[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70HAS4ZMAWC4ZGJPQ367H3H":3,"skill-same:01M70HAS4ZMAWC4ZGJPQ367H3H":103},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M70HAS4ZMAWC4ZGJPQ367H3H","senior-computer-vision",null,"World-class computer vision skill for image\u002Fvideo processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.","shengdabai\u002FTony-Claude-Code-Skills","shengdabai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F116417419?v=4","01M70F6D2WY5PQV5A92BKYJQEW","Tony Sheng","01M70F6D3GAAAYZ5H4CC1G8VXM",3,"MIT","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fengineering-team\u002Fsenior-computer-vision\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"senior","Computer Vision","senior computer vision","computer","vision","video processing","when","3d vision","implementing","yolo","processing","time","world","image","3D","Video","analysis","segmentation","training","pytorch","2026-11-10 10:12:30",false,"---\nname: senior-computer-vision\ndescription: World-class computer vision skill for image\u002Fvideo processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.\n---\n\n# Senior Computer Vision Engineer\n\nWorld-class senior computer vision engineer skill for production-grade AI\u002FML\u002FData systems.\n\n## Quick Start\n\n### Main Capabilities\n\n```bash\n# Core Tool 1\npython scripts\u002Fvision_model_trainer.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Finference_optimizer.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fdataset_pipeline_builder.py --config config.yaml --deploy\n```\n\n## Core Expertise\n\nThis skill covers world-class capabilities in:\n\n- Advanced production patterns and architectures\n- Scalable system design and implementation\n- Performance optimization at scale\n- MLOps and DataOps best practices\n- Real-time processing and inference\n- Distributed computing frameworks\n- Model deployment and monitoring\n- Security and compliance\n- Cost optimization\n- Team leadership and mentoring\n\n## Tech Stack\n\n**Languages:** Python, SQL, R, Scala, Go\n**ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost\n**Data Tools:** Spark, Airflow, dbt, Kafka, Databricks\n**LLM Frameworks:** LangChain, LlamaIndex, DSPy\n**Deployment:** Docker, Kubernetes, AWS\u002FGCP\u002FAzure\n**Monitoring:** MLflow, Weights & Biases, Prometheus\n**Databases:** PostgreSQL, BigQuery, Snowflake, Pinecone\n\n## Reference Documentation\n\n### 1. Computer Vision Architectures\n\nComprehensive guide available in `references\u002Fcomputer_vision_architectures.md` covering:\n\n- Advanced patterns and best practices\n- Production implementation strategies\n- Performance optimization techniques\n- Scalability considerations\n- Security and compliance\n- Real-world case studies\n\n### 2. Object Detection Optimization\n\nComplete workflow documentation in `references\u002Fobject_detection_optimization.md` including:\n\n- Step-by-step processes\n- Architecture design patterns\n- Tool integration guides\n- Performance tuning strategies\n- Troubleshooting procedures\n\n### 3. Production Vision Systems\n\nTechnical reference guide in `references\u002Fproduction_vision_systems.md` with:\n\n- System design principles\n- Implementation examples\n- Configuration best practices\n- Deployment strategies\n- Monitoring and observability\n\n## Production Patterns\n\n### Pattern 1: Scalable Data Processing\n\nEnterprise-scale data processing with distributed computing:\n\n- Horizontal scaling architecture\n- Fault-tolerant design\n- Real-time and batch processing\n- Data quality validation\n- Performance monitoring\n\n### Pattern 2: ML Model Deployment\n\nProduction ML system with high availability:\n\n- Model serving with low latency\n- A\u002FB testing infrastructure\n- Feature store integration\n- Model monitoring and drift detection\n- Automated retraining pipelines\n\n### Pattern 3: Real-Time Inference\n\nHigh-throughput inference system:\n\n- Batching and caching strategies\n- Load balancing\n- Auto-scaling\n- Latency optimization\n- Cost optimization\n\n## Best Practices\n\n### Development\n\n- Test-driven development\n- Code reviews and pair programming\n- Documentation as code\n- Version control everything\n- Continuous integration\n\n### Production\n\n- Monitor everything critical\n- Automate deployments\n- Feature flags for releases\n- Canary deployments\n- Comprehensive logging\n\n### Team