# Gui 360 Desktop Agent Dataset > Enable training and evaluation of desktop computer-using agents through 1.2M action steps across diverse Windows applications, covering GUI grounding, screen parsing, and action prediction with hybrid GUI+API action space reflecting modern agent designs. - Skill: `adu2021/gui-360-desktop-agent-dataset` (Agent Skill) - Install (CLI): `npx skillmds@latest add adu2021/gui-360-desktop-agent-dataset` - Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/gui-360-desktop-agent-dataset/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: adu2021 (https://skillmd.com/u/adu2021) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/adu2021/gui-360-desktop-agent-dataset --- # Title: Build Comprehensive Training Data for Desktop Autonomous Agents Desktop agents that autonomously complete computer tasks face unique challenges: grounding spatial coordinates, parsing complex UIs, predicting appropriate next actions. GUI-360° provides 1.2M executed action steps across thousands of real trajectories in Word, Excel, and PowerPoint, with full screenshots, accessibility metadata, task goals, reasoning traces, and both successful and failed action sequences. The dataset reveals that state-of-the-art models struggle with grounding and action prediction despite strong general capabilities. The benchmark spans three canonical tasks: GUI grounding, screen parsing, and action prediction. ## Core Concept **Comprehensive Desktop Agent Training and Evaluation**: - **Large-Scale Data**: 1.2M action steps, 13,750 trajectories across three Office applications - **Rich Annotations**: Screenshots + accessibility metadata + task descriptions + reasoning + failure cases - **Hybrid Action Space**: GUI actions (click, type, drag) + app-specific APIs (table insertion, cell editing) - **Three Evaluation Tasks**: Grounding (screen→coordinate), parsing (screen→UI elements), prediction (state→action) - **Real-World Complexity**: Diverse task types, variable UI layouts, complex tool interactions ## Architecture Overview - **Data Collection**: Instrumented Windows environment recording human demonstrations - **Annotation Pipeline**: Automatic extraction of actions, coordinates, UI elements via accessibility APIs - **Task Specification**: Goal descriptions with reasoning traces from human performers - **Corpus Composition**: 41% Word, 31.6% Excel, 27.4% PowerPoint - **Split Design**: Train/test maintaining consistent application distributions ## Implementation Steps **1. Design Data Collection Pipeline** Capture human demonstrations with full metadata. ```python class DesktopAgentDataCollector: def __init__(self, recording_dir="./gui_recordings"): self.recording_dir = recording_dir self.current_trajectory = None def start_recording(self, task_description): """Begin recording a new trajectory""" self.current_trajectory = { 'task': task_description, 'steps': [], 'screenshots': [], 'metadata': { 'app': None, 'start_time': datetime.now(), 'reasoning_notes': [] } } def capture_step(self, action_type, coordinates=None, text=None, keystroke=None): """Record a single action step""" # Capture screenshot before action screenshot = self.screenshot_full_screen() # Get accessibility tree a11y_tree = self.get_accessibility_tree() # Record action step = { 'action': { 'type': action_type, # 'click', 'type', 'drag', etc. 'coordinates': coordinates, 'text': text, 'keystroke': keystroke }, 'screenshot': screenshot, 'accessibility_tree': a11y_tree, 'timestamp': datetime.now() } self.current_trajectory['steps'].append(step) return len(self.current_trajectory['steps']) def end_recording(self, task_completed, final_notes=""): """Finalize trajectory recording""" self.current_trajectory['metadata']['success'] = task_completed self.current_trajectory['metadata']['notes'] = final_notes self.current_trajectory['metadata']['end_time'] = datetime.now() # Save to disk trajectory_id = self.save_trajectory(self.current_trajectory) return trajectory_id def screenshot_full_screen(self): """Capture current screen state""" import pyautogui screenshot = pyautogui.screenshot() return np.array(screenshot) def get_accessibility_tree(self): """Extract UI element structure using accessibility APIs""" import pywinauto app = pywinauto.GetFocusedWindow() # Build tree of accessible elements a11y_tree = self.build_tree(app) return a11y_tree def build_tree(self, element, max_depth=5, current_depth=0): """Recursively build accessibility tree""" if current_depth > max_depth: return None tree = { 'element': element.element_info.name, 'role': element.element_info.control_type, 'rectangle': element.rectangle().to_dict(), 'is_enabled': element.is_enabled(), 'is_visible': element.is_visible(), 'children': [] } # Get children if applicable try: for child in element.children(): child_tree = self.build_tree(child, max_depth, current_depth + 1) if child_tree: tree['children'].append(child_tree) except: pass return tree ``` **2. Implement Three Evaluation Tasks** Structure benchmark around distinct technical challenges. ```python class GUIAgentBenchmark: def __init__(self, dataset_path): self.trajectories = self.load_trajectories(dataset_path) def task1_gui_grounding(self, model): """Predict screen coordinates for target actions""" results = [] for trajectory in self.trajectories: for step in trajectory['steps']: # Input: screenshot + action description # Output: coordinate prediction screenshot = step['screenshot'] action_desc = step['action']['type'] if step['action']['type'] == 'click': true_coords = step['action']['coordinates'] # Model predicts coordinate pred_coords = model.predict_click_location(screenshot, action_desc) # Distance metric distance = np.linalg.norm(np.array(pred_coords) - np.array(true_coords)) results.append({ 'task': 'grounding', 'distance_error': distance, 