[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FWETF9FH7NRP8NF9RTJ6FF":3,"skill-same:01M6FWETF9FH7NRP8NF9RTJ6FF":64},{"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":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":14,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":47,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6FWETF9FH7NRP8NF9RTJ6FF","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.","reikiplanet\u002Fdaoskills","reikiplanet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F96377599?v=4","01M6CVWCMD740YHQDK7E9QYBW4","Reiki Planet","01M6CVWCS4KXVA2CWFVJ43EAXK",0,"MIT","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fother\u002Fcomputer-vision-expert\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"Computer Vision","computer","vision","expert","computer vision expert","segment anything","segment","sam","time","yolo26","language","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\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":41,"source":30,"description":7},"unknown","master","dff58085da947e81e8418c922055644bf0082b1b","other\u002Fcomputer-vision-expert","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills","https:\u002F\u002Fgithub.com\u002Freikiplanet","https:\u002F\u002Fwww.reiki-planet.com","Reiki Planet Game","2026-03-29 06:02:29",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add reikiplanet\u002Fdaoskills --skill computer-vision-expert",{"tool":56,"command":57},"bunx","bunx skills add reikiplanet\u002Fdaoskills --skill computer-vision-expert",{"tool":59,"command":60},"pnpm","pnpm dlx skills add reikiplanet\u002Fdaoskills --skill computer-vision-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fother\u002Fcomputer-vision-expert\u002FSKILL.md",true,[],[65,91,103,118,138,160],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":36},"01M6G614JTEQJHPT8AKK3YQ18S","web-games","Web browser game development principles. Framework selection, WebGPU, optimization, PWA.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fweb-games\u002FSKILL.md",[],[72,73,74,75,76,77,30,78,79,80,81,82,83,84,85,86,87,88,89,90],"games","web games","web","grep","principles","selection","framework selection","edit","write","read","pwa","game","web browser","game development","Optimization","framework","browser","development","webgpu",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":36},"01M6G611H2YGV0GZFP90D38ZMJ","vr-ar","VR\u002FAR development principles. Comfort, interaction, performance requirements.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fvr-ar\u002FSKILL.md",[],[98,99,89,100,101,30,79,80,81,75,102,76],"ar","vr ar","interaction","requirements","performance",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":36},"01M6G60XNMHAMV1SV0EJ1CX0AY","templates","Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Ftemplates\u002FSKILL.md",[],[105,110,111,112,113,114,30,115,81,116,117,75],"project scaffolding","From Scratch","tech","project","scaffolding","use","projects","applications",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":36},"01M6G60T0GATMK3Q5H8GDC1Z8S","wcag-audit-patterns","Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. 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OWASP 2025, Supply Chain Security, attack surface mapping, risk prioritization.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fvulnerability-scanner\u002FSKILL.md",[],[145,146,147,148,149,150,151,152,30,81,75,153,76,34,154,155,156,157,158,159],"scanner","vulnerability scanner","vulnerability","prioritization","2025","mapping","chain","supply chain","Bash","risk","owasp","supply chain security","security","supply","advanced",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":36},"01M6G60JZBB8DK0W3DSG1WNMZK","threat-modeling-expert","Expert in threat modeling methodologies, security architecture review, and risk assessment. 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