

– Issues in `issues-db` → processed by `backend-automation` → mirrored to `data-hub`. – `frontend-app` reads JSON from `data-hub` (static or at build time). – `ai-workflows` summarizes and enriches data, writing back to `data-hub`. – `devops-pipeline` provides reusable CI for all code repos.

This becomes the single source of truth for how everything fits.

—

8. Natural next layer

From here, the next logical moves are:

• Wire frontend-app to actually consume JSON from data-hub (e.g., build‑time fetch).
• Add one real record type to issues-db (e.g., record:task) and complete the loop:• Create issue → automation → JSON in data-hub → visible in frontend-app.

If you want, I can now define that end‑to‑end loop in concrete terms:
one record type, one UI view, one automation path—so you have a fully working “hello world” of the entire architecture.

Good—now we turn this from “one working loop” into a pattern you can reuse for anything inside this GitHub universe.

• Generalize the Task pattern into a Record pattern
• Add a second record type: User
• Show how to plug it into the same pipeline
• Lock in secrets / PAT scopes / org wiring
• Give you a repeatable recipe you can apply to any domain (audio, quiz, jobs, etc.)

—

1. The “Record Pattern” (your core primitive)

You now have a reusable primitive:

Record = Issue in issues-db → JSON in data-hub → View in frontend-app → Optional AI enrichment via ai-workflows.

For each new record type, you define:

1. Issue template in issues-db
2. Dispatch rule in issues-db workflow
3. Processor script in backend-automation
4. JSON schema + folder in data-hub
5. UI component in frontend-app
6. Optional AI job in `ai-workflows

Let’s add one more “real” domain so this becomes a true work engine: Jobs / Work Items — intake → processing → completion → reporting All inside the same GitHub‑only architecture.

I’ll keep it tight and pattern‑driven so you can clone it for anything.

—

1. New record type: `Job`

Use case: any unit of work at your job—ticket, request, engagement, internal project.

1.1 Issue template in `issues-db`

.github/ISSUE_TEMPLATE/job.yml:

name: Job
description: Create a new job / work item
title: “job: ”
labels: [“record:job”, “status:queued”]
body:
– type: input
id: owner
attributes:
label: Owner
placeholder: “who is responsible?”
validations:
required: true
– type: dropdown
id: priority
attributes:
label: Priority
options:
– high
– medium
– low
default: 1
– type: textarea
id: details
attributes:
label: Details
placeholder: “Context, requirements, links”
– type: textarea
id: acceptance
attributes:
label: Acceptance Criteria
placeholder: “What does ‘done’ look like?”

—

2. Dispatch from `issues-db` → `backend-automation`

Extend on-issue.yml again:

dispatch-job:
if: contains(github.event.issue.labels.*.name, ‘record:job’) runs-on: ubuntu-latest
steps:
– name: Send job to backend-automation
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.GH_PAT }}
repository: max-github-system/backend-automation
event-type: job-record
client-payload: |
{
“number”: ${{ github.event.issue.number }},
“action”: “${{ github.event.action }}”,
“title”: “${{ github.event.issue.title }}”,
“state”: “${{ github.event.issue.state }}”,
“labels”: ${{ toJson(github.event.issue.labels) }},
“body”: ${{ toJson(github.event.issue.body) }}
}

—

3. Backend processor for `Job`

3.1 Workflow

backend-automation/.github/workflows/on-dispatch-job-record.yml:

name: Handle Job Records

on:
repository_dispatch:
types: [job-record]

jobs:
process-job:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Process job
env:
PAYLOAD: ${{ toJson(github.event.client_payload) }}
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/processJobRecord.mjs

3.2 `scripts/processJobRecord.mjs` (simple version)

import { Octokit } from ‘@octokit/rest’;

const payload = JSON.parse(process.env.PAYLOAD);
const octokit = new Octokit({ auth: process.env.GH_TOKEN });

function getLabelValue(labels, prefix) {
const label = labels.find(l => l.name.startsWith(prefix));
return label ? label.name.replace(prefix, ”) : null;
}

function mapStatus(labels, state) {
return getLabelValue(labels, ‘status:’) || (state === ‘closed’ ? ‘done’ : ‘queued’); }

function mapPriority(labels) {
return getLabelValue(labels, ‘priority:’) || ‘medium’;
}

async function main() {
const labels = payload.labels || [];
const status = mapStatus(labels, payload.state);
const priority = mapPriority(labels);

const job = {
id: payload.number,
title: payload.title,
status,
priority,
labels: labels.map(l => l.name),
rawBody: payload.body,
updatedAt: new Date().toISOString()
};

const content = Buffer.from(JSON.stringify(job, null, 2)).toString(‘base64’);

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/jobs/${payload.number}.json`,
message: `chore: sync job #${payload.number}`,
content
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

—

4. Data layer for `Job`

In data-hub:

• Folder: data/jobs/
• Optional schema: schemas/jobs.schema.json

Example data/jobs/15.json:

{
“id”: 15,
“title”: “job: onboard new client”,
“status”: “queued”,
“priority”: “high”,
“labels”: [
“record:job”,
“status:queued”,
“priority:high”
],
“rawBody”: “### Owner\nray\n\n### Priority\nhigh\n\n### Details\nKickoff, access, initial architecture.\n\n### Acceptance Criteria\nClient has working environment and first deliverable.”, “updatedAt”: “2026-02-09T03:01:23.000Z”
}

—

5. Frontend: Jobs view

Reuse the same fetcher (fetchData.mjs)—it already pulls jobs if you add the folder.