Leadership\n\n- Mentor junior engineers\n- Drive technical decisions\n- Establish coding standards\n- Foster learning culture\n- Cross-functional collaboration\n\n## Performance Targets\n\n**Latency:**\n- P50: \u003C 50ms\n- P95: \u003C 100ms\n- P99: \u003C 200ms\n\n**Throughput:**\n- Requests\u002Fsecond: > 1000\n- Concurrent users: > 10,000\n\n**Availability:**\n- Uptime: 99.9%\n- Error rate: \u003C 0.1%\n\n## Security & Compliance\n\n- Authentication & authorization\n- Data encryption (at rest & in transit)\n- PII handling and anonymization\n- GDPR\u002FCCPA compliance\n- Regular security audits\n- Vulnerability management\n\n## Common Commands\n\n```bash\n# Development\npython -m pytest tests\u002F -v --cov\npython -m black src\u002F\npython -m pylint src\u002F\n\n# Training\npython scripts\u002Ftrain.py --config prod.yaml\npython scripts\u002Fevaluate.py --model best.pth\n\n# Deployment\ndocker build -t service:v1 .\nkubectl apply -f k8s\u002F\nhelm upgrade service .\u002Fcharts\u002F\n\n# Monitoring\nkubectl logs -f deployment\u002Fservice\npython scripts\u002Fhealth_check.py\n```\n\n## Resources\n\n- Advanced Patterns: `references\u002Fcomputer_vision_architectures.md`\n- Implementation Guide: `references\u002Fobject_detection_optimization.md`\n- Technical Reference: `references\u002Fproduction_vision_systems.md`\n- Automation Scripts: `scripts\u002F` directory\n\n## Senior-Level Responsibilities\n\nAs a world-class senior professional:\n\n1. **Technical Leadership**\n   - Drive architectural decisions\n   - Mentor team members\n   - Establish best practices\n   - Ensure code quality\n\n2. **Strategic Thinking**\n   - Align with business goals\n   - Evaluate trade-offs\n   - Plan for scale\n   - Manage technical debt\n\n3. **Collaboration**\n   - Work across teams\n   - Communicate effectively\n   - Build consensus\n   - Share knowledge\n\n4. **Innovation**\n   - Stay current with research\n   - Experiment with new approaches\n   - Contribute to community\n   - Drive continuous improvement\n\n5. **Production Excellence**\n   - Ensure high availability\n   - Monitor proactively\n   - Optimize performance\n   - Respond to incidents\n","# Senior Computer Vision Engineer\n\nWorld-class senior computer vision engineer skill for production-grade AI\u002FML\u002FData systems.\n\n## Quick Start\n\n### Main Capabilities\n\n```bash\n# Core Tool 1\npython scripts\u002Fvision_model_trainer.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Finference_optimizer.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fdataset_pipeline_builder.py --config config.yaml --deploy\n```\n\n## Core Expertise\n\nThis skill covers world-class capabilities in:\n\n- Advanced production patterns and architectures\n- Scalable system design and implementation\n- Performance optimization at scale\n- MLOps and DataOps best practices\n- Real-time processing and inference\n- Distributed computing frameworks\n- Model deployment and monitoring\n- Security and compliance\n- Cost optimization\n- Team leadership and mentoring\n\n## Tech Stack\n\n**Languages:** Python, SQL, R, Scala, Go\n**ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost\n**Data Tools:** Spark, Airflow, dbt, Kafka, Databricks\n**LLM Frameworks:** LangChain, LlamaIndex, DSPy\n**Deployment:** Docker, Kubernetes, AWS\u002FGCP\u002FAzure\n**Monitoring:** MLflow, Weights & Biases, Prometheus\n**Databases:** PostgreSQL, BigQuery, Snowflake, Pinecone\n\n## Reference Documentation\n\n### 1. Computer Vision Architectures\n\nComprehensive guide available in `references\u002Fcomputer_vision_architectures.md` covering:\n\n- Advanced patterns and best practices\n- Production implementation strategies\n- Performance optimization techniques\n- Scalability considerations\n- Security and compliance\n- Real-world case studies\n\n### 2. Object Detection Optimization\n\nComplete workflow documentation in `references\u002Fobject_detection_optimization.md` including:\n\n- Step-by-step processes\n- Architecture design patterns\n- Tool integration guides\n- Performance tuning strategies\n- Troubleshooting procedures\n\n### 3. Production Vision Systems\n\nTechnical reference guide in `references\u002Fproduction_vision_systems.md` with:\n\n- System design principles\n- Implementation examples\n- Configuration best practices\n- Deployment strategies\n- Monitoring and observability\n\n## Production Patterns\n\n### Pattern 1: Scalable Data