OpenQode/docs/GOOSE_SUPER_ANALYSIS.md

Goose Super - Current State Analysis & Enhancement Plan

Executive Summary

Goose Super is currently a functional Electron-based AI coding assistant that combines Qwen LLM with basic computer automation. However, to achieve the vision of a noob-proof, all-in-one AI coding environment (like Lovable but with full computer control), significant enhancements are needed.

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Current State Assessment

✅ What Works Today

Feature	Status	File
Qwen LLM Integration	✅ Working	bin/qwen-bridge.mjs, bin/goose-launch.mjs
Electron GUI	✅ Working	bin/goose-electron-app/main.cjs
Chat Interface	✅ Working	bin/goose-electron-app/renderer.js (1970 lines)
Screenshots	✅ Working	computer-use.cjs - PowerShell capture
Mouse Click	✅ Working	PowerShell mouse_event simulation
Keyboard Input	✅ Working	SendKeys via PowerShell
Key Combinations	✅ Working	Ctrl+C, Alt+Tab, etc.
Shell Commands	✅ Working	exec() with timeout
Window Listing	✅ Working	Get-Process filtering
Window Focus	✅ Working	SetForegroundWindow via WinAPI
App Opening	✅ Working	Common apps mapped
Preview Panel	✅ Working	Webview for HTML preview
Playwright Bridge	⚠️ Basic	playwright-bridge.js - navigate/click/type
AI Suggestions	✅ Working	Pre-defined prompt cards
Terminal Panel	✅ Working	Command execution UI

❌ What's Missing (vs. Goal)

Gap	Impact	Priority
No Vision/OCR Element Finding	Can't "see" and click buttons by name	🔴 CRITICAL
No Self-Correction Loop	Doesn't verify if actions worked	🔴 CRITICAL
No Vibe Coding Flow	Can't create/preview apps like Lovable	🔴 CRITICAL
No Project/File Management	No file tree, save/load projects	🟠 HIGH
No Embedded IDE	No Monaco editor, syntax highlighting	🟠 HIGH
No Server/SSH Management	Can't deploy/manage remote servers	🟡 MEDIUM
No Git Integration	Can't commit/push/pull	🟡 MEDIUM
Browser Automation is Surface-Level	No DOM inspection, smart selectors	🟡 MEDIUM
No Memory/Context Persistence	Forgets between sessions	🟡 MEDIUM

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Reference Implementations Deep-Dive

1. Windows-Use (CursorTouch)

Best for: Desktop automation without computer vision

windows_use/
├── agent/        # Agent orchestration
├── llms/         # LLM providers (Ollama, Google, etc.)
├── messages/     # Conversation handling
├── tool/         # Tool definitions
└── telemetry/    # Analytics

Key Innovations:

• Uses UIAutomation (Windows Accessibility API) to find elements by name/role
• PyAutoGUI for mouse/keyboard (more reliable than raw SendKeys)
• Works with any LLM (Qwen, Gemini, Ollama) - not tied to specific models
• Grounding - Shows how it "sees" the screen with labeled elements

What to Take:

• UIAutomation for element discovery (instead of blind x,y clicking)
• Agent loop pattern with tool execution
• LLM abstraction layer

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2. Browser-Use

Best for: Comprehensive web automation

browser_use/
├── actor/        # Action execution
├── agent/        # Agent service
├── browser/      # Playwright wrapper
├── code_use/     # Code execution sandbox
├── controller/   # Action controller
├── dom/          # DOM manipulation & analysis
├── filesystem/   # File operations
├── llm/          # LLM integrations
├── mcp/          # Model Context Protocol
├── sandbox/      # Safe execution environment
├── skills/       # Reusable action patterns
└── tools/        # Custom tool definitions

Key Innovations:

• Smart DOM analysis - extracts meaningful selectors
• Multi-tab support with session persistence
• Custom tools API - @tools.action(description='...')
• Sandbox execution for safe code running
• Cloud deployment option
• Form filling with validation
• CAPTCHA handling (via stealth browsers)

What to Take:

• DOM extraction and smart selector logic
• Tools/actions decorator pattern
• Multi-tab browser session management
• Sandbox for safe code execution

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3. Open-Interface

Best for: Simple LLM → Screenshot → Execute loop

app/
├── core.py       # Main orchestration loop
├── interpreter.py # Parse LLM responses
├── llm.py        # LLM communication
├── ui.py         # Tkinter UI (18KB)
└── utils/        # Helpers

Architecture:

User Request → Screenshot → LLM → Parse Instructions → Execute → Repeat

Key Innovations:

• Course-correction via screenshot feedback loop
• Stop button + corner detection to interrupt
• Simple, understandable architecture
• Works across Windows/Mac/Linux

What to Take:

• The "screenshot → analyze → execute → verify" loop
• Interrupt mechanisms (corner detection)
• Cross-platform automation patterns

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4. OpenCode TUI (sst/opencode)

Best for: Terminal-based IDE experience

packages/
├── core/         # Core logic
├── tui/          # Terminal UI (Ink-based)
├── lsp/          # Language Server Protocol
└── ...