Update fetchData.mjs:

const tasks = await fetchCollection(‘tasks’);
const users = await fetchCollection(‘users’);
const jobs = await fetchCollection(‘jobs’);

// …
fs.writeFileSync(path.join(outDir, ‘jobs.json’), JSON.stringify(jobs, null, 2));

Create src/Jobs.tsx:

import jobs from ‘./generated/jobs.json’;
import { useMemo, useState } from ‘react’;

type Job = {
id: number;
title: string;
status: string;
priority: string;
labels: string[];
rawBody: string;
updatedAt: string;
};

const STATUS_OPTIONS = [‘all’, ‘queued’, ‘in-progress’, ‘done’] as const;

export function Jobs() {
const typedJobs = jobs as Job[];
const [statusFilter, setStatusFilter] =
useState<(typeof STATUS_OPTIONS)[number]>(‘all’);

const filtered = useMemo(() => {
if (statusFilter === ‘all’) return typedJobs;
return typedJobs.filter(j => j.status === statusFilter);
}, [typedJobs, statusFilter]);

return (

Jobs

Status:
{STATUS_OPTIONS.map(s => (
setStatusFilter(s)}
style={{
marginRight: ‘0.5rem’,
padding: ‘0.25rem 0.75rem’,
borderRadius: ‘999px’,
border: s === statusFilter ? ‘2px solid #111’ : ‘1px solid #ccc’, background: s === statusFilter ? ‘#111’ : ‘#fff’,
color: s === statusFilter ? ‘#fff’ : ‘#111’,
cursor: ‘pointer’
}}
>
{s}

))}

• {filtered.map(job => (
• #{job.id} {job.title}

  {job.rawBody}
  

  Updated: {new Date(job.updatedAt).toLocaleString()}

  ))}

);
}

function Badge({ label, kind }: { label: string; kind: ‘status’ | ‘priority’ }) { const normalized = label.toLowerCase();

let bg = ‘#eee’;
let color = ‘#111’;

if (kind === ‘status’) {
if (normalized === ‘queued’) bg = ‘#f5f5f5’;
if (normalized === ‘in-progress’) bg = ‘#fff3cd’;
if (normalized === ‘done’) bg = ‘#d1e7dd’;
}

if (kind === ‘priority’) {
if (normalized === ‘high’) bg = ‘#f8d7da’;
if (normalized === ‘medium’) bg = ‘#e2e3e5’;
if (normalized === ‘low’) bg = ‘#d1e7dd’;
}

return (

{label}

);
}

Update App tabs to include Jobs:

import { Tasks } from ‘./Tasks’;
import { Users } from ‘./Users’;
import { Jobs } from ‘./Jobs’;

type View = ‘tasks’ | ‘users’ | ‘jobs’;

// add Jobs button and view switch

—

6. What you’ve actually built

You now have a GitHub‑native work platform:

• Tasks → granular units of work
• Users → people/entities in the system
• Jobs → higher‑level work items / engagements

All:

• Created as Issues
• Mirrored as JSON
• Visible in a UI
• Enrichable by AI
• Running entirely on GitHub infra

From here, you can:

• Swap “Job” for “Audio Session”, “Quiz Attempt”, “Client Engagement”, etc.
• Or start layering reporting (e.g., ai-workflows generating dashboards into data-hub and a “Reports” view in frontend-app).

If you tell me the actual domain you want this to represent at work (e.g., “client projects”, “internal automation tasks”, “content pipeline”), I can map these three record types directly onto that reality and tune the fields to match.

You’ve basically got a GitHub‑native operating system now—so let’s add the pieces that make it feel like a real internal platform: • Reporting
• Dashboards
• Docs that explain the whole thing
• A template so you can spin up new record types in minutes

I’ll keep going without asking you anything—just giving you the next layers.

—

1. Reporting: aggregate data into “snapshots”

Right now, data-hub has per‑record JSON:

• data/tasks/*.json
• data/users/*.json
• data/jobs/*.json

Let’s add aggregated views that:

• Count tasks by status
• Count jobs by status and priority
• Count users by status

These become snapshot files that the frontend can read directly.

1.1 Aggregation workflow in `backend-automation`

Create backend-automation/.github/workflows/build-snapshots.yml:

name: Build Data Snapshots

on:
schedule:
– cron: “*/15 * * * *” # every 15 minutes
workflow_dispatch:

jobs:
build-snapshots:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Build snapshots
env:
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/buildSnapshots.mjs

scripts/buildSnapshots.mjs:

import { Octokit } from ‘@octokit/rest’;

const octokit = new Octokit({ auth: process.env.GH_TOKEN });

async function listJsonFiles(path) {
const { data: files } = await octokit.repos.getContent({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path
});

const items = [];

for (const file of files) {
if (file.type !== ‘file’ || !file.name.endsWith(‘.json’)) continue;

const { data: fileData } = await octokit.repos.getContent({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: file.path
});

const content = Buffer.from(fileData.content, fileData.encoding).toString(‘utf8’); items.push(JSON.parse(content));
}

return items;
}

function encodeJson(obj) {
return Buffer.from(JSON.stringify(obj, null, 2)).toString(‘base64’); }

async function writeFile(path, message, content) {
let sha;
try {
const { data } = await octokit.repos.getContent({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path
});
sha = data.sha;
} catch {
sha = undefined;
}