Processing\n\nEnterprise-scale data processing with distributed computing:\n\n- Horizontal scaling architecture\n- Fault-tolerant design\n- Real-time and batch processing\n- Data quality validation\n- Performance monitoring\n\n### Pattern 2: ML Model Deployment\n\nProduction ML system with high availability:\n\n- Model serving with low latency\n- A\u002FB testing infrastructure\n- Feature store integration\n- Model monitoring and drift detection\n- Automated retraining pipelines\n\n### Pattern 3: Real-Time Inference\n\nHigh-throughput inference system:\n\n- Batching and caching strategies\n- Load balancing\n- Auto-scaling\n- Latency optimization\n- Cost optimization\n\n## Best Practices\n\n### Development\n\n- Test-driven development\n- Code reviews and pair programming\n- Documentation as code\n- Version control everything\n- Continuous integration\n\n### Production\n\n- Monitor everything critical\n- Automate deployments\n- Feature flags for releases\n- Canary deployments\n- Comprehensive logging\n\n### Team Leadership\n\n- Mentor junior engineers\n- Drive technical decisions\n- Establish coding standards\n- Foster learning culture\n- Cross-functional collaboration\n\n## Performance Targets\n\n**Latency:**\n- P50: \u003C 50ms\n- P95: \u003C 100ms\n- P99: \u003C 200ms\n\n**Throughput:**\n- Requests\u002Fsecond: > 1000\n- Concurrent users: > 10,000\n\n**Availability:**\n- Uptime: 99.9%\n- Error rate: \u003C 0.1%\n\n## Security & Compliance\n\n- Authentication & authorization\n- Data encryption (at rest & in transit)\n- PII handling and anonymization\n- GDPR\u002FCCPA compliance\n- Regular security audits\n- Vulnerability management\n\n## Common Commands\n\n```bash\n# Development\npython -m pytest tests\u002F -v --cov\npython -m black src\u002F\npython -m pylint src\u002F\n\n# Training\npython scripts\u002Ftrain.py --config prod.yaml\npython scripts\u002Fevaluate.py --model best.pth\n\n# Deployment\ndocker build -t service:v1 .\nkubectl apply -f k8s\u002F\nhelm upgrade service .\u002Fcharts\u002F\n\n# Monitoring\nkubectl logs -f deployment\u002Fservice\npython scripts\u002Fhealth_check.py\n```\n\n## Resources\n\n- Advanced Patterns: `references\u002Fcomputer_vision_architectures.md`\n- Implementation Guide: `references\u002Fobject_detection_optimization.md`\n- Technical Reference: `references\u002Fproduction_vision_systems.md`\n- Automation Scripts: `scripts\u002F` directory\n\n## Senior-Level Responsibilities\n\nAs a world-class senior professional:\n\n1. **Technical Leadership**\n   - Drive architectural decisions\n   - Mentor team members\n   - Establish best practices\n   - Ensure code quality\n\n2. **Strategic Thinking**\n   - Align with business goals\n   - Evaluate trade-offs\n   - Plan for scale\n   - Manage technical debt\n\n3. **Collaboration**\n   - Work across teams\n   - Communicate effectively\n   - Build consensus\n   - Share knowledge\n\n4. **Innovation**\n   - Stay current with research\n   - Experiment with new approaches\n   - Contribute to community\n   - Drive continuous improvement\n\n5. **Production Excellence**\n   - Ensure high availability\n   - Monitor proactively\n   - Optimize performance\n   - Respond to incidents\n",{"name":5,"description":7},"main","7850740bd837adb32f0a109ab2af82482196c6f2","skills\u002F_collections\u002Frsmdt-the-startup\u002Falirezarezvani-claude-skills\u002Fengineering-team\u002Fsenior-computer-vision",1,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills","https:\u002F\u002Fzturnsgo.com","https:\u002F\u002Fgithub.com\u002Fshengdabai","https:\u002F\u002Fzturnsgo.com\u002F","AI × education creator — producer, not consumer. Taught 6,000+ learners; shipping learning tools & daily essays with Claude Code, practicing @xiaolai's way 🥋","2026-11-01 21:28:23",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add shengdabai\u002FTony-Claude-Code-Skills --skill senior-computer-vision",{"tool":68,"command":69},"bunx","bunx skills add shengdabai\u002FTony-Claude-Code-Skills --skill senior-computer-vision",{"tool":71,"command":72},"pnpm","pnpm dlx skills add shengdabai\u002FTony-Claude-Code-Skills --skill 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Xiaolai)的思考方式、方法论与表达风格,为用户做全方位 1-on-1 教练指导,覆盖创业、赚钱、投资、学习、自学、读书写作、用 AI、以及人生心态与自我管理。