[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71RXV638TEDCB93KMZDCM27":3,"skill-same:01M71RXV638TEDCB93KMZDCM27":63},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":13,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M71RXV638TEDCB93KMZDCM27","computer-use-agents",null,"Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator\u002FCUA, and open-so...","javiertarazon\u002Fide-agente-free-jt7-","javiertarazon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153141207?v=4","01M71R52TB3S9HXKH6WSNT8A7V","01M71R52VG5KS2DKDDH4GDQ30A",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fcomputer-use-agents\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"computer use agents","Computer Use","computer","use","openai","operator","screens","buttons","build","apache","with","text","ai agents","unknown","spawner","open","cua","do","typing","2026-11-10 13:02:02",false,"---\nname: computer-use-agents\ndescription: \"Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator\u002FCUA, and open-so...\"\nsource: vibeship-spawner-skills (Apache 2.0)\nrisk: unknown\n---\n\n# Computer Use Agents\n\n## Patterns\n\n### Perception-Reasoning-Action Loop\n\nThe fundamental architecture of computer use agents: observe screen,\nreason about next action, execute action, repeat. This loop integrates\nvision models with action execution through an iterative pipeline.\n\nKey components:\n1. PERCEPTION: Screenshot captures current screen state\n2. REASONING: Vision-language model analyzes and plans\n3. ACTION: Execute mouse\u002Fkeyboard operations\n4. FEEDBACK: Observe result, continue or correct\n\nCritical insight: Vision agents are completely still during \"thinking\"\nphase (1-5 seconds), creating a detectable pause pattern.\n\n\n**When to use**: ['Building any computer use agent from scratch', 'Integrating vision models with desktop control', 'Understanding agent behavior patterns']\n\n```python\nfrom anthropic import Anthropic\nfrom PIL import Image\nimport base64\nimport pyautogui\nimport time\n\nclass ComputerUseAgent:\n    \"\"\"\n    Perception-Reasoning-Action loop implementation.\n    Based on Anthropic Computer Use patterns.\n    \"\"\"\n\n    def __init__(self, client: Anthropic, model: str = \"claude-sonnet-4-20250514\"):\n        self.client = client\n        self.model = model\n        self.max_steps = 50  # Prevent runaway loops\n        self.action_delay = 0.5  # Seconds between actions\n\n    def capture_screenshot(self) -> str:\n        \"\"\"Capture screen and return base64 encoded image.\"\"\"\n        screenshot = pyautogui.screenshot()\n        # Resize for token efficiency (1280x800 is good balance)\n        screenshot = screenshot.resize((1280, 800), Image.LANCZOS)\n\n        import io\n        buffer = io.BytesIO()\n        screenshot.save(buffer, format=\"PNG\")\n        return base64.b64encode(buffer.getvalue()).decode()\n\n    def execute_action(self, action: dict) -> dict:\n        \"\"\"Execute mouse\u002Fkeyboard action on the computer.\"\"\"\n        action_type = action.get(\"type\")\n\n        if action_type == \"click\":\n            x, y = action[\"x\"], action[\"y\"]\n            button = action.get(\"button\", \"left\")\n            pyautogui.click(x, y, button=button)\n            return {\"success\": True, \"action\": f\"clicked at ({x}, {y})\"}\n\n        elif action_type == \"type\":\n            text = action[\"text\"]\n            pyautogui.typewrite(text, interval=0.02)\n            return {\"success\": True, \"action\": f\"typed {len(text)} chars\"}\n\n        elif action_type == \"key\":\n            key = action[\"key\"]\n            pyautogui.press(key)\n            return {\"success\": True, \"action\": f\"pressed {key}\"}\n\n        elif action_type == \"scroll\":\n            direction = action.get(\"direction\", \"down\")\n            amount = action.get(\"amount\", 3)\n            scroll = -amount if direction == \"down\" else amount\n            pyautogui.scroll(scroll)\n            return {\"success\": True, \"action\": f\"scrolled {dir\n```\n\n### Sandboxed Environment Pattern\n\nComputer use agents MUST run in isolated, sandboxed environments.\nNever give agents direct access to your main system - the security\nrisks are too high. Use Docker containers with virtual desktops.