Astro is a TypeScript workable choice for streaming / event-driven. GreatCTO auto-detects both — adds the streaming archetype overlay, wires streaming-specific gates, and runs 83 specialist agents around your existing Astro workflow.
GreatCTO reads your package.json and detects astro + streaming archetype from signals: imports, file structure, env vars, README hints.
Attaches the streaming archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for Astro-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 Astro 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-astro-app && npx great-cto init ✓ scanning manifests… found package.json ✓ stack: astro (TypeScript) ✓ archetype: streaming ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add streaming feature" ▸ architect drafting ARCH-streaming.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 "streaming feature" — auth first, schema validation, and the audit line the streaming reviewer requires before gate:ship opens.
// src/pages/api/streaming.ts — drafted by senior-dev, reviewed by 5 agents
import type { APIRoute } from 'astro';
import { requireUser } from '../../lib/auth'; // security-officer: auth before handler
import { auditLog } from '../../lib/audit'; // gate:streaming: every decision logged
export const POST: APIRoute = async ({ request, locals }) => {
const user = requireUser(locals);
const body = schema.parse(await request.json()); // qa-engineer: zod enforced
const result = await handle(body, user);
await auditLog({ who: user.id, what: 'streaming feature', confidence: result.confidence });
return new Response(JSON.stringify(result));
};streaming overlay.Event pipelines with exactly-once semantics.
CDC (Debezium) flows with schema-registry compat rules.
Real-time analytics with p99 latency budgets.
Astro (TypeScript) is not a typical fit for streaming / event-driven. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the streaming 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.