Quick-Question Plugin Automates Unity Development with Claude Code
A developer has released quick-question, a plugin that automates Unity development workflows when using Claude Code. The tool handles compilation, testing, and implements a cross-model code review system to reduce manual intervention.
Key Features
The plugin provides several automation features:
- Auto-compile on edit: Triggers compilation every time Claude edits a .cs file. It first tries the in-editor HTTP server, then falls back to batch mode.
- Test pipeline: The
/qq:testcommand runs both EditMode and PlayMode tests while checking for runtime errors. - Cross-model code review:
/qq:codex-code-reviewsends diffs to Codex for review, then Claude spawns subagents to independently verify each finding against the actual source code. Only confirmed issues get fixed, with a maximum of 5 review rounds. - 20 slash commands: Includes commands for testing, committing, reviewing, explaining code, and dependency analysis.
Architecture Details
The cross-model review system uses what the developer calls the "Tribunal" pattern. Instead of trusting one model's opinion, two models review each other's work with verification: Codex finds potential issues, Claude checks if they're real problems in the actual source code, and an over-engineering filter prevents unnecessarily complex fixes.
The plugin includes tykit, an HTTP server that auto-starts inside the Unity Editor. This allows Claude to control Play Mode, read console logs, run tests, and inspect GameObjects via HTTP requests.
Technical Requirements
- macOS only (v1 limitation)
- Unity 2021.3+
- MIT licensed
- Available on GitHub at https://github.com/tykisgod/quick-question
The developer notes they built this to address the tedium of manually checking compilation, running tests, and reviewing code during Unity game development with Claude Code.
📖 Read the full source: r/ClaudeAI
👀 See Also
Benchmarking Nemotron 3 Super 120B with 1M token context on M1 Ultra
A user tested Nemotron 3 Super 120B with a Q4_K_M quantized model using llama.cpp on an M1 Ultra, achieving a 1 million token context window that consumed approximately 90GB of VRAM. Performance benchmarks show token generation speeds ranging from 255 t/s at 512 prompt processing down to 22.37 t/s at 100,000 token context.
WebMCP browser APIs could reduce web scraping needs for AI agents
Google's WebMCP introduces browser APIs that let websites register tools for AI agents to call directly, potentially eliminating much of the DOM scraping and anti-bot workarounds developers currently build.
ClearSpec: A Spec Generator to Reduce Hallucination in Claude Code
ClearSpec is a tool that generates structured specifications from plain English descriptions, connecting to GitHub repos to reference real file paths and dependencies, then uses those specs as prompts for Claude Code to provide better context.
Tendr Skill: Deterministic CLI Operations for Agent Memory Management
Tendr Skill is an Agent Skill that separates reasoning from execution for structured long-term memory, allowing agents to decide what needs changing while a CLI tool handles structural operations deterministically. It supports [[wikilinks]] and explicit semantic hierarchies across files.