\n\nKey isolation requirements:\n1. NETWORK: Restrict to necessary endpoints only\n2. FILESYSTEM: Read-only or scoped to temp directories\n3. CREDENTIALS: No access to host credentials\n4. SYSCALLS: Filter dangerous system calls\n5. RESOURCES: Limit CPU, memory, time\n\nThe goal is \"blast radius minimization\" - if the agent goes wrong,\ndamage is contained to the sandbox.\n\n\n**When to use**: ['Deploying any computer use agent', 'Testing agent behavior safely', 'Running untrusted automation tasks']\n\n```python\n# Dockerfile for sandboxed computer use environment\n# Based on Anthropic's reference implementation pattern\n\nFROM ubuntu:22.04\n\n# Install desktop environment\nRUN apt-get update && apt-get install -y \\\n    xvfb \\\n    x11vnc \\\n    fluxbox \\\n    xterm \\\n    firefox \\\n    python3 \\\n    python3-pip \\\n    supervisor\n\n# Security: Create non-root user\nRUN useradd -m -s \u002Fbin\u002Fbash agent && \\\n    mkdir -p \u002Fhome\u002Fagent\u002F.vnc\n\n# Install Python dependencies\nCOPY requirements.txt \u002Ftmp\u002F\nRUN pip3 install -r \u002Ftmp\u002Frequirements.txt\n\n# Security: Drop capabilities\nRUN apt-get install -y --no-install-recommends libcap2-bin && \\\n    setcap -r \u002Fusr\u002Fbin\u002Fpython3 || true\n\n# Copy agent code\nCOPY --chown=agent:agent . \u002Fapp\nWORKDIR \u002Fapp\n\n# Supervisor config for virtual display + VNC\nCOPY supervisord.conf \u002Fetc\u002Fsupervisor\u002Fconf.d\u002F\n\n# Expose VNC port only (not desktop directly)\nEXPOSE 5900\n\n# Run as non-root\nUSER agent\n\nCMD [\"\u002Fusr\u002Fbin\u002Fsupervisord\", \"-c\", \"\u002Fetc\u002Fsupervisor\u002Fconf.d\u002Fsupervisord.conf\"]\n\n---\n\n# docker-compose.yml with security constraints\nversion: '3.8'\n\nservices:\n  computer-use-agent:\n    build: .\n    ports:\n      - \"5900:5900\"  # VNC for observation\n      - \"8080:8080\"  # API for control\n\n    # Security constraints\n    security_opt:\n      - no-new-privileges:true\n      - seccomp:seccomp-profile.json\n\n    # Resource limits\n    deploy:\n      resources:\n        limits:\n          cpus: '2'\n          memory: 4G\n        reservations:\n          cpus: '0.5'\n          memory: 1G\n\n    # Network isolation\n    networks:\n      - agent-network\n\n    # No access to host filesystem\n    volumes:\n      - agent-tmp:\u002Ftmp\n\n    # Read-only root filesystem\n    read_only: true\n    tmpfs:\n      - \u002Frun\n      - \u002Fvar\u002Frun\n\n    # Environment\n    environment:\n      - DISPLAY=:99\n      - NO_PROXY=localhost\n\nnetworks:\n  agent-network:\n    driver: bridge\n    internal: true  # No internet by default\n\nvolumes:\n  agent-tmp:\n\n---\n\n# Python wrapper with additional runtime sandboxing\nimport subprocess\nimport os\nfrom dataclasses im\n```\n\n### Anthropic Computer Use Implementation\n\nOfficial implementation pattern using Claude's computer use capability.\nClaude 3.5 Sonnet was the first frontier model to offer computer use.\nClaude Opus 4.5 is now the \"best model in the world for computer use.\"\n\nKey capabilities:\n- screenshot: Capture current screen state\n- mouse: Click, move, drag operations\n- keyboard: Type text, press keys\n- bash: Run shell commands\n- text_editor: View and edit files\n\nTool versions:\n- computer_20251124 (Opus 4.5): Adds zoom action for detailed inspection\n- computer_20250124 (All other models): Standard capabilities\n\nCritical limitation: \"Some UI elements (like dropdowns and scrollbars)\nmight be tricky for Claude to manipulate\" - Anthropic docs\n\n\n**When to use**: ['Building production computer use agents', 'Need highest quality vision understanding', 'Full desktop control (not just browser)']\n\n```python\nfrom anthropic import Anthropic\nfrom anthropic.types.beta import (\n    BetaToolComputerUse20241022,\n    BetaToolBash20241022,\n    BetaToolTextEditor20241022,\n)\nimport subprocess\nimport base64\nfrom PIL import Image\nimport io\n\nclass AnthropicComputerUse:\n    \"\"\"\n    Official Anthropic Computer Use implementation.