Django is a Python workable choice for game. GreatCTO auto-detects both — adds the game archetype overlay, wires game-specific gates, and runs 34 specialist agents around your existing Django workflow.
GreatCTO reads your pyproject.toml / requirements.txt and detects django + 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 Django-style projects.
qa-engineer runs mypy / ruff / pytest --cov; security-officer scans for SQL injection patterns common in ORMs (SQLAlchemy, Django ORM); performance-engineer profiles async patterns for I/O contention.
Bugs you've hit before in other Django 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-django-app && npx great-cto init ✓ scanning manifests… found pyproject.toml ✓ stack: django (Python) ✓ archetype: game ⚠ archetype + stack combo is unusual — review overlay manually ✓ 34 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.
Django (Python) 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. Works in Claude Code, Cursor, OpenAI Codex CLI, Aider, and Continue.