Update iso-bot skill for Go stack
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## Overview
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Screen-reading bot engine for isometric games. First implementation: Diablo II: Resurrected.
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**Approach:** Screen capture + computer vision + human-like input simulation. No memory injection, no hooking, no client modification.
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**Approach:** Screen capture + computer vision + human-like input simulation. No memory injection, no hooking, no client modification. Engine runs on host, game runs in VM for detection isolation.
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## Repository
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- **Local:** `/home/openclaw/.openclaw/workspace/projects/iso-bot`
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- **Remote:** `git@git.cloonar.com:openclawd/iso-bot.git` (pending repo creation)
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- **Remote:** `ssh://forgejo@git.cloonar.com/openclawd/iso-bot.git`
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## Tech Stack
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- **Engine:** Go 1.23+
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- **Vision:** GoCV (OpenCV bindings for Go)
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- **Screen capture:** Platform-native (Win32 API / X11)
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- **Input simulation:** Platform-native (SendInput / uinput)
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- **API:** net/http + gorilla/websocket (REST + WS)
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- **Dashboard:** React + TypeScript (planned)
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- **Config:** YAML
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- **Loot filter:** Declarative YAML rule engine
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## Architecture
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```
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engine/ # Reusable core — game-agnostic
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├── screen/ # Screenshot capture (mss), OCR, template matching
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├── input/ # Mouse (bezier curves), keyboard, humanization
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├── vision/ # Object detection, color analysis, UI element finding
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├── state/ # State machine, event bus
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├── navigation/ # Pathfinding (A*), click-to-move
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└── safety/ # Session timing, break scheduling, pattern randomization
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games/d2r/ # Diablo II: Resurrected implementation
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├── config.py # Screen regions, colors, timings (1920x1080)
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├── game.py # Main bot loop & orchestration
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├── screens/ # State detection (menu, in-game, inventory)
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├── routines/ # Farming routines (Mephisto, Pindle, Countess)
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└── templates/ # UI template images for matching
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ui/ # Web dashboard (FastAPI) — planned
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config/ # YAML configuration
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cmd/iso-bot/ Single binary entry point
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pkg/
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├── engine/
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│ ├── capture/ Screen capture (window, VM, full screen)
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│ ├── vision/ Template matching, color detection (GoCV)
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│ ├── input/ Mouse (Bézier curves), keyboard, humanization
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│ ├── state/ Game state machine with event callbacks
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│ ├── safety/ Session timing, breaks, pattern randomization
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│ ├── navigation/ A* pathfinding, click-to-move
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│ └── loot/ Declarative rule-based loot filter
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├── plugin/ Game plugin interface
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├── api/ REST + WebSocket API
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└── auth/ License/account validation
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plugins/d2r/ D2R game plugin
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web/ React dashboard (planned)
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```
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## Key Design Decisions
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1. **Screen reading only** — captures screenshots, analyzes pixels/templates, never touches game memory
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2. **Human-like input** — Bezier mouse curves, randomized delays, micro/long breaks, fatigue simulation
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3. **Reusable engine** — adding a new game = new `games/<name>/` directory implementing game-specific detection
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4. **Anti-detection** — session timing, route randomization, behavioral variation, configurable break schedules
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5. **Configuration-driven** — YAML configs for all tunable parameters
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## Tech Stack
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- Python 3.11+
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- OpenCV (template matching, color detection)
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- pytesseract (OCR)
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- mss (fast screenshot capture)
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- pyautogui/pynput (input simulation)
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- FastAPI (dashboard — planned)
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## Plugin System
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All game logic is behind interfaces in `pkg/plugin/plugin.go`:
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- `Plugin` — main entry point, returns detector/reader/routines
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- `GameDetector` — detect state from screenshots
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- `ScreenReader` — extract items, enemies, text
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- `Routine` — automated farming sequences (context-aware, cancellable)
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- `LootFilter` — item pickup rules
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- `EngineServices` — engine capabilities provided to plugins
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## Development Conventions
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- Type hints on all functions
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- Docstrings on all modules, classes, public methods
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- `black` formatting
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- Tests in `tests/`
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- Go standard project layout
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- Type hints / godoc on all exported types
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- `gofmt` formatting
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- Tests in `*_test.go` files alongside code
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- Feature branches → main
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- Always commit and push after changes
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## Git Workflow
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```bash
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cd /home/openclaw/.openclaw/workspace/projects/iso-bot
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git checkout -b feature/<name>
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# ... work ...