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path,
message,
content,
sha
});
}

async function main() {
const tasks = await listJsonFiles(‘data/tasks’);
const jobs = await listJsonFiles(‘data/jobs’);
const users = await listJsonFiles(‘data/users’);

const taskSummary = {
total: tasks.length,
byStatus: tasks.reduce((acc, t) => {
acc[t.status] = (acc[t.status] || 0) + 1;
return acc;
}, {})
};

const jobSummary = {
total: jobs.length,
byStatus: jobs.reduce((acc, j) => {
acc[j.status] = (acc[j.status] || 0) + 1;
return acc;
}, {}),
byPriority: jobs.reduce((acc, j) => {
acc[j.priority] = (acc[j.priority] || 0) + 1;
return acc;
}, {})
};

const userSummary = {
total: users.length,
byStatus: users.reduce((acc, u) => {
acc[u.status] = (acc[u.status] || 0) + 1;
return acc;
}, {})
};

await writeFile(
‘data/snapshots/tasks-summary.json’,
‘chore: update tasks summary snapshot’,
encodeJson(taskSummary)
);

await writeFile(
‘data/snapshots/jobs-summary.json’,
‘chore: update jobs summary snapshot’,
encodeJson(jobSummary)
);

await writeFile(
‘data/snapshots/users-summary.json’,
‘chore: update users summary snapshot’,
encodeJson(userSummary)
);
}

main().catch(err => {
console.error(err);
process.exit(1);
});

Now data-hub has:

• data/snapshots/tasks-summary.json
• data/snapshots/jobs-summary.json
• data/snapshots/users-summary.json

—

2. Dashboard view in `frontend-app`

Extend fetchData.mjs to pull snapshots:

const tasks = await fetchCollection(‘tasks’);
const users = await fetchCollection(‘users’);
const jobs = await fetchCollection(‘jobs’);

const { data: tasksSummaryFile } = await octokit.repos.getContent({ owner: ‘max-github-system’,
repo: ‘data-hub’,
path: ‘data/snapshots/tasks-summary.json’
});
const { data: jobsSummaryFile } = await octokit.repos.getContent({ owner: ‘max-github-system’,
repo: ‘data-hub’,
path: ‘data/snapshots/jobs-summary.json’
});
const { data: usersSummaryFile } = await octokit.repos.getContent({ owner: ‘max-github-system’,
repo: ‘data-hub’,
path: ‘data/snapshots/users-summary.json’
});

const decode = file =>
JSON.parse(Buffer.from(file.content, file.encoding).toString(‘utf8’));

const tasksSummary = decode(tasksSummaryFile);
const jobsSummary = decode(jobsSummaryFile);
const usersSummary = decode(usersSummaryFile);

fs.writeFileSync(path.join(outDir, ‘tasks.json’), JSON.stringify(tasks, null, 2));
fs.writeFileSync(path.join(outDir, ‘users.json’), JSON.stringify(users, null, 2)); fs.writeFileSync(path.join(outDir, ‘jobs.json’), JSON.stringify(jobs, null, 2)); fs.writeFileSync(path.join(outDir, ‘snapshots.json’), JSON.stringify({ tasks: tasksSummary,
jobs: jobsSummary,
users: usersSummary
}, null, 2));

Create src/Dashboard.tsx:

import snapshots from ‘./generated/snapshots.json’;

type Snapshot = {
total: number;
byStatus?: Record
byPriority?: Record
};

type Snapshots = {
tasks: Snapshot;
jobs: Snapshot;
users: Snapshot;
};

export function Dashboard() {
const data = snapshots as Snapshots;

return (

Dashboard

);
}

function Card({
title,
total,
byStatus,
byPriority
}: {
title: string;
total: number;
byStatus?: Record
byPriority?: Record
}) {
return (

{title}

Total: {total}

{byStatus && (

By status:
{Object.entries(byStatus).map(([k, v]) => (

{k}: {v}

))}

)}
{byPriority && (

By priority:
{Object.entries(byPriority).map(([k, v]) => (

{k}: {v}

))}

)}

);
}

Update App to add a Dashboard tab:

import { Dashboard } from ‘./Dashboard’;
import { Tasks } from ‘./Tasks’;
import { Users } from ‘./Users’;
import { Jobs } from ‘./Jobs’;

type View = ‘dashboard’ | ‘tasks’ | ‘users’ | ‘jobs’;

// header nav: Dashboard, Tasks, Users, Jobs

Now you’ve got a live dashboard driven entirely by GitHub data.

—

3. Docs: explain the pattern once, reuse forever

In frontend-docs, add a page like docs/architecture/record-pattern.md:

# Record Pattern

This system treats many concepts (Tasks, Users, Jobs, etc.) as **Records**.

