Gin is a Go workable choice for regulated industry. GreatCTO auto-detects both — adds the regulated archetype overlay, wires regulated-specific gates, and runs 83 specialist agents around your existing Gin workflow.
GreatCTO reads your go.mod and detects gin + regulated archetype from signals: imports, file structure, env vars, README hints.
Attaches the regulated archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for Gin-style projects.
qa-engineer runs go vet / staticcheck / go test -race -cover; security-officer reviews context cancellation + goroutine leaks; performance-engineer profiles pprof CPU + heap.
Bugs you've hit before in other Gin 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-gin-app && npx great-cto init ✓ scanning manifests… found manifest ✓ stack: gin (Go) ✓ archetype: regulated ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add regulated feature" ▸ architect drafting ARCH-regulated.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 "regulated feature" — auth first, schema validation, and the audit line the regulated reviewer requires before gate:ship opens.
// internal/handlers/regulated.go — drafted by senior-dev, reviewed by 5 agents
func CreateRegulated(c *gin.Context) {
user := middleware.RequireUser(c) // security-officer: auth before handler
var req RegulatedRequest
if err := c.ShouldBindJSON(&req); err != nil { // qa-engineer: binding validation
c.JSON(400, gin.H{"error": err.Error()}); return
}
result, err := service.Handle(c, req, user)
audit.Log(c, user.ID, "regulated feature", result.Confidence) // gate:regulated: every decision logged
c.JSON(200, result)
}regulated overlay.SOX-scoped systems with ITGC change management.
DORA / NIS2-covered services with ICT risk evidence.
ISO 27001 environments with SoA gap tracking.
Gin (Go) is not a typical fit for regulated industry. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the regulated 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.