Gin is a Go workable choice for game. GreatCTO auto-detects both — adds the game archetype overlay, wires game-specific gates, and runs 83 specialist agents around your existing Gin workflow.
GreatCTO reads your go.mod and detects gin + game archetype from signals: imports, file structure, env vars, README hints.
Attaches the game 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: game ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add game feature" ▸ architect drafting ARCH-game.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 "game feature" — auth first, schema validation, and the audit line the game reviewer requires before gate:ship opens.
// internal/handlers/game.go — drafted by senior-dev, reviewed by 5 agents
func CreateGame(c *gin.Context) {
user := middleware.RequireUser(c) // security-officer: auth before handler
var req GameRequest
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, "game feature", result.Confidence) // gate:game: every decision logged
c.JSON(200, result)
}game overlay.F2P games with IAP age gates and spending limits.
Games with loot-box odds disclosure (BE / NL / China).
Kids titles under COPPA with IARC rating alignment.
Gin (Go) is not a typical fit for game. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the game 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.