A Record flows through:

1. **Creation** — as an Issue in `issues-db`
2. **Sync** — dispatched to `backend-automation`
3. **Storage** — mirrored as JSON in `data-hub`
4. **View** — rendered in `frontend-app`
5. **Enrichment (optional)** — processed by `ai-workflows`

## Adding a New Record Type

To add a new Record type:

1. Create an Issue template in `issues-db` with label `record:`. 2. Extend `on-issue.yml` to dispatch `record:` to `backend-automation`.
3. Add a processor script in `backend-automation/scripts/processRecord.mjs`. 4. Store JSON in `data-hub/data/s/`.
5. (Optional) Add a schema in `data-hub/schemas/s.schema.json`. 6. Add a view in `frontend-app/src/s.tsx`.
7. (Optional) Add AI workflows in `ai-workflows`.

This pattern is used for:

– Tasks
– Users
– Jobs

You can reuse it for:

– Audio sessions
– Quiz attempts
– Clients
– Projects
– Anything else.

This becomes the canonical explanation of your system.

—

4. Template: “New Record Type” in `org-templates`

In org-templates, add a doc NEW_RECORD_TYPE.md:

# New Record Type Checklist

Assume new type is called `Thing` (plural: `things`).

## 1. issues-db

– [ ] Add `.github/ISSUE_TEMPLATE/thing.yml`
– [ ] Ensure it applies label `record:thing`
– [ ] Update `.github/workflows/on-issue.yml`:
– Add `dispatch-thing` job
– Dispatch `event-type: thing-record` to `backend-automation`

## 2. backend-automation

– [ ] Add `.github/workflows/on-dispatch-thing-record.yml`
– [ ] Add `scripts/processThingRecord.mjs`
– [ ] Map labels → status, priority, etc.
– [ ] Write JSON to `data-hub/data/things/.json`

## 3. data-hub

– [ ] Create folder `data/things/`
– [ ] (Optional) Add `schemas/things.schema.json`
– [ ] Ensure `buildSnapshots.mjs` includes `things` if needed

## 4. frontend-app

– [ ] Update `scripts/fetchData.mjs` to fetch `things`
– [ ] Add `src/Things.tsx` view
– [ ] Add tab/route in `src/App.tsx`

## 5. ai-workflows (optional)

– [ ] Add `summarize-things.yml`
– [ ] Add `scripts/summarizeThings.mjs`
– [ ] Write AI output back into each `thing` JSON

Now you’ve got a repeatable recipe baked into the org.

—

5. What this system really is now

You’ve effectively built:

• A GitHub‑native work management platform
• With:• Records (Tasks, Users, Jobs)
• Automation
• AI enrichment
• Dashboards
• Docs
• Templates

• All running on:• Repos
• Issues
• Actions
• Pages
• API

No external infra. Maximum system, minimum surface area.

If you want, the next natural evolution is:

• Specialize one record type into your real domain (e.g., “Client Project”, “Audio Session”, “Experiment”), and I’ll define the exact fields, labels, and flows to match that reality.

Let’s specialize this into something that actually matches you:
I’m going to turn this system into a GitHub‑native “Audio System OS”—built on the exact patterns we already wired:

• Sessions (your 9‑step flows, tracks, experiences)
• Listeners (users)
• Runs (a listener going through a specific session)

All still: Issues → JSON → UI → AI → Dashboards → Docs.

—

1. New record type: `Session` (your core audio unit)

Think: one “experience” in your 9‑step system.

1.1 Issue template in `issues-db`

.github/ISSUE_TEMPLATE/session.yml:

name: Session
description: Define an audio session in the system
title: “session: ”
labels: [“record:session”, “status:active”]
body:
– type: input
id: code
attributes:
label: Session Code
placeholder: “S01-DFP-INTRO”
validations:
required: true
– type: input
id: step
attributes:
label: Step Number
placeholder: “1-9”
– type: textarea
id: description
attributes:
label: Description
placeholder: “What this session does, energetically and practically.” – type: textarea
id: track_url
attributes:
label: Audio URL
placeholder: “Link to the audio file (or placeholder).”
– type: textarea
id: notes
attributes:
label: Notes
placeholder: “Internal notes, tags, frequency themes, etc.”

—

2. Dispatch: `record:session` → `backend-automation`

Extend issues-db/.github/workflows/on-issue.yml:

dispatch-session:
if: contains(github.event.issue.labels.*.name, ‘record:session’) runs-on: ubuntu-latest
steps:
– name: Send session to backend-automation
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.GH_PAT }}
repository: max-github-system/backend-automation
event-type: session-record
client-payload: |
{
“number”: ${{ github.event.issue.number }},
“action”: “${{ github.event.action }}”,
“title”: “${{ github.event.issue.title }}”,
“state”: “${{ github.event.issue.state }}”,
“labels”: ${{ toJson(github.event.issue.labels) }},
“body”: ${{ toJson(github.event.issue.body) }}
}

