Godogen: Claude Code Skills for Complete Godot Game Generation

Godogen is a pipeline that takes a text prompt and generates complete, playable Godot 4 projects. It uses Claude Code skills to design architecture, generate 2D/3D assets, write GDScript, and test visually.
Engineering Solutions
The developer solved three specific bottlenecks:
- Training Data Scarcity: LLMs have minimal GDScript training. Godogen includes a custom reference system with a hand-written language spec, full API docs converted from Godot's XML source, and a quirks database for undocumented engine behaviors. The agent lazy-loads only needed APIs at runtime to avoid context window overflow from Godot's ~850 classes.
- Build-Time vs Runtime State: Scenes are generated by headless scripts that build node graphs in memory and serialize to .tscn files, avoiding fragility of hand-editing Godot's serialization format. The model is taught which APIs are available at each phase and that every node needs its owner set correctly.
- Evaluation Loop: A separate Gemini Flash agent acts as visual QA, analyzing rendered screenshots from the running engine to catch visual bugs like z-fighting, floating objects, physics explosions, and grid-like placements that should be organic.
Architecture and Setup
The system runs as two Claude Code skills: an orchestrator that plans the pipeline, and a task executor that implements each piece in a context: fork window to prevent mistakes and state accumulation.
Prerequisites:
- Godot 4 (headless or editor) on PATH
- Claude Code installed
- API keys as environment variables:
GOOGLE_API_KEYfor Gemini (image generation and visual QA),TRIPO3D_API_KEYfor Tripo3D (image-to-3D model conversion, only for 3D games) - Python 3 with pip
Getting Started:
Run ./publish.sh ~/my-game to create a new project folder with all skills installed. This creates the target directory with .claude/skills/ and a CLAUDE.md, then initializes a git repo. Open Claude Code in that folder and describe what game to make — the /godogen skill handles everything.
Performance Notes:
- A single generation run can take several hours
- Running on a cloud VM with a T4 or L4 GPU works well for Godot's screenshot capture
- Claude Code with Opus 4.6 delivers the best outcome; Sonnet 4.6 works but requires more user guidance
- OpenCode is a viable alternative and porting the skills is straightforward
Asset Generation: Gemini creates 2D art and textures; Tripo3D converts selected images to 3D models. The system is budget-aware, maximizing visual impact per cent spent.
Tested Environment: Ubuntu and Debian. macOS is untested — screenshot capture depends on X11/xvfb/Vulkan and needs a native capture path.
📖 Read the full source: HN AI Agents
👀 See Also

QCAI App Provides Mobile Control Center for OpenClaw Ecosystem
Academic research team releases QCAI app for iOS and Android, built with AI-assisted development, offering dashboard monitoring, gateway chat, and secure VPN access to OpenClaw tools.

Mastering Antropic Subscription Modes: Haiku, Sonnet, and Opus
Explore Antropic's innovative subscription modes—Haiku, Sonnet, and Opus—designed to enhance your AI coding experience with tailored features and pricing.

MegaClaw: Containerized OpenClaw Setup with Playwright and Homebrew
MegaClaw is a two-image Podman setup for OpenClaw that addresses common installation issues like permission errors and missing dependencies. It uses a multi-stage build with pre-installed Playwright and Homebrew, and bakes user configuration into a runtime image.

Qwen 3.6 27B with MTP on V100 32GB: 54 t/s via llama.cpp Branch
am17an's MTP branch of llama.cc runs Qwen 3.6 27B at 54 t/s on V100 32GB via PCIe adapter, dropping to 29-30 t/s without MTP.