\n\n    Requires:\n    - Docker container with virtual display\n    - VNC for viewing agent actions\n    - Proper tool implementations\n    \"\"\"\n\n    def __init__(self):\n        self.client = Anthropic()\n        self.model = \"claude-sonnet-4-20250514\"  # Best for computer use\n        self.screen_size = (1280, 800)\n\n    def get_tools(self) -> list:\n        \"\"\"Define computer use tools.\"\"\"\n        return [\n            BetaToolComputerUse20241022(\n                type=\"computer_20241022\",\n                name=\"computer\",\n                display_width_px=self.screen_size[0],\n                display_height_px=self.screen_size[1],\n            ),\n            BetaToolBash20241022(\n                type=\"bash_20241022\",\n                name=\"bash\",\n            ),\n            BetaToolTextEditor20241022(\n                type=\"text_editor_20241022\",\n                name=\"str_replace_editor\",\n            ),\n        ]\n\n    def execute_tool(self, name: str, input: dict) -> dict:\n        \"\"\"Execute a tool and return result.\"\"\"\n\n        if name == \"computer\":\n            return self._handle_computer_action(input)\n        elif name == \"bash\":\n            return self._handle_bash(input)\n        elif name == \"str_replace_editor\":\n            return self._handle_editor(input)\n        else:\n            return {\"error\": f\"Unknown tool: {name}\"}\n\n    def _handle_computer_action(self, input: dict) -> dict:\n        \"\"\"Handle computer control actions.\"\"\"\n        action = input.get(\"action\")\n\n        if action == \"screenshot\":\n            # Capture via xdotool\u002Fscrot\n            subprocess.run([\"scrot\", \"\u002Ftmp\u002Fscreenshot.png\"])\n\n            with open(\"\u002Ftmp\u002Fscreenshot.png\", \"rb\") as f:\n            \n```\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Issue | critical | ## Defense in depth - no single solution works |\n| Issue | medium | ## Add human-like variance to actions |\n| Issue | high | ## Use keyboard alternatives when possible |\n| Issue | medium | ## Accept the tradeoff |\n| Issue | high | ## Implement context management |\n| Issue | high | ## Monitor and limit costs |\n| Issue | critical | ## ALWAYS use sandboxing |\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# Computer Use Agents\n\n## Patterns\n\n### Perception-Reasoning-Action Loop\n\nThe fundamental architecture of computer use agents: observe screen,\nreason about next action, execute action, repeat. This loop integrates\nvision models with action execution through an iterative pipeline.\n\nKey components:\n1. PERCEPTION: Screenshot captures current screen state\n2. REASONING: Vision-language model analyzes and plans\n3. ACTION: Execute mouse\u002Fkeyboard operations\n4. FEEDBACK: Observe result, continue or correct\n\nCritical insight: Vision agents are completely still during \"thinking\"\nphase (1-5 seconds), creating a detectable pause pattern.\n\n\n**When to use**: ['Building any computer use agent from scratch', 'Integrating vision models with desktop control', 'Understanding agent behavior patterns']\n\n```python\nfrom anthropic import Anthropic\nfrom PIL import Image\nimport base64\nimport pyautogui\nimport time\n\nclass ComputerUseAgent:\n    \"\"\"\n    Perception-Reasoning-Action loop implementation.\n    Based on Anthropic Computer Use patterns.\n    \"\"\"\n\n    def __init__(self, client: Anthropic, model: str = \"claude-sonnet-4-20250514\"):\n        self.client = client\n        self.model = model\n        self.max_steps = 50  # Prevent runaway loops\n        self.action_delay = 0.5  # Seconds between actions\n\n    def capture_screenshot(self) -> str:\n        \"\"\"Capture screen and return base64 encoded image.\"\"\"\n        screenshot = pyautogui.screenshot()\n        # Resize for token efficiency (1280x800 is good balance)\n        screenshot = screenshot.resize((1280, 800), Image.LANCZOS)\n\n        import io\n        buffer = io.BytesIO()\n        screenshot.save(buffer, format=\"PNG\")\n        return base64.b64encode(buffer.getvalue()).decode()\n\n    def execute_action(self, action: dict) -> dict:\n        \"\"\"Execute mouse\u002Fkeyboard action on the computer.