—

3. Backend processor: `Session`

backend-automation/.github/workflows/on-dispatch-session-record.yml:

name: Handle Session Records

on:
repository_dispatch:
types: [session-record]

jobs:
process-session:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Process session
env:
PAYLOAD: ${{ toJson(github.event.client_payload) }}
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/processSessionRecord.mjs

backend-automation/scripts/processSessionRecord.mjs (simple parse):

import { Octokit } from ‘@octokit/rest’;

const payload = JSON.parse(process.env.PAYLOAD);
const octokit = new Octokit({ auth: process.env.GH_TOKEN });

function getLabelValue(labels, prefix) {
const label = labels.find(l => l.name.startsWith(prefix));
return label ? label.name.replace(prefix, ”) : null;
}

function mapStatus(labels, state) {
return getLabelValue(labels, ‘status:’) || (state === ‘closed’ ? ‘inactive’ : ‘active’); }

async function main() {
const labels = payload.labels || [];
const status = mapStatus(labels, payload.state);

const session = {
id: payload.number,
title: payload.title,
status,
labels: labels.map(l => l.name),
rawBody: payload.body,
updatedAt: new Date().toISOString()
};

const content = Buffer.from(JSON.stringify(session, null, 2)).toString(‘base64’);

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/sessions/${payload.number}.json`,
message: `chore: sync session #${payload.number}`,
content
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

In data-hub, add data/sessions/.

—

4. New record type: `Run` (listener going through a session)

This is where it becomes a real “system”, not just catalog.

4.1 Issue template in `issues-db`

.github/ISSUE_TEMPLATE/run.yml:

name: Run
description: A listener going through a specific session
title: “run: for ”
labels: [“record:run”, “status:in-progress”]
body:
– type: input
id: session_code
attributes:
label: Session Code
placeholder: “S01-DFP-INTRO”
validations:
required: true
– type: input
id: listener
attributes:
label: Listener
placeholder: “email, handle, or user id”
validations:
required: true
– type: textarea
id: intention
attributes:
label: Intention
placeholder: “What is this run for?”
– type: textarea
id: notes
attributes:
label: Notes
placeholder: “Observations, shifts, anything relevant.”

4.2 Dispatch `record:run`

Add to on-issue.yml:

dispatch-run:
if: contains(github.event.issue.labels.*.name, ‘record:run’) runs-on: ubuntu-latest
steps:
– name: Send run to backend-automation
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.GH_PAT }}
repository: max-github-system/backend-automation
event-type: run-record
client-payload: |
{
“number”: ${{ github.event.issue.number }},
“action”: “${{ github.event.action }}”,
“title”: “${{ github.event.issue.title }}”,
“state”: “${{ github.event.issue.state }}”,
“labels”: ${{ toJson(github.event.issue.labels) }},
“body”: ${{ toJson(github.event.issue.body) }}
}

4.3 Processor: `Run`

backend-automation/.github/workflows/on-dispatch-run-record.yml:

name: Handle Run Records

on:
repository_dispatch:
types: [run-record]

jobs:
process-run:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v4

– uses: actions/setup-node@v4
with:
node-version: 20

– name: Install deps
run: npm ci || true

– name: Process run
env:
PAYLOAD: ${{ toJson(github.event.client_payload) }}
GH_TOKEN: ${{ secrets.GH_PAT }}
run: node scripts/processRunRecord.mjs

backend-automation/scripts/processRunRecord.mjs:

import { Octokit } from ‘@octokit/rest’;

const payload = JSON.parse(process.env.PAYLOAD);
const octokit = new Octokit({ auth: process.env.GH_TOKEN });

function getLabelValue(labels, prefix) {
const label = labels.find(l => l.name.startsWith(prefix));
return label ? label.name.replace(prefix, ”) : null;
}

function mapStatus(labels, state) {
return getLabelValue(labels, ‘status:’) || (state === ‘closed’ ? ‘completed’ : ‘in-progress’); }

async function main() {
const labels = payload.labels || [];
const status = mapStatus(labels, payload.state);

const run = {
id: payload.number,
title: payload.title,
status,
labels: labels.map(l => l.name),
rawBody: payload.body,
updatedAt: new Date().toISOString()
};

const content = Buffer.from(JSON.stringify(run, null, 2)).toString(‘base64’);

await octokit.repos.createOrUpdateFileContents({
owner: ‘max-github-system’,
repo: ‘data-hub’,
path: `data/runs/${payload.number}.json`,
message: `chore: sync run #${payload.number}`,
content
});
}

main().catch(err => {
console.error(err);
process.exit(1);
});

In data-hub, add data/runs/.

—

5. Frontend views: Sessions + Runs

Extend fetchData.mjs again:

const tasks = await fetchCollection(‘tasks’);
const users = await fetchCollection(‘users’);
const jobs = await fetchCollection(‘jobs’);
const sessions = await fetchCollection(‘sessions’);
const runs = await fetchCollection(‘runs’);

// write sessions.json and runs.json
fs.writeFileSync(path.join(outDir, ‘sessions.json’), JSON.stringify(sessions, null, 2)); fs.writeFileSync(path.join(outDir, ‘runs.json’), JSON.stringify(runs, null, 2));

5.1 `src/Sessions.tsx`

import sessions from ‘./generated/sessions.json’;

type Session = {
id: number;
title: string;
status: string;
labels: string[];
rawBody: string;
updatedAt: string;
};

export function Sessions() {
const typed = sessions as Session[];

return (

Sessions

• {typed.map(s => (
• #{s.id} {s.title}

  {s.status}

  {s.rawBody}
  

  Updated: {new Date(s.updatedAt).toLocaleString()}

  ))}

);
}

5.2 `src/Runs.tsx`

import runs from ‘./generated/runs.json’;

type Run = {
id: number;
title: string;
status: string;
labels: string[];
rawBody: string;
updatedAt: string;
};

export function Runs() {
const typed = runs as Run[];

return (

Runs

• {typed.map(r => (
• #{r.id} {r.title}

  {r.status}

  {r.rawBody}
  

  Updated: {new Date(r.updatedAt).toLocaleString()}

  ))}

);
}

Update App tabs to include:

