[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AETH2XM486PRBB1GFSMH3F":3,"skill-same:01M6AETH2XM486PRBB1GFSMH3F":123},{"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":37,"publishedAt":68,"linkOnly":69,"markdownContent":70,"markdownBody":71,"frontMatter":72,"refName":73,"commitSha":74,"skillDirectory":75,"forksCount":76,"repositoryHtmlUrl":77,"repositoryHomepageUrl":78,"ownerHtmlUrl":79,"ownerBlogUrl":80,"ownerDescription":81,"updatedAt":82,"install":83,"resources":96},"01M6AETH2XM486PRBB1GFSMH3F","senior-data-scientist",null,"World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A\u002FB testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002FSKILL.md",[18,23,26,29,34],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",{"categoryNo":35,"categoryName":36,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000033","数据分析师",[38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],"senior","senior data scientist","data","data scientist","engineering","when","business intelligence","data-driven","advanced","scikit-learn","time","world","model","analysis","Evaluation","testing","time series","pandas","feature engineering","communication","learn","experiment design","data science","sql","analytics","experiments","causal inference","inference","python","design","2026-11-10 10:12:30",false,"---\nname: senior-data-scientist\ndescription: World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A\u002FB testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.\n---\n\n# Senior Data Scientist\n\nWorld-class senior data scientist 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\u002Fexperiment_designer.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Ffeature_engineering_pipeline.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fmodel_evaluation_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. Statistical Methods Advanced\n\nComprehensive guide available in `references\u002Fstatistical_methods_advanced.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. Experiment Design Frameworks\n\nComplete workflow documentation in `references\u002Fexperiment_design_frameworks.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. Feature Engineering Patterns\n\nTechnical reference guide in `references\u002Ffeature_engineering_patterns.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\u002Fstatistical_methods_advanced.md`\n- Implementation Guide: `references\u002Fexperiment_design_frameworks.md`\n- Technical Reference: `references\u002Ffeature_engineering_patterns.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 Data Scientist\n\nWorld-class senior data scientist 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\u002Fexperiment_designer.py --input data\u002F --output results\u002F\n\n# Core Tool 2  \npython scripts\u002Ffeature_engineering_pipeline.py --target project\u002F --analyze\n\n# Core Tool 3\npython scripts\u002Fmodel_evaluation_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. Statistical Methods Advanced\n\nComprehensive guide available in `references\u002Fstatistical_methods_advanced.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. Experiment Design Frameworks\n\nComplete workflow documentation in `references\u002Fexperiment_design_frameworks.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. Feature Engineering Patterns\n\nTechnical reference guide in `references\u002Ffeature_engineering_patterns.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\u002Fstatistical_methods_advanced.md`\n- Implementation Guide: `references\u002Fexperiment_design_frameworks.md`\n- Technical Reference: `references\u002Ffeature_engineering_patterns.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","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist",3909,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 11:53:30",{"source":8,"skillName":5,"commands":84,"prompt":94,"zipAvailable":95},[85,88,91],{"tool":86,"command":87},"npx","npx skills add davila7\u002Fclaude-code-templates --skill senior-data-scientist",{"tool":89,"command":90},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill senior-data-scientist",{"tool":92,"command":93},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill senior-data-scientist","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002FSKILL.md",true,[97,103,106,109,115,119],{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fexperiment_design_frameworks.md","REFERENCE","参考文档",1435,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Freferences\u002Fexperiment_design_frameworks.md",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":105},"references\u002Ffeature_engineering_patterns.md","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Freferences\u002Ffeature_engineering_patterns.md",{"path":107,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":108},"references\u002Fstatistical_methods_advanced.md","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Freferences\u002Fstatistical_methods_advanced.md",{"path":110,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fexperiment_designer.py","SCRIPT","脚本",2784,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Fscripts\u002Fexperiment_designer.py",{"path":116,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Ffeature_engineering_pipeline.py",2827,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Fscripts\u002Ffeature_engineering_pipeline.py",{"path":120,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fmodel_evaluation_suite.py",2797,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fsenior-data-scientist\u002Fscripts\u002Fmodel_evaluation_suite.py",[124,151,178,206,241,264],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":131,"publishedAt":68},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[130],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[132,133,134,135,136,137,138,139,140,43,141,142,143,144,145,146,147,148,149,150],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":68},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[157],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[159,160,161,162,163,164,165,166,167,168,169,43,170,171,172,173,174,175,176,177],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"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":189,"publishedAt":68},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[184,187,188],{"categoryNo":185,"categoryName":186,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":35,"categoryName":36,"taxonomyType":32,"taxonomyTypeDesc":33},[190,191,192,61,193,194,195,137,196,197,198,199,200,201,202,140,203,204,43,205],"readonly","guard","sql guard","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":211,"sourceHtmlUrl":212,"categories":213,"keywords":217,"publishedAt":68},"01M6AFY6F7CC2BBZP1QD6VTNVC","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[214],{"categoryNo":215,"categoryName":216,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,137,240],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":245,"sourceHtmlUrl":246,"categories":247,"keywords":252,"publishedAt":68},"01M6AFWF33SFV9Z3SF4PEEE4BF","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[248,251],{"categoryNo":249,"categoryName":250,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":215,"categoryName":216,"taxonomyType":21,"taxonomyTypeDesc":22},[253,53,254,255,256,219,257,228,229,258,259,260,261,137,262,142,263],"webapp testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":265,"name":266,"nameZh":6,"description":267,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":268,"sourceHtmlUrl":269,"categories":270,"keywords":278,"publishedAt":68},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[271,274,275],{"categoryNo":272,"categoryName":273,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":276,"categoryName":277,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000036","内容创作者",[279,280,281,282,283,284,40,285,221,286,231,170,287,288,289,290,291,201,292,43,293,51,294,295,296,148,297],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]