[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADMAMGQ1P9N18VKSK0HVQD":3,"skill-same:01M6ADMAMGQ1P9N18VKSK0HVQD":89},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6ADMAMGQ1P9N18VKSK0HVQD","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-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33602,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcomputer-use-agents\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"Computer Use","computer use agents","computer","use","buttons","ai agents","when","spawner","source","gui automation","typing","screen","critical","focus","text","open","operator","apache","openai","desktop","automation","security","2026-11-10 10:12:30",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-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.\"\nmetadata:\n  source: vibeship-spawner-skills (Apache 2.0)\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-4-6\"  # 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","# 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-4-6\"  # 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",{"name":5,"metadata":51,"description":7},{"source":52},"vibeship-spawner-skills (Apache 2.0)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcomputer-use-agents",3963,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-15 01:32:48",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add davila7\u002Fclaude-code-templates --skill computer-use-agents",{"tool":69,"command":70},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill computer-use-agents",{"tool":72,"command":73},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill computer-use-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcomputer-use-agents\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"properties.ts","SCRIPT","脚本",489,"https:\u002F\u002Fgithub.com\u002Fvertesia\u002Fcomposableai\u002Fblob\u002Fmain\u002Ftemplates\u002Fplugin-template\u002Fsrc\u002Fmodules\u002Fexamples\u002Fresources\u002Fskills\u002Fuser-select\u002Fproperties.ts",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"user-select.tsx","OTHER","其他",9235,"https:\u002F\u002Fgithub.com\u002Fvertesia\u002Fcomposableai\u002Fblob\u002Fmain\u002Ftemplates\u002Fplugin-template\u002Fsrc\u002Fmodules\u002Fexamples\u002Fresources\u002Fskills\u002Fuser-select\u002Fuser-select.tsx",[90,116,142,175,210,234],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":97,"publishedAt":46},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[96],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[98,99,100,101,102,103,104,105,27,30,106,107,108,109,110,111,112,113,114,115],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[122],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,129,130,131,132,133,134,30,135,136,137,138,37,139,140,141],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","signals","audit","dependency",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":157,"publishedAt":46},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[148,151,152],{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":153,"categoryName":154,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[158,159,160,161,162,163,164,103,165,166,167,168,169,170,171,27,172,173,30,174],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":46},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,103,209],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":214,"sourceHtmlUrl":215,"categories":216,"keywords":221,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[217,220],{"categoryNo":218,"categoryName":219,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},[222,223,224,225,226,188,227,197,198,228,229,230,231,103,232,107,233],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":238,"sourceHtmlUrl":239,"categories":240,"keywords":248,"publishedAt":46},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[241,244,245],{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":246,"categoryName":247,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000036","内容创作者",[249,250,251,252,253,254,255,256,190,257,200,135,258,259,260,261,262,170,263,30,264,265,266,267,268,113,269],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]