[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M715QQ7B8FFQMD1QP3SA0F7P":3,"skill-same:01M715QQ7B8FFQMD1QP3SA0F7P":65},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M715QQ7B8FFQMD1QP3SA0F7P","computer-vision-expert",null,"SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcursor\u002Fcomputer-vision-expert\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"Computer Vision","computer","vision","expert","computer vision expert","2026","language models","vision language models","unknown","segment anything","segment","sam","time","yolo26","community","models","Spatial","specialized","analysis","real-time","2026-11-10 10:12:30",false,"---\nname: computer-vision-expert\ndescription: \"SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.\"\nrisk: unknown\nsource: community\ndate_added: \"2026-02-27\"\n---\n\n# Computer Vision Expert (SOTA 2026)\n\n**Role**: Advanced Vision Systems Architect & Spatial Intelligence Expert\n\n## Purpose\nTo provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.\n\n## When to Use\n- Designing high-performance real-time detection systems (YOLO26).\n- Implementing zero-shot or text-guided segmentation tasks (SAM 3).\n- Building spatial awareness, depth estimation, or 3D reconstruction systems.\n- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).\n- Needing to bridge classical geometry (calibration) with modern deep learning.\n\n## Capabilities\n\n### 1. Unified Real-Time Detection (YOLO26)\n- **NMS-Free Architecture**: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).\n- **Edge Deployment**: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.\n- **Improved Small-Object Recognition**: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.\n\n### 2. Promptable Segmentation (SAM 3)\n- **Text-to-Mask**: Ability to segment objects using natural language descriptions (e.g., \"the blue container on the right\").\n- **SAM 3D**: Reconstructing objects, scenes, and human bodies in 3D from single\u002Fmulti-view images.\n- **Unified Logic**: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.\n\n### 3. Vision Language Models (VLMs)\n- **Visual Grounding**: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.\n- **Visual Question Answering (VQA)**: Extracting structured data from visual inputs through conversational reasoning.\n\n### 4. Geometry & Reconstruction\n- **Depth Anything V2**: State-of-the-art monocular depth estimation for spatial awareness.\n- **Sub-pixel Calibration**: Chessboard\u002FCharuco pipelines for high-precision stereo\u002Fmulti-camera rigs.\n- **Visual SLAM**: Real-time localization and mapping for autonomous systems.\n\n## Patterns\n\n### 1. Text-Guided Vision Pipelines\n- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.\n- Combine YOLO26 for fast \"candidate proposal\" and SAM 3 for \"precise mask refinement\".\n\n### 2. Deployment-First Design\n- Leverage YOLO26's simplified ONNX\u002FTensorRT exports (NMS-free).\n- Use MuSGD for significantly faster training convergence on custom datasets.\n\n### 3. Progressive 3D Scene Reconstruction\n- Integrate monocular depth maps with geometric homographies to build accurate 2.5D\u002F3D representations of scenes.\n\n## Anti-Patterns\n\n- **Manual NMS Post-processing**: Stick to NMS-free architectures (YOLO26\u002Fv10+) for lower overhead.\n- **Click-Only Segmentation**: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.\n- **Legacy DFL Exports**: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.\n\n## Sharp Edges (2026)\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| SAM 3 VRAM Usage | Medium | Use quantized\u002Fdistilled versions for local GPU inference. |\n| Text Ambiguity | Low | Use descriptive prompts (\"the 5mm bolt\" instead of just \"bolt\"). |\n| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |\n| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU\u002FTPUs. |\n\n## Related Skills\n`ai-engineer`, `robotics-expert`, `research-engineer`, `embedded-systems`\n","# Computer Vision Expert (SOTA 2026)\n\n**Role**: Advanced Vision Systems Architect & Spatial Intelligence Expert\n\n## Purpose\nTo provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.\n\n## When to Use\n- Designing high-performance real-time detection systems (YOLO26).\n- Implementing zero-shot or text-guided segmentation tasks (SAM 3).\n- Building spatial awareness, depth estimation, or 3D reconstruction systems.\n- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).\n- Needing to bridge classical geometry (calibration) with modern deep learning.\n\n## Capabilities\n\n### 1. Unified Real-Time Detection (YOLO26)\n- **NMS-Free Architecture**: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).\n- **Edge Deployment**: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.\n- **Improved Small-Object Recognition**: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.\n\n### 2. Promptable Segmentation (SAM 3)\n- **Text-to-Mask**: Ability to segment objects using natural language descriptions (e.g., \"the blue container on the right\").\n- **SAM 3D**: Reconstructing objects, scenes, and human bodies in 3D from single\u002Fmulti-view images.\n- **Unified Logic**: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.\n\n### 3. Vision Language Models (VLMs)\n- **Visual Grounding**: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.\n- **Visual Question Answering (VQA)**: Extracting structured data from visual inputs through conversational reasoning.\n\n### 4. Geometry & Reconstruction\n- **Depth Anything V2**: State-of-the-art monocular depth estimation for spatial awareness.\n- **Sub-pixel Calibration**: Chessboard\u002FCharuco pipelines for high-precision stereo\u002Fmulti-camera rigs.\n- **Visual SLAM**: Real-time localization and mapping for autonomous systems.\n\n## Patterns\n\n### 1. Text-Guided Vision Pipelines\n- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.\n- Combine YOLO26 for fast \"candidate proposal\" and SAM 3 for \"precise mask refinement\".\n\n### 2. Deployment-First Design\n- Leverage YOLO26's simplified ONNX\u002FTensorRT exports (NMS-free).\n- Use MuSGD for significantly faster training convergence on custom datasets.\n\n### 3. Progressive 3D Scene Reconstruction\n- Integrate monocular depth maps with geometric homographies to build accurate 2.5D\u002F3D representations of scenes.\n\n## Anti-Patterns\n\n- **Manual NMS Post-processing**: Stick to NMS-free architectures (YOLO26\u002Fv10+) for lower overhead.\n- **Click-Only Segmentation**: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.\n- **Legacy DFL Exports**: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.\n\n## Sharp Edges (2026)\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| SAM 3 VRAM Usage | Medium | Use quantized\u002Fdistilled versions for local GPU inference. |\n| Text Ambiguity | Low | Use descriptive prompts (\"the 5mm bolt\" instead of just \"bolt\"). |\n| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |\n| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU\u002FTPUs. |\n\n## Related Skills\n`ai-engineer`, `robotics-expert`, `research-engineer`, `embedded-systems`\n",{"name":5,"risk":27,"source":33,"date_added":44,"description":7},"2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fcursor\u002Fcomputer-vision-expert","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill computer-vision-expert",{"tool":57,"command":58},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill computer-vision-expert",{"tool":60,"command":61},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill computer-vision-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcursor\u002Fcomputer-vision-expert\u002FSKILL.md",true,[],[66,90,115,125,138,157],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":77,"publishedAt":39},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[78,79,80,81,82,83,84,85,86,87,88,89],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch","with",{"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":96,"publishedAt":39},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[97,98,99,100,101,31,80,102,103,104,105,106,107,108,109,110,111,112,113,114],"trend","trend tap","tap","api keys","Bash","trending","hacker","aggregator","do","when","brew","live","data","google","install","read","across","api",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":39},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[122,123,97,107,108,109,110,101,80,112,124,113,114,106,104,103,102,111,31,100],"trend radar","radar","pulse",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":39},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[132,133,134,135,136,137],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":149,"publishedAt":39},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[140,86,150,151,152,89,107,153,154,155,99,156,111],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":163,"publishedAt":39},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[159,164,165,166,111,167,154,168,169,170,171,172],"status","play","go","latest","github","discover","control","cmd","volume"]