当用户提到 \"李笑来\"、\"笑来\"、\"笑来老师\"、\"扮演李笑来\"、\"找笑来聊聊\"、\"笑来怎么看\"、\"xiaolai\"、\"把时间当作朋友\"、\"通往财富自由之路\"、\"定投改变命运\"、\"韭菜的自我修养\"、\"自学是门手艺\"、\"让时间陪你慢慢变富\"、\"七年就是一辈子\"、\"元认知\"、\"活在未来\"、\"复利\u002F长期主义\"、\"践行\"、\"痴迷改进\"、\"个人商业模式\u002F出售时间\"、\"睡后收入\"、\"定投\"、\"财富自由\"、\"操作系统升级\"、\"最少必要知识\"、\"北极星提示词\"、\"用 AI 该怎么学\u002F思考\" 等任一触发词,或当用户带着关于创业\u002F赚钱\u002F投资\u002F学习\u002F职业\u002F重大人生决策的困惑、希望被人用笑来式的「逻辑 + 大白话 + 类比 + 重新定义 + 践行导向」来点醒和指导时,自动进入李笑来教练模式。","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fxiaolai-methodology\u002FSKILL.md",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",[114,115,116,117,118,119,120],"methodology","xiaolai","xiaolai 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Use this skill whenever the user wants to understand, onboard onto, explore, summarize, map, or get an overview of an unfamiliar codebase; asks for architecture docs, a project wiki, a repo walkthrough, module\u002Fpackage explanations, or \"what does this repo do\"; wants to generate, regenerate, resume, browse, or serve code documentation locally; or mentions zread \u002F zread.ai directly. Also use it proactively before diving into a large unknown repo — if `.\u002F.zread\u002Fwiki\u002Fcurrent` exists, read the generated pages instead of crawling source file-by-file; if it doesn't, consider offering to run `zread generate`. The trigger is the intent (understand a codebase through generated docs), not the literal word \"zread\".","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fzread\u002FSKILL.md",[127,128],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[123,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152],"knowledge","wiki","explore","walkthrough","project","get","knowledge base","run","generate","intent","cli","map","documentation","project wiki","disk","architecture","code","unknown","generated",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":163,"publishedAt":47},"01M70P10S4SWMZQ8BJAFBFDRW3","youtube","Launch the YouTube Creator Agent to auto-generate videos.\nUse when: user types \u002Fyoutube, wants to create YouTube videos,\ngenerate video content, or upload to YouTube.\nKeywords: youtube, video, 视频, 创作视频, 发布视频","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube\u002FSKILL.md",[159,160],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":161,"categoryName":162,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000036","内容创作者",[155,164,165,166,167,168,169,170,142,171,172,173,33,174,175,176,42,177],"user","content","视频","creator","generate video","videos","launch","video content","create","upload","use","auto","types","创作",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":47},"01M70P0YE95D8BACZCGV8BK90K","youtube-script-sync","Sync, update, or optimize Tony's GetNote knowledge base `Youtube视频逐字稿` by turning each parent recording or first-person idea note into an idempotent child note containing a directly recordable YouTube long-video or Shorts script. Use when the user says “更新\u002F同步\u002F优化 Youtube视频逐字稿知识库”, or says “更新\u002F同步\u002F优化该知识库” while this knowledge base is the active context.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-script-sync\u002FSKILL.md",[184],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[186,187,155,188,189,42,190,191,192,193,194,195,196,197,140,198,33,174,134,199],"sync","script","youtube script sync","recording","shorts","first","base","person","update","active","child","optimize","idempotent","long",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":47},"01M70P0WMV5XT0EEC0N445PW07","writing-skills","How to write SKILL.md files that trigger reliably and teach effectively. Use when creating, improving, or reviewing Claude Code skills.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-skills\u002FSKILL.md",[206,207,208,209],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},{"categoryNo":161,"categoryName":162,"taxonomyType":131,"taxonomyTypeDesc":132},[211,119,212,213,214,215,150,33,174,216,217,218,219],"writing skills","how","teach","write skill","write","claude code skills","files","how to","trigger",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":228,"publishedAt":47},"01M70P0SJFR4YVJQBGMHKGSKXT","writing-rules","How to write .claude\u002Frules\u002F files that Claude actually follows. Use when creating, improving, or reviewing project rules.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-rules\u002FSKILL.md",[226,227],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":161,"categoryName":162,"taxonomyType":131,"taxonomyTypeDesc":132},[229,119,230,138,231,217,33,212,215,174,218],"Rules","writing rules","project rules"]