⛓️ sveltekit × web3

Ship web3 / defi on SvelteKit with sensible defaults.

SvelteKit is a TypeScript workable choice for web3 / defi. GreatCTO auto-detects both — adds the web3 archetype overlay, wires web3-specific gates, and runs 83 specialist agents around your existing SvelteKit workflow.

What changes when GreatCTO joins your SvelteKit project

Detection → overlay → gates → reviewers.

1 · DETECT

Stack + archetype

GreatCTO reads your package.json and detects sveltekit + web3 archetype from signals: imports, file structure, env vars, README hints.

2 · OVERLAY

Archetype pack

Attaches the web3 archetype overlay: oracle review, MEV protection, upgradeability (UUPS/Diamond/Beacon) decision, multisig. Override if your specifics differ; the defaults are sensible for SvelteKit-style projects.

3 · GATES

SvelteKit-aware reviewers

qa-engineer runs tsc --strict / eslint / vitest --coverage; security-officer checks for prototype pollution + XSS sinks; performance-engineer reviews bundle size + cold-start times.

4 · MEMORY

Cross-project lessons

Bugs you've hit before in other SvelteKit 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).

First 10 minutes

Concrete walkthrough.

$ cd my-sveltekit-app && npx great-cto init
✓ scanning manifests… found package.json
✓ stack: sveltekit (TypeScript)
✓ archetype: web3
⚠ archetype + stack combo is unusual — review overlay manually
✓ 83 agents ready

$ /start "add multisig vault deposit handler"
▸ architect drafting ARCH-web3.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.

What ships

The first feature, SvelteKit-idiomatic.

This is the shape of what senior-dev drafts for "multisig vault deposit handler" — auth first, schema validation, and the audit line the web3 reviewer requires before gate:ship opens.

// src/routes/web3/+server.ts — drafted by senior-dev, reviewed by 5 agents
import { json } from '@sveltejs/kit';
import { requireUser } from '$lib/auth';      // security-officer: auth before handler
import { auditLog } from '$lib/audit';        // gate:web3: every decision logged

export async function POST({ 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: 'multisig vault deposit handler', confidence: result.confidence });
  return json(result);
}
Where this combo lands

What teams build with SvelteKit + the web3 overlay.

1

DeFi protocols with oracle strategy and MEV protection.

2

Token contracts with upgradeability decisions reviewed.

3

Custody flows with multisig and timelock enforcement.

⚠ Honest caveat

SvelteKit (TypeScript) is not a typical fit for web3 / defi. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the web3 archetype page for the typical stack list and decide if your case is the right tool / right archetype.

Architecture

Every step of the pipeline, transparent.

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 →

Install

SvelteKit + GreatCTO in one command.

$ npx great-cto init

Free, MIT, runs locally. Built as a Claude Code plugin — install with one command.

Related deep-dives

More from the blog

AI

How I designed the SDLC state machine for agentic coding

Eight stages, two human gates, four memory layers. Why this exact shape, and what I tried that didn't work.

AI

First real shipped feature with this stack — receipts

One run, one feature, from prompt to merged PR. Time, cost, and gate-by-gate breakdown — no marketing math.

AI

How GreatCTO chooses which compliance pack to attach

Regex vs LLM-based archetype detection, the false-positive count, and why I keep rejecting the obvious fix.

AI

Why your agent system fails: missing gates, not missing intelligence

The bottleneck in agentic SDLC isn't model quality — it's process governance. Here's the state machine that closes the gap.