\"\"\"\n        action_type = action.get(\"type\")\n\n        if action_type == \"click\":\n            x, y = action[\"x\"], action[\"y\"]\n            button = action.get(\"button\", \"left\")\n            pyautogui.click(x, y, button=button)\n            return {\"success\": True, \"action\": f\"clicked at ({x}, {y})\"}\n\n        elif action_type == \"type\":\n            text = action[\"text\"]\n            pyautogui.typewrite(text, interval=0.02)\n            return {\"success\": True, \"action\": f\"typed {len(text)} chars\"}\n\n        elif action_type == \"key\":\n            key = action[\"key\"]\n            pyautogui.press(key)\n            return {\"success\": True, \"action\": f\"pressed {key}\"}\n\n        elif action_type == \"scroll\":\n            direction = action.get(\"direction\", \"down\")\n            amount = action.get(\"amount\", 3)\n            scroll = -amount if direction == \"down\" else amount\n            pyautogui.scroll(scroll)\n            return {\"success\": True, \"action\": f\"scrolled {dir\n```\n\n### Sandboxed Environment Pattern\n\nComputer use agents MUST run in isolated, sandboxed environments.\nNever give agents direct access to your main system - the security\nrisks are too high. Use Docker containers with virtual desktops.\n\nKey isolation requirements:\n1. NETWORK: Restrict to necessary endpoints only\n2. FILESYSTEM: Read-only or scoped to temp directories\n3. CREDENTIALS: No access to host credentials\n4. SYSCALLS: Filter dangerous system calls\n5. RESOURCES: Limit CPU, memory, time\n\nThe goal is \"blast radius minimization\" - if the agent goes wrong,\ndamage is contained to the sandbox.\n\n\n**When to use**: ['Deploying any computer use agent', 'Testing agent behavior safely', 'Running untrusted automation tasks']\n\n```python\n# Dockerfile for sandboxed computer use environment\n# Based on Anthropic's reference implementation pattern\n\nFROM ubuntu:22.04\n\n# Install desktop environment\nRUN apt-get update && apt-get install -y \\\n    xvfb \\\n    x11vnc \\\n    fluxbox \\\n    xterm \\\n    firefox \\\n    python3 \\\n    python3-pip \\\n    supervisor\n\n# Security: Create non-root user\nRUN useradd -m -s \u002Fbin\u002Fbash agent && \\\n    mkdir -p \u002Fhome\u002Fagent\u002F.vnc\n\n# Install Python dependencies\nCOPY requirements.txt \u002Ftmp\u002F\nRUN pip3 install -r \u002Ftmp\u002Frequirements.txt\n\n# Security: Drop capabilities\nRUN apt-get install -y --no-install-recommends libcap2-bin && \\\n    setcap -r \u002Fusr\u002Fbin\u002Fpython3 || true\n\n# Copy agent code\nCOPY --chown=agent:agent . \u002Fapp\nWORKDIR \u002Fapp\n\n# Supervisor config for virtual display + VNC\nCOPY supervisord.conf \u002Fetc\u002Fsupervisor\u002Fconf.d\u002F\n\n# Expose VNC port only (not desktop directly)\nEXPOSE 5900\n\n# Run as non-root\nUSER agent\n\nCMD [\"\u002Fusr\u002Fbin\u002Fsupervisord\", \"-c\", \"\u002Fetc\u002Fsupervisor\u002Fconf.d\u002Fsupervisord.conf\"]\n\n---\n\n# docker-compose.yml with security constraints\nversion: '3.8'\n\nservices:\n  computer-use-agent:\n    build: .\n    ports:\n      - \"5900:5900\"  # VNC for observation\n      - \"8080:8080\"  # API for control\n\n    # Security constraints\n    security_opt:\n      - no-new-privileges:true\n      - seccomp:seccomp-profile.json\n\n    # Resource limits\n    deploy:\n      resources:\n        limits:\n          cpus: '2'\n          memory: 4G\n        reservations:\n          cpus: '0.5'\n          memory: 1G\n\n    # Network isolation\n    networks:\n      - agent-network\n\n    # No access to host filesystem\n    volumes:\n      - agent-tmp:\u002Ftmp\n\n    # Read-only root filesystem\n    read_only: true\n    tmpfs:\n      - \u002Frun\n      - \u002Fvar\u002Frun\n\n    # Environment\n    environment:\n      - DISPLAY=:99\n      - NO_PROXY=localhost\n\nnetworks:\n  agent-network:\n    driver: bridge\n    internal: true  # No internet by default\n\nvolumes:\n  agent-tmp:\n\n---\n\n# Python wrapper with additional runtime sandboxing\nimport subprocess\nimport os\nfrom dataclasses im\n```\n\n### Anthropic Computer Use Implementation\n\nOfficial implementation pattern using Claude's computer use capability.\nClaude 3.5 Sonnet was the first frontier model to offer computer use.\nClaude Opus 4.5 is now the \"best model in the world for computer use.