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git add -A && git commit -m "descriptive message"
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git checkout main && git merge feature/<name>
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git push origin main
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GIT_SSH_COMMAND="ssh -o StrictHostKeyChecking=no" git push origin main
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```
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## Current Status
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- ✅ Project structure created
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- ✅ Core engine modules with meaningful stubs
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- ✅ D2R config (screen regions, colors, timings)
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- ✅ D2R farming routines (Mephisto, Pindle, Countess) — stub phase
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- ⏳ Remote git repo (needs write-access token)
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- ⏳ Implement actual screen detection logic
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- ⏳ Implement input simulation
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- ⏳ Template image collection
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- ⏳ Web dashboard
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- ⏳ Testing
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- ✅ Go project structure
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- ✅ Plugin interface system (`pkg/plugin`)
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- ✅ Engine: capture, vision, input/humanize, state, safety, loot filter
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- ✅ API server skeleton (REST endpoints)
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- ✅ D2R plugin: config, detector, reader, Mephisto routine
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- ✅ Declarative loot filter with YAML rules
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- ⏳ Platform-specific capture backends (Win32, X11)
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- ⏳ GoCV integration for actual vision processing
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- ⏳ Platform-specific input backends
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- ⏳ Remaining D2R routines (Pindle, Countess)
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- ⏳ Web dashboard (React)
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- ⏳ Account/license system
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- ⏳ Multi-instance support
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- ⏳ Tests
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## Next Steps (Priority Order)
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1. Get remote repo set up and push
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2. Implement `engine/screen/capture.py` — verified working on target machine
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3. Implement `engine/input/mouse.py` — Bezier curve mouse movement
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4. Implement `engine/input/keyboard.py` — human-like key presses
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5. D2R menu detection (character select, create game)
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6. D2R basic Mephisto run (teleport → kill → loot → exit)
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7. Loot detection and filtering
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8. Web dashboard for monitoring
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1. GoCV integration — make vision pipeline actually work
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2. Platform capture backends — Windows (BitBlt/DXGI) and Linux (X11)
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3. Platform input backends — Windows (SendInput) and Linux (uinput)
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4. D2R detector implementation — health orb reading, menu detection
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5. D2R Mephisto routine — complete implementation
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6. WebSocket real-time status streaming
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7. React dashboard
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8. Pindle + Countess routines
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9. Account system + licensing
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## Notes
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- Bot runs on a separate machine or VM from the game client
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## Key Design Decisions
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1. **Go over Python** — performance for real-time capture+vision at 30+ FPS
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2. **Plugin system** — engine is game-agnostic, new game = new plugin
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3. **VM isolation** — engine on host, game in VM, zero detection surface
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4. **Declarative loot** — YAML rules, user-customizable via web UI
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5. **Single binary** — engine + API in one Go binary, easy distribution
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6. **Human-like input** — Bézier curves, fatigue, breaks, route randomization
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## D2R Notes
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- Target resolution: 1920x1080
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- Primary farming character: Sorceress with Teleport
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- The user plays D2R (Necro "Baltasar", Summoner build) — knows the game well
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- Key routines: Mephisto (moat trick), Pindle (fastest), Countess (runes)
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- Screen regions and HSV colors defined in `plugins/d2r/config.go`
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- The user plays D2R actively and knows the game well
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