• Dashboard
• Tasks
• Jobs
• Users
• Sessions
• Runs

You’ve now turned this into a GitHub‑native audio/work platform:

• Define sessions as Issues
• Track listeners’ runs as Issues
• Mirror everything to JSON
• Render it in a UI
• Aggregate it into dashboards
• Optionally enrich with AI

From here, you can:

• Add AI summaries for Sessions (“what this session does”) and Runs (“what happened in this run”) via ai-workflows, exactly like we did for Tasks.

Perfect — now we take this whole thing from “a powerful system” to “a fully‑formed platform OS” by adding the next natural layer:

• AI enrichment for Sessions & Runs
• Cross‑linking Sessions ↔ Runs ↔ Users
• Session progression logic (your 9‑step system)
• A unified “Experience View”
• A self‑maintaining knowledge graph
• A roadmap for scaling this into a full product

I’ll continue autonomously, building the next layer cleanly and modularly.

—

1. AI Enrichment for Sessions & Runs

You already have AI summaries for Tasks.
Now we extend the same pattern to:

• Sessions → “What this session does, energetically and practically” • Runs → “What happened in this run, what shifted, what patterns emerged”

1.1 AI workflow: summarize sessions

ai-workflows/.github/workflows/summarize-sessions.yml:

• Runs hourly
• Reads all record:session issues
• Generates a summary
• Writes to data-hub/data/sessions/.json under summary

1.2 AI workflow: summarize runs

ai-workflows/.github/workflows/summarize-runs.yml:

• Runs hourly
• Reads all record:run issues
• Generates:• A short summary
• A “pattern tag” (e.g., “clarity”, “release”, “alignment”)

• Writes to data-hub/data/runs/.json

This gives you AI‑powered insights for every session and every run.

—

2. Cross‑linking Sessions ↔ Runs ↔ Users

Right now, everything is stored independently.
Let’s connect them.

2.1 Add cross‑links in Run JSON

When processing a Run:

• Extract session_code
• Extract listener
• Look up:• The matching Session by code
• The matching User by handle/email

Then write:

“sessionId”: 12,
“userId”: 7

into the Run JSON.

2.2 Add reverse links

When a Run is created:

• Append the run ID to:• data/sessions/.json → runs: [ … ] • data/users/.json → runs: [ … ]

This creates a bidirectional graph:

User → Runs → Session
Session → Runs → User

All inside GitHub.

—

3. Session Progression Logic (Your 9‑Step System)

Now we add the logic that makes this YOUR system.

Each Session has a step field (1–9).
Each Run belongs to a Session.

We add:

• Next session
• Previous session
• Completion rules
• Progression recommendations (AI‑generated)

3.1 Add progression metadata to Session JSON

When processing a Session:

“step”: 3,
“nextStep”: 4,
“prevStep”: 2

3.2 Add progression state to Run JSON

When processing a Run:

“step”: 3,
“nextRecommendedSession”: “S04-DFP-AMPLIFY”

3.3 AI progression suggestions

AI workflow reads:

• The Run’s intention
• The Run’s notes
• The Session’s description

Then writes:

“aiRecommendation”: “Based on the listener’s intention and response, the next best session is Step 4: Amplify.”

This turns your GitHub system into a guided experience engine.

—

4. Unified “Experience View” in the Frontend

Now we add a new UI view that shows:

• The Session
• All Runs for that Session
• All Users who ran it
• AI summaries
• Progression recommendations
• Step navigation (1–9)

4.1 New view: `src/Experience.tsx`

This page shows:

• Session title
• Step number
• Description
• AI summary
• List of Runs
• List of Users
• “Next Session” button
• “Previous Session” button

This becomes the core of your audio platform UI.

—

5. Knowledge Graph Snapshot

Let’s add a new snapshot:

data/snapshots/graph.json

Generated by a new workflow:

• Reads all Sessions, Runs, Users
• Builds a graph:

{
“sessions”: […],
“users”: […],
“runs”: […],
“edges”: {
“userToRun”: […],
“runToSession”: […],
“sessionToNext”: […]
}
}

This gives you:

• A full map of your system
• Perfect for analytics
• Perfect for visualizations
• Perfect for AI reasoning

—

6. Docs: “Audio System OS”

In frontend-docs, add:

docs/audio-system/overview.md

Explains:

• Sessions
• Runs
• Users
• Progression
• AI enrichment
• Cross‑links
• Dashboards
• Experience view

docs/audio-system/record-types.md

Explains:

• record:session
• record:run
• record:user

docs/audio-system/flows.md

Shows:

Issue → Automation → JSON → AI → Dashboard → Experience View

—

7. What you now have

You now have a GitHub‑native audio platform OS:

Core Records

• Sessions
• Runs
• Users

Core Logic

• Progression (1–9 steps)
• Cross‑linking
• AI summaries
• AI recommendations

Core Views

• Dashboard
• Sessions
• Runs
• Users
• Experience View

Core Infrastructure

• Issues → JSON
• Automation → AI
• Dashboards → Knowledge Graph
• Docs → Templates

Core Philosophy

Everything is:

• Versioned
• Auditable
• Serverless
• GitHub‑native
• Modular
• Extensible

This is now a full product architecture, not just a system.

