[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6R15QSASG1CFSWZD6ZT34DY":3,"skill-same:01M6R15QSASG1CFSWZD6ZT34DY":98},{"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":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6R15QSASG1CFSWZD6ZT34DY","senior-data-engineer",null,"World-class data engineering skill for building scalable data pipelines, ETL\u002FELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"senior","data engineer","data","engineer","senior data engineer","pipeline","when","expertise","implementing","Scalable","modeling","world","stack","orchestration","etl","elt","data pipelines","data engineering","airflow","sql","workflows","2026-11-10 10:12:30",false,"---\nname: senior-data-engineer\ndescription: World-class data engineering skill for building scalable data pipelines, ETL\u002FELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.\n---\n\n# Senior Data Engineer\n\nWorld-class senior data 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\u002Fpipeline_orchestrator.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Fdata_quality_validator.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fetl_performance_optimizer.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. Data Pipeline Architecture\n\nComprehensive guide available in `references\u002Fdata_pipeline_architecture.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. Data Modeling Patterns\n\nComplete workflow documentation in `references\u002Fdata_modeling_patterns.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. Dataops Best Practices\n\nTechnical reference guide in `references\u002Fdataops_best_practices.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\u002Fdata_pipeline_architecture.md`\n- Implementation Guide: `references\u002Fdata_modeling_patterns.md`\n- Technical Reference: `references\u002Fdataops_best_practices.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\n","# Senior Data Engineer\n\nWorld-class senior data 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\u002Fpipeline_orchestrator.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Fdata_quality_validator.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fetl_performance_optimizer.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. Data Pipeline Architecture\n\nComprehensive guide available in `references\u002Fdata_pipeline_architecture.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. Data Modeling Patterns\n\nComplete workflow documentation in `references\u002Fdata_modeling_patterns.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. Dataops Best Practices\n\nTechnical reference guide in `references\u002Fdataops_best_practices.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\u002Fdata_pipeline_architecture.md`\n- Implementation Guide: `references\u002Fdata_modeling_patterns.md`\n- Technical Reference: `references\u002Fdataops_best_practices.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\n",{"name":5,"description":7},"main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-14 17:36:32",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill senior-data-engineer",{"tool":63,"command":64},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill senior-data-engineer",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill senior-data-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002FSKILL.md",true,[71,77,81,84,90,94],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fdata_modeling_patterns.md","REFERENCE","参考文档",1423,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Freferences\u002Fdata_modeling_patterns.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fdata_pipeline_architecture.md",1431,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Freferences\u002Fdata_pipeline_architecture.md",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":83},"references\u002Fdataops_best_practices.md","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Freferences\u002Fdataops_best_practices.md",{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"scripts\u002Fdata_quality_validator.py","SCRIPT","脚本",2797,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Fscripts\u002Fdata_quality_validator.py",{"path":91,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Fetl_performance_optimizer.py",2812,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Fscripts\u002Fetl_performance_optimizer.py",{"path":95,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":96,"sourceHtmlUrl":97},"scripts\u002Fpipeline_orchestrator.py",2794,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-engineer\u002Fscripts\u002Fpipeline_orchestrator.py",[99,133,159,186,212,237],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":104,"sourceHtmlUrl":105,"categories":106,"keywords":112,"publishedAt":45},"01M6R4ARN4W2VZRDERDTMN1KP0","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","use","single-file","ui components","multi","for","single",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":137,"sourceHtmlUrl":138,"categories":139,"keywords":144,"publishedAt":45},"01M6R45GXREM5DP8T08MAC788H","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[140,143],{"categoryNo":141,"categoryName":142,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[145,146,147,118,148,149,114,150,151,152,153,154,155,156,157,158,131],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":163,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":45},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,30],"think tank","think","design","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":190,"sourceHtmlUrl":191,"categories":192,"keywords":196,"publishedAt":45},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000023","Skill与智能体开发",[197,198,44,199,200,201,202,203,204,205,206,207,208,209,158,131,210,30,211],"skill creator","creator","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":218,"publishedAt":45},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[219,220,221,222,223,224,225,226,127,227,228,229,230,231,232,233,234,235,236,30],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":241,"sourceHtmlUrl":242,"categories":243,"keywords":250,"publishedAt":45},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[244,247],{"categoryNo":245,"categoryName":246,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":248,"categoryName":249,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000016","图像与多媒体",[251,252,253,198,254,255,256,257,202,127,182,209,184,258,259,131,260,30],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]