[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H7RN96TH2GH4M4KJCFHQGS":3,"skill-same:01M6H7RN96TH2GH4M4KJCFHQGS":62},{"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":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":46,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M6H7RN96TH2GH4M4KJCFHQGS","tensorflow-expert",null,"Use when implementing tensorflow functionality with production-grade patterns and safeguards.","0xharryriddle\u002Fcodex-field-kit","0xharryriddle","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F100945007?v=4","01M6H686JV0QKZYP2RWG6B2B8T","harryriddle","01M6H686PVZ8JGKWEG7B3GM5PD",3,"MIT","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills-hermes\u002Fcodex-agents\u002Ftensorflow-expert\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29],"tensorflow","expert","tensorflow expert","codex","Production","use","general","patterns","with","kit","implementing","2026-11-10 10:12:30",false,"---\nname: tensorflow-expert\ndescription: Use when implementing tensorflow functionality with production-grade patterns and safeguards.\nmetadata:\n  hermes:\n    tags: [codex-agent, general]\n    source: codex-field-kit\u002Fgeneral\n---\n\n# Tensorflow Expert\n\n## Focus Areas\n- Building neural network architectures using TensorFlow \n- Optimizing model performance and hyperparameter tuning\n- Implementing data preprocessing pipelines\n- Utilizing TensorFlow’s Dataset API for data loading\n- Deploying models to production using TensorFlow Serving\n- Performing transfer learning with pre-trained models\n- Implementing custom training loops with GradientTape\n- Managing GPU and TPU computation strategies \n- Creating models for computer vision, NLP, and other domains\n- Understanding TensorFlow’s execution modes (eager vs. graph)\n\n## Approach\n- Start with sequential models, move to functional API for complex architectures\n- Leverage TensorBoard for visualization and debugging\n- Use data augmentation techniques to enhance training datasets\n- Apply regularization techniques to prevent overfitting\n- Employ mixed precision training to speed up computation with minimal loss in precision\n- Optimize input pipelines for scalability and performance\n- Use callbacks for model checkpointing and learning rate scheduling\n- Conduct error analysis and iterate on model improvements\n- Perform cross-validation to evaluate model generalization\n- Implement robust testing frameworks for TensorFlow code\n\n## Quality Checklist\n- Ensure reproducibility by setting random seeds and ensuring environment consistency\n- Maintain well-documented code with clear function descriptions\n- Verify data integrity and ensure proper data preprocessing\n- Monitor training to detect and address overfitting or underfitting\n- Validate model accuracy and performance on unseen data\n- Ensure efficient use of hardware resources during training\n- Confirm model compatibility with TensorFlow Lite for mobile deployments\n- Validate input data shape and type consistency\n- Perform unit and integration testing for TensorFlow components\n- Periodically update dependencies to keep up with TensorFlow’s developments\n\n## Output\n- TensorFlow models with comprehensive training scripts\n- Configured training loops and evaluation metrics ready to deploy\n- Performance benchmarks comparing different architectures\n- Visualization artifacts using TensorBoard for analysis\n- Detailed notebooks demonstrating model training and predictions\n- Deployment-ready models compatible with TensorFlow Serving and TensorFlow Lite\n- Code snippets showcasing advanced TensorFlow functionalities\n- Compatibility with both CPU and GPU environments\n- Robust preprocessing pipelines for diverse datasets\n- Generated reports of model performance and analysis results\n\n","# Tensorflow Expert\n\n## Focus Areas\n- Building neural network architectures using TensorFlow \n- Optimizing model performance and hyperparameter tuning\n- Implementing data preprocessing pipelines\n- Utilizing TensorFlow’s Dataset API for data loading\n- Deploying models to production using TensorFlow Serving\n- Performing transfer learning with pre-trained models\n- Implementing custom training loops with GradientTape\n- Managing GPU and TPU computation strategies \n- Creating models for computer vision, NLP, and other domains\n- Understanding TensorFlow’s execution modes (eager vs. graph)\n\n## Approach\n- Start with sequential models, move to functional API for complex architectures\n- Leverage TensorBoard for visualization and debugging\n- Use data augmentation techniques to enhance training datasets\n- Apply regularization techniques to prevent overfitting\n- Employ mixed precision training to speed up computation with minimal loss in precision\n- Optimize input pipelines for scalability and performance\n- Use callbacks for model checkpointing and learning rate scheduling\n- Conduct error analysis and iterate on model improvements\n- Perform cross-validation to evaluate model generalization\n- Implement robust testing frameworks for TensorFlow code\n\n## Quality Checklist\n- Ensure reproducibility by setting random seeds and ensuring environment consistency\n- Maintain well-documented code with clear function descriptions\n- Verify data integrity and ensure proper data preprocessing\n- Monitor training to detect and address overfitting or underfitting\n- Validate model accuracy and performance on unseen data\n- Ensure efficient use of hardware resources during