[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6Q0XYNM2565APVCQWGD4ADX":3,"skill-same:01M6Q0XYNM2565APVCQWGD4ADX":111},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":33,"publishedAt":54,"linkOnly":55,"markdownContent":56,"markdownBody":57,"frontMatter":58,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":6,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6Q0XYNM2565APVCQWGD4ADX","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.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002FSKILL.md",[17,22,25,30],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",{"categoryNo":31,"categoryName":32,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000033","数据分析师",[34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"data scientist","data","senior data scientist","business intelligence","testing","data-driven","expertise","advanced","scikit-learn","time","world","model","analysis","pandas","feature engineering","communication","learn","experiment design","sql","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","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","数据与分析\u002Fsenior-data-scientist",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill senior-data-scientist",{"tool":73,"command":74},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill senior-data-scientist",{"tool":76,"command":77},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill senior-data-scientist","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002FSKILL.md",true,[81,87,91,94,97,103,107],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"_meta.json","REFERENCE","参考文档",302,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002F_meta.json",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fexperiment_design_frameworks.md",1435,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Freferences\u002Fexperiment_design_frameworks.md",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":93},"references\u002Ffeature_engineering_patterns.md","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Freferences\u002Ffeature_engineering_patterns.md",{"path":95,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":96},"references\u002Fstatistical_methods_advanced.md","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Freferences\u002Fstatistical_methods_advanced.md",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002Fexperiment_designer.py","SCRIPT","脚本",2784,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Fscripts\u002Fexperiment_designer.py",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":105,"sourceHtmlUrl":106},"scripts\u002Ffeature_engineering_pipeline.py",2827,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Fscripts\u002Ffeature_engineering_pipeline.py",{"path":108,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Fmodel_evaluation_suite.py",2797,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F数据与分析\u002Fsenior-data-scientist\u002Fscripts\u002Fmodel_evaluation_suite.py",[112,136,168,179,189,218],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":54},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[114,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":137,"name":138,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":150,"publishedAt":54},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[141,144,147],{"categoryNo":142,"categoryName":143,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":148,"categoryName":149,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000030","开发工程师",[151,152,153,154,155,156,157,121,158,125,159,160,161,162,163,164,165,166,167],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":169,"name":170,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":176,"publishedAt":54},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[173,174,175],{"categoryNo":142,"categoryName":143,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":148,"categoryName":149,"taxonomyType":28,"taxonomyTypeDesc":29},[151,177,178,152,153,154,155,156,157,121,158,125,160,161,162,163,164,165,166,167],"api skill","zerion api skill",{"skillNo":180,"name":181,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":187,"publishedAt":54},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[184,185,186],{"categoryNo":142,"categoryName":143,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":148,"categoryName":149,"taxonomyType":28,"taxonomyTypeDesc":29},[154,151,188,152,153,155,156,121,158,125,159,160,161,162,163,164,165,166,167],"zerion api mcp",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":198,"publishedAt":54},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[195],{"categoryNo":196,"categoryName":197,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000040","财务",[199,200,201,202,203,204,205,206,207,208,209,210,211,212,43,213,214,215,216,217],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","currency","gbp","pip","market data","for",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":225,"publishedAt":54},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[224],{"categoryNo":196,"categoryName":197,"taxonomyType":28,"taxonomyTypeDesc":29},[199,200,202,226,227,228,229,230,231,210,232,233,234,235,236,237,238,239,167,216],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]