Ruby on Rails is a Ruby workable choice for healthcare. GreatCTO auto-detects both — adds the healthcare archetype overlay, wires healthcare-specific gates, and runs 83 specialist agents around your existing Ruby on Rails workflow.
GreatCTO reads your Gemfile and detects rails + healthcare archetype from signals: imports, file structure, env vars, README hints.
Attaches the healthcare archetype overlay: HIPAA gates, BAA tracking, PHI encryption review, 21 CFR Part 11 audit-trail. Override if your specifics differ; the defaults are sensible for Ruby on Rails-style projects.
qa-engineer runs rubocop / rspec / brakeman; security-officer flags mass assignment + N+1 ORM queries; performance-engineer checks ActiveRecord query hot paths.
Bugs you've hit before in other Ruby on Rails projects (connection-pool exhaustion, ORM N+1 queries, retry storms) — the agent's Step 0 includes the prior detection order. MTTR drops 94 % on second occurrence (methodology).
$ cd my-rails-app && npx great-cto init ✓ scanning manifests… found manifest ✓ stack: rails (Ruby) ✓ archetype: healthcare ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add HL7 patient resource endpoint" ▸ architect drafting ARCH-healthcare.md… ▸ pm decomposing into beads tasks… ⚐ gate:plan — your approval needed
Approve → 3 senior-devs run in parallel worktrees → 5 reviewers fan out in parallel → gate:ship → deploy. One real run walked stage-by-stage: /proof.
This is the shape of what senior-dev drafts for "HL7 patient resource endpoint" — auth first, schema validation, and the audit line the healthcare reviewer requires before gate:ship opens.
# app/controllers/healthcare_controller.rb — reviewed by 5 agents
class HealthcareController < ApplicationController
before_action :authenticate_user! # security-officer: auth before handler
def create
result = HealthcareService.call(permitted_params, current_user)
AuditLog.record(who: current_user.id, what: "HL7 patient resource endpoint",
confidence: result.confidence) # gate:healthcare: every decision logged
render json: result
end
endhealthcare overlay.Patient-facing portals handling PHI under HIPAA.
FHIR / HL7 integration services for EHR data.
Clinical-workflow tools with 21 CFR Part 11 audit trails.
Ruby on Rails (Ruby) is not a typical fit for healthcare. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the healthcare archetype page for the typical stack list and decide if your case is the right tool / right archetype.
No black-box "AI does it all" loop. GreatCTO is a deterministic state machine — 8 stages, 22 nodes, 2 human gates. Every node maps to a real agent on GitHub. Inspect the state machine →
$ npx great-cto init
Free, MIT, runs locally. Built as a Claude Code plugin — install with one command.
Eight stages, two human gates, four memory layers. Why this exact shape, and what I tried that didn't work.
One run, one feature, from prompt to merged PR. Time, cost, and gate-by-gate breakdown — no marketing math.
Regex vs LLM-based archetype detection, the false-positive count, and why I keep rejecting the obvious fix.
The bottleneck in agentic SDLC isn't model quality — it's process governance. Here's the state machine that closes the gap.