Repeat pattern for frontend-docs (use Docusaurus/MkDocs/Astro), data-hub (add schemas folder + validation workflow), etc.

Step 5: Wire Issues → JSON Sync (the Core Loop) In issues-db:

• Create issue templates (e.g. .github/ISSUE_TEMPLATE/session.yml, label record:session)

• Add dispatch workflow → backend-automation

In backend-automation:

• Create listener workflow (repository_dispatch event)

• Use Node + Octokit to parse issue → write JSON to data-hub/data/sessions/<number>.json

Step 6: Build Frontend Data Consumption In frontend-app scripts/fetchData.mjs (run at build time):

• Use Octokit to list files in data-hub/data/*

• Download + decode JSON → generate static data files

• Use in React components (e.g. Sessions.tsx, Dashboard.tsx)

Step 7: Add Automation & AI Layer In ai-workflows:

• Cron workflow → hourly

• Read issues / JSON → call AI API (if key in secrets) → write summaries back as comments or files

Step 8: Enforce Quality & Scale In devops-pipeline: reusable CI yaml

In every code repo: call it via uses: max-github-system/devops-pipeline/.github/workflows/reusable-node-ci.yml@main

Step 9: Document & Template In frontend-docs: explain flows

In org-templates: create template repos with boilerplate workflows + README

Realistic Outcome

You now have a versioned, auditable, mostly-static platform that can:

• Show dashboards of “records” (sessions, runs, tasks…)

• Run scheduled processing / AI enrichment

• Accept user input via GitHub Issues / Forms

• Auto-deploy UI & docs

But expect friction: rate limits, workflow minutes quota, no real-time, manual scaling pain.

If you want to specialize this (e.g. exactly your 9-step audio system, internal job tracker, content factory), give me the domain + key entities (sessions, runs, users, jobs…) and I’ll give the tightest copy-paste next layer: exact issue templates, dispatch rules, processor scripts, frontend views.