[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JJP0KCXV4HMB174D6TYY3N":3,"skill-same:01M6JJP0KCXV4HMB174D6TYY3N":94},{"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":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6JJP0KCXV4HMB174D6TYY3N","senior-ml-engineer",null,"World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"ml","engineer","ml engineer","senior ml engineer","expertise","agentic","implementing","Scalable","feature","world","ml engineering","model","tuning","monitoring","infrastructure","rag","pytorch","tensorflow","llms","agentic ai","2026-11-10 10:12:30",false,"---\nname: senior-ml-engineer\ndescription: World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.\n---\n\n# Senior ML\u002FAI Engineer\n\nWorld-class senior ml\u002Fai 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\u002Fmodel_deployment_pipeline.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Frag_system_builder.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fml_monitoring_suite.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. Mlops Production Patterns\n\nComprehensive guide available in `references\u002Fmlops_production_patterns.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. Llm Integration Guide\n\nComplete workflow documentation in `references\u002Fllm_integration_guide.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. Rag System Architecture\n\nTechnical reference guide in `references\u002Frag_system_architecture.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\u002Fmlops_production_patterns.md`\n- Implementation Guide: `references\u002Fllm_integration_guide.md`\n- Technical Reference: `references\u002Frag_system_architecture.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 ML\u002FAI Engineer\n\nWorld-class senior ml\u002Fai 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\u002Fmodel_deployment_pipeline.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Frag_system_builder.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fml_monitoring_suite.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. Mlops Production Patterns\n\nComprehensive guide available in `references\u002Fmlops_production_patterns.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. Llm Integration Guide\n\nComplete workflow documentation in `references\u002Fllm_integration_guide.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. Rag System Architecture\n\nTechnical reference guide in `references\u002Frag_system_architecture.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\u002Fmlops_production_patterns.md`\n- Implementation Guide: `references\u002Fllm_integration_guide.md`\n- Technical Reference: `references\u002Frag_system_architecture.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","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill senior-ml-engineer",{"tool":58,"command":59},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill senior-ml-engineer",{"tool":61,"command":62},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill senior-ml-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002FSKILL.md",true,[66,72,76,80,86,90],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Fllm_integration_guide.md","REFERENCE","参考文档",1421,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Freferences\u002Fllm_integration_guide.md",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fmlops_production_patterns.md",1429,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Freferences\u002Fmlops_production_patterns.md",{"path":77,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Frag_system_architecture.md",1425,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Freferences\u002Frag_system_architecture.md",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fml_monitoring_suite.py","SCRIPT","脚本",2782,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Fscripts\u002Fml_monitoring_suite.py",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"scripts\u002Fmodel_deployment_pipeline.py",2812,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Fscripts\u002Fmodel_deployment_pipeline.py",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Frag_system_builder.py",2777,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-ml-engineer\u002Fscripts\u002Frag_system_builder.py",[95,127,148,172,192,218],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":106,"publishedAt":39},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","platform","apps","how to","No-Code","which","for","spawner",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":39},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[129,134,135,136,137,138,139,140,141,142,143,144,117,145,146,147],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":39},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[154],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[112,113,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,125,126],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":39},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[179,180,181,182,183,159,184,160,161,185,117,186,187,188,189,190,191,171,125,126],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":39},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[198],{"categoryNo":199,"categoryName":200,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000021","资料检索与研究",[202,203,204,171,205,112,206,207,208,209,210,211,117,212,213,214,215,216,217,125],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":228,"publishedAt":39},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[224,227],{"categoryNo":225,"categoryName":226,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[112,229,166,230,108,231,232,159,233,207,203,234,235,236,237,238,239,240,241,242],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]