training\n- Confirm model compatibility with TensorFlow Lite for mobile deployments\n- Validate input data shape and type consistency\n- Perform unit and integration testing for TensorFlow components\n- Periodically update dependencies to keep up with TensorFlow’s developments\n\n## Output\n- TensorFlow models with comprehensive training scripts\n- Configured training loops and evaluation metrics ready to deploy\n- Performance benchmarks comparing different architectures\n- Visualization artifacts using TensorBoard for analysis\n- Detailed notebooks demonstrating model training and predictions\n- Deployment-ready models compatible with TensorFlow Serving and TensorFlow Lite\n- Code snippets showcasing advanced TensorFlow functionalities\n- Compatibility with both CPU and GPU environments\n- Robust preprocessing pipelines for diverse datasets\n- Generated reports of model performance and analysis results\n\n",{"name":5,"metadata":35,"description":7},{"hermes":36},{"tags":37,"source":39},[38,25],"codex-agent","codex-field-kit\u002Fgeneral","main","6c13f6f4ac5a3bc1e6b78cd2bb1704f122c4a73e","skills-hermes\u002Fcodex-agents\u002Ftensorflow-expert",0,"https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit","https:\u002F\u002Fgithub.com\u002F0xharryriddle","https:\u002F\u002Fharryriddle.vercel.app","2026-04-11 11:49:51",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add 0xharryriddle\u002Fcodex-field-kit --skill tensorflow-expert",{"tool":54,"command":55},"bunx","bunx skills add 0xharryriddle\u002Fcodex-field-kit --skill tensorflow-expert",{"tool":57,"command":58},"pnpm","pnpm dlx skills add 0xharryriddle\u002Fcodex-field-kit --skill tensorflow-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills-hermes\u002Fcodex-agents\u002Ftensorflow-expert\u002FSKILL.md",true,[],[63,95,114,124,149,176],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":74,"publishedAt":30},"01M6H7ZKE6MR33HC84QZ1TA6MV","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[69],{"categoryNo":70,"categoryName":71,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94],"react best practices","react","vercel react best practices","best practices","vercel","tasks","data fetching","triggers","pages","next","performance optimization","refactoring","Next.js","Optimization","code","guidelines","react components","components","performance","writing",{"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":101,"publishedAt":30},"01M6H7ZGC2WK8SEKYHWDNTMDA9","swarm-planner","[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Fswarm-planner\u002FSKILL.md",[],[102,103,104,105,106,107,108,109,110,111,112,113],"swarm","swarm planner","planner","parallel","multi-agent","only","execution","plans","dependency","for","multi","implementation",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":30},"01M6H7ZD93853RZCYQWH1D38TM","super-swarm-spark","Only to be triggered by explicit super-swarm-spark commands.","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Fsuper-swarm-spark\u002FSKILL.md",[],[121,102,122,107,123],"super swarm spark","spark","commands",{"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":130,"publishedAt":30},"01M6H7Z987QBY49JCCPT953EAZ","role-creator","Create and install Codex custom agent roles in ~\u002F.codex\u002Fconfig.toml, generate role config files, enforce supported keys, and guide users through required role inputs (model, reasoning effort, developer_instructions).","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Frole-creator\u002FSKILL.md",[],[131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,22,148],"role","creator","role creator","keys","users","roles","toml","enforce","install","guide","instructions","model","developer","reasoning","generate","create","config","files",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":158,"publishedAt":30},"01M6H7Z5TRPMDQ7YSZQXPN1G60","read-github","Read and search GitHub repository documentation via gitmcp.io MCP service.\n\n**WHEN TO USE:**\n- User provides a GitHub URL\n- User mentions a specific repo in owner\u002Frepo format\n- User asks \"what does this repo do?\", \"read the docs for X repo\", or similar\n- User wants to search code or docs within a repo","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Fread-github\u002FSKILL.md",[155],{"categoryNo":156,"categoryName":157,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000023","Skill与智能体开发",[159,160,161,89,162,163,164,165,111,166,167,168,169,170,171,24,172,173,174,175],"github","read github","read","documentation","docs","MCP","search","when to use","URL","via","io","service","do","user","repo","format","repository",{"skillNo":177,"name":104,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":30},"01M6H7Z215CTKYJ4Y1X6EQWN6M","Create comprehensive, phased implementation plans with sprints and atomic tasks. Use when user says: \"make a plan\", \"create a plan\", \"plan this out\", \"plan the implementation\", \"help me plan\", \"design a plan\", \"draft a plan\", \"write a plan\", \"outline the steps\", \"break this down into tasks\", \"what's the plan for\", or any similar planning request. Also triggers on explicit \"\u002Fplanner\" or \"\u002Fplan\" commands.","https:\u002F\u002Fgithub.com\u002F0xharryriddle\u002Fcodex-field-kit\u002Fblob\u002Fmain\u002Fskills\u002Fplanner\u002FSKILL.md",[],[104,182,111,183,27,184,82,109,123,185,186,24,187,188,189,190,191,146,80,192],"outline","atomic","sprints","draft","me","write","phased implementation","plan","help","planning","design"]