Key Innovations:

• Uses Bun for speed
• LSP integration for code intelligence
• SST infrastructure for deployment
• Beautiful TUI with Ink

What to Take:

• LSP integration for code completion/diagnostics
• Bun for faster package management
• TUI patterns (if we add TUI mode)

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5. Mini-Agent (MiniMax)

Best for: Lightweight Python agent framework

mini_agent/
├── agent.py      # Agent implementation
├── tools.py      # Tool definitions
└── memory.py     # Context management

What to Take:

• Memory/context management patterns
• Simple agent abstraction

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Gap Analysis: Current State vs. Noob-Proof Vision

🎯 User Experience Goals

Goal	Current	Required
"Build me a website"	❌ Can chat, can't create	✅ One prompt → working preview
"Click the Settings button"	⚠️ Blind x,y click	✅ Find element by name/OCR
"Deploy this to my server"	❌ No SSH	✅ Connect, upload, run commands
"Open my last project"	❌ No persistence	✅ Project save/load
"Edit this file"	❌ No editor	✅ Monaco with syntax highlighting
"Show me what you see"	⚠️ Can screenshot	✅ Annotated vision with element labels

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Proposed Architecture

Layered Super-Powers

┌─────────────────────────────────────────────────────────────┐
│                     GOOSE SUPER UI                          │
│  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌────────┐ │
│  │  Chat   │ │ Preview │ │  Editor │ │ Browser │ │Terminal│ │
│  │  Panel  │ │  Panel  │ │  Panel  │ │  Panel  │ │ Panel  │ │
│  └─────────┴─┴─────────┴─┴─────────┴─┴─────────┴─┴────────┘ │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        ▼                     ▼                     ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│  AI BRAIN     │    │  EXECUTION    │    │  CONTEXT      │
│               │    │  LAYER        │    │  LAYER        │
│ • Qwen Bridge │    │ • Computer Use│    │ • Memory      │
│ • Planning    │    │ • Browser Use │    │ • Projects    │
│ • Verification│    │ • Server Mgmt │    │ • Sessions    │
│ • Correction  │    │ • File Ops    │    │ • History     │
└───────────────┘    └───────────────┘    └───────────────┘

Phase-by-Phase Enhancement

Phase 1: Vision & Smart Automation

Make the AI truly "see" and interact reliably

1. Add UIAutomation element discovery (from Windows-Use)

   • Find buttons/inputs by name, not x,y
   • Label screenshot with element overlays

2. Implement verification loop (from Open-Interface)

   • After each action, screenshot and verify success
   • Self-correct if needed

3. Enhanced computer-use.cjs

   • Add findElement(name) using UIAutomation
   • Add getElementsOnScreen() for element listing
   • Add clickElement(name) for reliable interaction

Phase 2: Vibe Coding Experience

Create apps from prompts like Lovable

1. Embedded Monaco Editor

   • File tree sidebar
   • Multi-tab editing
   • Syntax highlighting
   • Live error detection

2. Project System

   • Create/save/load projects
   • Auto-scaffold HTML/CSS/JS
   • Template library

3. Live Preview Enhancement

   • Hot reload on file save
   • Dev server auto-start (Vite integration)
   • Console output in UI

Phase 3: Full Automation Power

Control everything

1. Server Management

   • SSH connection panel
   • Remote command execution
   • Log streaming
   • File upload/download

2. Browser Automation

   • DOM inspection
   • Smart element selectors
   • Multi-tab support
   • Cookie/auth persistence

3. Git Integration

   • Clone/commit/push
   • Branch management
   • Diff visualization

Phase 4: Noob-Proof Polish

Make it intuitive for anyone

1. Onboarding Wizard

   • API key setup
   • Permissions check
   • Quick tutorial

2. AI Suggestions

   • Context-aware suggestions
   • One-click actions
   • Visual tutorials

3. Error Recovery

   • Smart retry
   • User-friendly error messages
   • Undo/redo history

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Technical Debt to Address

Issue	Risk	Fix
PowerShell scripts for automation	Slow, fragile	Use native Node.js (robotjs/nut.js)
1970-line renderer.js	Hard to maintain	Modularize into components
No TypeScript	Type errors	Migrate to TypeScript
No tests	Regressions	Add jest/playwright tests
Hardcoded paths	Portability	Use config files

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Recommended Priorities

🔴 Immediate (Week 1)

1. Add UIAutomation element discovery
2. Implement screenshot → verify loop
3. Fix any existing bugs in computer-use

🟠 Short-term (Week 2-3)

4. Monaco Editor integration
5. Project save/load system
6. Enhanced preview with hot reload

🟡 Medium-term (Week 4-6)

7. SSH/Server management panel
8. Git integration
9. Browser DOM inspection

🟢 Long-term (Week 7+)

10. Onboarding wizard
11. AI-driven auto-correction
12. Multi-agent support

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Questions for User

1. Which LLMs to support? Currently Qwen only - add OpenAI/Claude/Ollama?
2. Deployment target? Windows only or also Mac/Linux?
3. Cloud features? Should Goose have cloud sync/remote execution?
4. Monetization? Any commercial plans affecting architecture?
5. Performance priority? Speed vs. reliability trade-off?

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Summary

Goose Super has a solid foundation but needs these critical additions to become "noob-proof":

1. Vision - UIAutomation element discovery (not blind clicking)
2. Verification - Screenshot → analyze → correct loop
3. IDE - Monaco editor with project management
4. Server - SSH/deployment capabilities
5. Polish - Onboarding, error handling, undo/redo

The reference repos provide excellent patterns to adopt. Windows-Use gives us UIAutomation. Browser-Use gives us smart DOM handling. Open-Interface gives us the verification loop. OpenCode gives us TUI patterns.

Next step recommendation: Start with Phase 1 (Vision & Smart Automation) as it unblocks all other "noob-proof" features.