'correct': distance < 50 # Within 50 pixels }) accuracy = np.mean([r['correct'] for r in results]) return {'accuracy': accuracy, 'results': results} def task2_screen_parsing(self, model): """Predict UI elements and their properties""" results = [] for trajectory in self.trajectories: for step in trajectory['steps']: screenshot = step['screenshot'] true_a11y_tree = step['accessibility_tree'] # Model parses screen pred_elements = model.parse_screen(screenshot) # Match predictions to ground truth # Metrics: precision, recall, F1 on element detection metrics = self.compute_parsing_metrics(pred_elements, true_a11y_tree) results.append(metrics) avg_f1 = np.mean([r['f1'] for r in results]) return {'f1': avg_f1, 'results': results} def task3_action_prediction(self, model): """Predict next action given current state and goal""" results = [] for trajectory in self.trajectories: for i in range(len(trajectory['steps']) - 1): current_step = trajectory['steps'][i] next_step = trajectory['steps'][i + 1] # Input: current screenshot + goal description screenshot = current_step['screenshot'] goal = trajectory['task'] # Ground truth next action true_action = next_step['action'] # Model predicts action pred_action = model.predict_next_action(screenshot, goal) # Accuracy: action type matches action_correct = pred_action['type'] == true_action['type'] results.append({ 'action_correct': action_correct, 'action_type': true_action['type'] }) accuracy = np.mean([r['action_correct'] for r in results]) accuracy_by_type = {} for action_type in set(r['action_type'] for r in results): type_results = [r for r in results if r['action_type'] == action_type] accuracy_by_type[action_type] = np.mean([r['action_correct'] for r in type_results]) return {'accuracy': accuracy, 'by_type': accuracy_by_type, 'results': results} def compute_parsing_metrics(self, predicted, ground_truth): """Compare parsed UI elements to ground truth""" # Extract ground truth elements gt_elements = self.extract_elements(ground_truth) # Precision: predicted elements that match ground truth true_positives = 0 for pred in predicted: for gt in gt_elements: if self.elements_match(pred, gt): true_positives += 1 break precision = true_positives / len(predicted) if predicted else 0 recall = true_positives / len(gt_elements) if gt_elements else 0 f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0 return {'precision': precision, 'recall': recall, 'f1': f1} ``` **3. Implement Hybrid Action Execution** Support both GUI and API-based actions. ```python class HybridActionExecutor: def __init__(self, app_type='word'): self.app_type = app_type self.gui_executor = GUIActionExecutor() self.api_executor = APIActionExecutor(app_type) def execute_action(self, action_spec): """Execute action using appropriate method""" action_type = action_spec['type'] if action_type in ['click', 'type', 'drag', 'scroll']: # GUI action return self.gui_executor.execute(action_spec) elif action_type in ['table_insert', 'cell_set', 'slide_modify']: # API action (app-specific) return self.api_executor.execute(action_spec) else: raise ValueError(f"Unknown action type: {action_type}") class GUIActionExecutor: def execute(self, action_spec): """Execute GUI action via mouse/keyboard""" import pyautogui action_type = action_spec['type'] if action_type == 'click': pyautogui.click(action_spec['coordinates']) elif action_type == 'type': pyautogui.typewrite(action_spec['text'], interval=0.05) elif action_type == 'drag': start = action_spec['start_coords'] end = action_spec['end_coords'] pyautogui.moveTo(start) pyautogui.drag(end[0] - start[0], end[1] - start[1], duration=0.5) elif action_type == 'scroll': amount = action_spec['amount'] pyautogui.scroll(amount) return {'success': True} class APIActionExecutor: def __init__(self, app_type): self.app_type = app_type self.excel_api = ExcelAPI() if app_type == 'excel' else None self.word_api = WordAPI() if app_type == 'word' else None def execute(self, action_spec): """Execute action via application API""" if self.app_type == 'excel': return self.excel_api.execute(action_spec) elif self.app_type == 'word': return self.word_api.execute(action_spec) class ExcelAPI: def execute(self, action_spec): """Execute Excel-specific actions""" from openpyxl import load_workbook action_type = action_spec['type'] if action_type == 'cell_set': wb = load_workbook(action_spec['file']) ws = wb[action_spec['sheet']] ws[action_spec['cell']] = action_spec['value'] wb.save(action_spec['file']) elif action_type == 'auto_fill': ws = self.get_worksheet(action_spec) start_cell = action_spec['start'] fill_range = action_spec['range'] # Implementation details... return {'success': True} ``` ## Practical Guidance **When to Use**: - Training desktop/office automation agents - Evaluating grounding, parsing, action prediction capabilities - Developing systems for document/data processing automation **Hyperparameters**: - Screenshot resolution: 1920×1080 (standardize environments) - Action timeout: 5s per action - Max trajectory length: 50-100 steps (typical task size) **When NOT to Use**: - Mobile or web application agents (different UI paradigms) - Custom enterprise applications with unique toolbars - Real-time system interaction (latency-sensitive) **Pitfalls**: - **Accessibility tree incompleteness**: Some custom UI elements not exposed via APIs; supplement with vision - **Coordinate brittleness**: Screen resolution changes invalidate coordinates; use relative positions - **Task ambiguity**: Unclear task descriptions harm learning; use detailed goal specifications **Integration Strategy**: Use as pre-training data for agents. Fine-tune on domain-specific applications after. Combine with synthetic data augmentation for scaling. ## Reference arXiv: https://arxiv.org/abs/2511.04307