\"\n\nKey capabilities:\n- screenshot: Capture current screen state\n- mouse: Click, move, drag operations\n- keyboard: Type text, press keys\n- bash: Run shell commands\n- text_editor: View and edit files\n\nTool versions:\n- computer_20251124 (Opus 4.5): Adds zoom action for detailed inspection\n- computer_20250124 (All other models): Standard capabilities\n\nCritical limitation: \"Some UI elements (like dropdowns and scrollbars)\nmight be tricky for Claude to manipulate\" - Anthropic docs\n\n\n**When to use**: ['Building production computer use agents', 'Need highest quality vision understanding', 'Full desktop control (not just browser)']\n\n```python\nfrom anthropic import Anthropic\nfrom anthropic.types.beta import (\n    BetaToolComputerUse20241022,\n    BetaToolBash20241022,\n    BetaToolTextEditor20241022,\n)\nimport subprocess\nimport base64\nfrom PIL import Image\nimport io\n\nclass AnthropicComputerUse:\n    \"\"\"\n    Official Anthropic Computer Use implementation.\n\n    Requires:\n    - Docker container with virtual display\n    - VNC for viewing agent actions\n    - Proper tool implementations\n    \"\"\"\n\n    def __init__(self):\n        self.client = Anthropic()\n        self.model = \"claude-sonnet-4-20250514\"  # Best for computer use\n        self.screen_size = (1280, 800)\n\n    def get_tools(self) -> list:\n        \"\"\"Define computer use tools.\"\"\"\n        return [\n            BetaToolComputerUse20241022(\n                type=\"computer_20241022\",\n                name=\"computer\",\n                display_width_px=self.screen_size[0],\n                display_height_px=self.screen_size[1],\n            ),\n            BetaToolBash20241022(\n                type=\"bash_20241022\",\n                name=\"bash\",\n            ),\n            BetaToolTextEditor20241022(\n                type=\"text_editor_20241022\",\n                name=\"str_replace_editor\",\n            ),\n        ]\n\n    def execute_tool(self, name: str, input: dict) -> dict:\n        \"\"\"Execute a tool and return result.\"\"\"\n\n        if name == \"computer\":\n            return self._handle_computer_action(input)\n        elif name == \"bash\":\n            return self._handle_bash(input)\n        elif name == \"str_replace_editor\":\n            return self._handle_editor(input)\n        else:\n            return {\"error\": f\"Unknown tool: {name}\"}\n\n    def _handle_computer_action(self, input: dict) -> dict:\n        \"\"\"Handle computer control actions.\"\"\"\n        action = input.get(\"action\")\n\n        if action == \"screenshot\":\n            # Capture via xdotool\u002Fscrot\n            subprocess.run([\"scrot\", \"\u002Ftmp\u002Fscreenshot.png\"])\n\n            with open(\"\u002Ftmp\u002Fscreenshot.png\", \"rb\") as f:\n            \n```\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Issue | critical | ## Defense in depth - no single solution works |\n| Issue | medium | ## Add human-like variance to actions |\n| Issue | high | ## Use keyboard alternatives when possible |\n| Issue | medium | ## Accept the tradeoff |\n| Issue | high | ## Implement context management |\n| Issue | high | ## Monitor and limit costs |\n| Issue | critical | ## ALWAYS use sandboxing |\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":31,"source":42,"description":7},"vibeship-spawner-skills (Apache 2.0)","release\u002Fv4.2.11-panel-pro","a92ea02bcf7e2b3dbaf46d49696c29a3cb490d4f",".github\u002Fskills\u002Fcomputer-use-agents","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-","https:\u002F\u002Fgithub.com\u002Fjaviertarazon","2026-07-12 07:10:45",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add javiertarazon\u002Fide-agente-free-jt7- --skill computer-use-agents",{"tool":55,"command":56},"bunx","bunx skills add javiertarazon\u002Fide-agente-free-jt7- --skill computer-use-agents",{"tool":58,"command":59},"pnpm","pnpm dlx skills add javiertarazon\u002Fide-agente-free-jt7- --skill computer-use-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fcomputer-use-agents\u002FSKILL.md",true,[],[64,93,120,140,155,175],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":75,"publishedAt":37},"01M71TQ7Q6Z10VWF3C2H5X1KW9","zustand-store-ts","Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state\u002Faction separation. 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