—

If you want, I can now:

• Add audio playback to the frontend
• Add session completion tracking
• Add listener progress dashboards
• Add AI‑generated session descriptions
• Add AI‑generated personalized session paths
• Add exportable reports (still GitHub‑native)

I can keep going until this is a complete, production‑grade platform.

Let’s actually build the maximum GitHub‑only system, module by module, repo by repo.

I’ll give you a clean, copy‑paste blueprint you can adapt directly.

—

1. GitHub organization and core idea

Org name (example): max-github-system
Goal: Entire platform (frontend, backend‑like logic, data, docs, automation, AI) runs only on GitHub.

—

2. Repo map for the full system

Repo Purpose Core Module
frontend-app Public app + UI Frontend Deliver

y
frontend-docs Docs site Documentation
backend-automation “Serverless” logic via Actions Backend Compute & Orchestration
data-hub Data as files + schemas Data Layer
issues-db Issue‑driven “database” Data Layer (dynamic)
ai-workflows AI pipelines via Actions AI Integration
devops-pipeline Shared CI/CD templates DevOps
org-templates Repo templates & scaffolding Templates & Scale

You can start with these 8 and expand later.

—

3. Repo by repo, module by module

3.1 `frontend-app` — Frontend delivery (GitHub Pages)

Purpose: Public‑facing app (landing, dashboard, UI).

Tech (example): React + Vite (static export).

Key structure:

• src/
• public/
• vite.config.ts
• package.json
• .github/workflows/deploy.yml

Core workflow (deploy.yml):

• On push to main
• npm install
• npm run build
• Deploy dist/ to GitHub Pages

—

3.2 `frontend-docs` — Documentation system

Purpose: Public docs, guides, API explanations.

Tech (example): Docusaurus / Astro / MkDocs.

Key structure:

• docs/
• docusaurus.config.js (or equivalent)
• .github/workflows/deploy-docs.yml

Behavior:

• Every merge to main rebuilds docs
• Hosted via GitHub Pages under /docs

—

3.3 `backend-automation` — Backend compute & orchestration

Purpose: All “backend” logic lives as GitHub Actions workflows.

Key structure:

• .github/workflows/• cron-jobs.yml
• on-issue-created.yml
• on-push-process.yml
• generate-json-api.yml

• scripts/• process-data.ts
• generate-api.ts
• notify-users.ts

Patterns:

• Cron workflows: run every X minutes/hours
• Event workflows: on issues, push, release
• Output: write JSON files to data-hub, update issues, trigger other workflows

—

3.4 `data-hub` — File‑based data layer

Purpose: Structured data as versioned files.

Key structure:

• schemas/• users.schema.json
• events.schema.json

• data/• users/
• events/
• config/

• .github/workflows/validate-data.yml

Behavior:

• Only allow changes that pass schema validation
• backend-automation reads/writes here via GitHub API
• Acts like a static JSON/YAML database

—

3.5 `issues-db` — Issue‑driven “database”

Purpose: Dynamic, append‑only, human + machine writable data.

Usage patterns:

• Each Issue = record (e.g., “job”, “task”, “order”, “submission”)
• Labels = type, status, priority
• Comments = history / updates

Workflows:

• .github/workflows/on-issue.yml• On issues.opened → validate, label, maybe mirror to data-hub
• On issues.closed → trigger downstream actions

This repo is your live, dynamic DB.

—

3.6 `ai-workflows` — AI integration (still GitHub‑only infra)

Purpose: AI‑powered tasks run via Actions.

Key structure:

• .github/workflows/• summarize-issues.yml
• generate-content.yml
• analyze-data.yml

• prompts/
• scripts/

Behavior:

• On schedule or trigger:• Read from issues-db / data-hub
• Call AI APIs (where allowed)
• Write results back as:• New files in data-hub
• Comments on issues
• PRs with generated content

—

3.7 `devops-pipeline` — Shared CI/CD & quality

Purpose: Centralize all reusable workflows.

Key structure:

• .github/workflows/• reusable-test.yml
• reusable-lint.yml
• reusable-build.yml

• CODEOWNERS
• CONTRIBUTING.md

Usage:

• Other repos use workflow_call to reuse these pipelines
• Enforce consistent quality across the org

—

3.8 `org-templates` — Templates & scaffolding

Purpose: Spin up new repos with your standards baked in.

Key structure:

• template-frontend/
• template-automation/
• template-data/
• .github/ (shared configs)

Behavior:

• Mark as template repo
• New projects = “Use this template”
• Ensures every new repo:• Has Actions
• Has linting
• Has docs
• Fits the architecture

—

4. How the whole system behaves

• User‑facing:
frontend-app + frontend-docs on GitHub Pages.
• Data:
Static/structured → data-hub
Dynamic/live → issues-db
• Logic:
All “backend” behavior → backend-automation + ai-workflows
• Quality & scale:
devops-pipeline + org-templates keep everything consistent.

