T3 Stack is a TypeScript workable choice for insurance / insurtech. GreatCTO auto-detects both — adds the insurance archetype overlay, wires insurance-specific gates, and runs 83 specialist agents around your existing T3 Stack workflow.
GreatCTO reads your package.json and detects t3-stack + insurance archetype from signals: imports, file structure, env vars, README hints.
Attaches the insurance archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for T3 Stack-style projects.
qa-engineer runs tsc --strict / eslint / vitest --coverage; security-officer checks for prototype pollution + XSS sinks; performance-engineer reviews bundle size + cold-start times.
Bugs you've hit before in other T3 Stack 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-t3-stack-app && npx great-cto init ✓ scanning manifests… found package.json ✓ stack: t3-stack (TypeScript) ✓ archetype: insurance ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add insurance feature" ▸ architect drafting ARCH-insurance.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 "insurance feature" — auth first, schema validation, and the audit line the insurance reviewer requires before gate:ship opens.
// src/server/api/routers/insurance.ts — reviewed by 5 agents
export const insuranceRouter = createTRPCRouter({
create: protectedProcedure // security-officer: auth before handler
.input(insuranceSchema) // qa-engineer: zod schema enforced
.mutation(async ({ ctx, input }) => {
const result = await handle(input, ctx.session.user);
await ctx.audit.log(ctx.session.user.id, 'insurance feature',
result.confidence); // gate:insurance: every decision logged
return result;
}),
});insurance overlay.Claims systems with NAIC model-act state matrices.
Pricing models audited for disparate impact.
Re-insurance reporting (bordereau) automation.
T3 Stack (TypeScript) is not a typical fit for insurance / insurtech. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the insurance 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.