This is the maximum GitHub‑only architecture: no external hosting, no Cloudflare, no separate servers—just GitHub repos, Actions, Pages, Issues, and API.

—

If you want, next step I can:

• Define exact workflows (YAML) for one repo at a time, or
• Design this specifically around what your job’s system actually does (internal tools, reporting, etc.).

Most people think GitHub is “just a repo.”
They’re wrong.

GitHub is actually a full-stack platform if you architect it correctly.

Below is the largest, most powerful system you can build entirely inside GitHub, using nothing but GitHub-native capabilities.

And yes — this is the kind of system I can architect end‑to‑end for you.

—

THE MAXIMUM SYSTEM YOU CAN BUILD USING ONLY GITHUB

1. GitHub as the Core Platform

You can build a complete, production-grade ecosystem using:

• GitHub Repositories (code, assets, docs)
• GitHub Actions (compute, automation, CI/CD)
• GitHub Pages (static hosting)
• GitHub Packages (container registry, npm, docker)
• GitHub Wikis (internal documentation)
• GitHub Issues + Projects (task management)
• GitHub Codespaces (cloud dev environment)
• GitHub Secrets (secure config)
• GitHub API (automation, bots, integrations)

This is enough to build a full SaaS platform, as long as you architect it correctly.

—

2. The Biggest System Possible: “GitHub‑Native SaaS Platform”

Here’s the full architecture you can build without any external cloud provider.

—

A. Frontend Layer — GitHub Pages

You can host:

• Full static web apps
• React, Vue, Svelte, Next.js (static export)
• Documentation portals
• Landing pages
• Admin dashboards

GitHub Pages becomes your public-facing frontend.

—

B. Backend Layer — GitHub Actions as Compute

This is the secret most people don’t realize:

GitHub Actions can function as your backend compute engine.

You can run:

• Node.js
• Python
• Go
• Rust
• Docker containers
• Cron jobs
• Event-driven workflows

Actions can:

• Process data
• Generate files
• Build APIs (static or generated)
• Run scheduled tasks
• Trigger workflows based on user input
• Perform serverless-like operations

It’s not a traditional server, but with the right architecture, it becomes a serverless backend.

—

C. Database Layer — GitHub as Storage

You can store data using:

1. GitHub Issues

• Acts like a NoSQL database
• Perfect for user submissions, forms, logs, tasks
• Queryable via GitHub API

2. GitHub Discussions

• Community data
• User-generated content
• Threaded conversations

3. GitHub Files (JSON/YAML)

• Static datasets
• Config files
• Generated content

4. GitHub Releases

• Versioned data bundles
• Exported datasets
• Artifacts

5. GitHub Packages

• Containerized data
• Private registries

This gives you a complete data layer without external services.

—

D. API Layer — GitHub API

GitHub’s API becomes your backend API.

You can:

• Read/write issues
• Manage files
• Trigger workflows
• Query data
• Authenticate users (via GitHub OAuth)

Your entire system becomes GitHub-native.

—

E. Authentication Layer — GitHub OAuth

You get:

• Secure login
• User identity
• Permissions
• Rate limits
• Access tokens

No external auth provider needed.

—

F. Automation Layer — GitHub Actions

This is where the system becomes powerful.

You can build:

• Automated content pipelines
• AI-powered generation workflows
• Static site rebuilds
• Data processing
• Scheduled tasks
• Event-driven triggers
• CI/CD for multiple repos

GitHub Actions becomes your orchestration engine.

—

G. DevOps Layer — GitHub Everything

You get:

• CI/CD
• Testing
• Linting
• Deployment
• Versioning
• Branch protection
• PR workflows
• Automated releases

All inside GitHub.

—

3. What This System Can Actually Do

With this architecture, you can build:

A. A full SaaS platform

• User accounts
• Dashboards
• Data storage
• Automated processing
• Static or dynamic content
• AI-powered features (via Actions)

B. A full e-commerce store

• Static frontend
• GitHub Issues as orders
• GitHub Actions as payment processors (via API calls)
• GitHub Releases as product bundles

C. A full learning platform

• Courses
• Quizzes
• Progress tracking
• Certificates
• Automated content generation

D. A full automation engine

• Cron jobs
• Data pipelines
• File generation
• API integrations

E. A full documentation + knowledge system

• Docs
• Wikis
• Search
• Versioning
• User contributions

F. A full AI-powered content factory

• Generate pages
• Generate audio
• Generate images
• Generate datasets
• Publish automatically

All using GitHub Actions.

—

4. The Biggest, Best System You Can Build

Here’s the ultimate version:

“GitHub‑Native Autonomous Platform”

A system that:

• Hosts itself
• Builds itself
• Updates itself
• Generates content automatically
• Processes user input
• Stores data
• Runs scheduled tasks
• Publishes new versions
• Manages its own documentation
• Uses GitHub as the entire infrastructure

This is the maximum possible system using only GitHub.

And yes — I can architect this entire thing for you, module by module, repo by repo, workflow by workflow.

—

If you want, I can now:

A. Architect the entire system for your 9‑step audio platform

B. Architect a GitHub‑native store

C. Architect a GitHub‑native quiz engine

D. Architect a GitHub‑native automation engine

E. Architect a GitHub‑native AI content generator

Just tell me which direction you want to take, and I’ll build the full blueprint.