[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FCHRKDPKVN6NVDAEWCAZ2R":3,"skill-same:01M6FCHRKDPKVN6NVDAEWCAZ2R":103},{"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":17,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":26,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6FCHRKDPKVN6NVDAEWCAZ2R","transformers-js",null,"Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript\u002FTypeScript.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"transformers js","Transformers","js","learning","blob","tree","machine","critical","main","models","Art","use","typescript","HuggingFace","https","license","state","run","official","javascript","machine learning","github","2026-11-10 10:12:30",false,"---\nname: transformers-js\ndescription: \"Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript\u002FTypeScript.\"\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Ftransformers-js\nsource_repo: huggingface\u002Fskills\nsource_type: official\ndate_added: 2026-07-01\nlicense: Apache-2.0\nlicense_source: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# Transformers.js - Machine Learning for JavaScript\n\nTransformers.js enables running state-of-the-art machine learning models directly in JavaScript across browsers and server-side runtimes (Node.js, Bun, Deno), with no Python server required.\n\n## Detailed Guide\n\nRead [the detailed guide](references\u002Fdetailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Run ML models for text analysis, generation, or translation in JavaScript\n- Perform image classification, object detection, or segmentation\n- Implement speech recognition or audio processing\n- Build multimodal AI applications (text-to-image, image-to-text, etc.)\n- Run models client-side in the browser without a backend\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n\n### Runtime and model boundaries\n\nThe references mix stable APIs with v4\u002Fmain-only examples (including ModelRegistry and `env.fetch`). Pin a tested library version and check its matching documentation; the unversioned main docs may require a source build. Model repositories need compatible ONNX exports, tokenizer\u002Fconfig files and supported backend operators; an `onnx\u002F` folder alone does not prove compatibility. Respect each model's license and gated-access terms. Keep tokens server-side. Offline operation also requires local runtime\u002FWASM assets; cache presence alone is insufficient.\n\nExamples are illustrative and were not executed against downloaded models in this review. Larger models and lower precision do not guarantee better quality or faster inference. Browser unload handlers cannot guarantee asynchronous disposal; await explicit cleanup while the application is active. React examples require cancellation\u002Funmount handling for pending loads and inference. Server deployments need authentication, input\u002Frate limits, readiness checks and bounded shutdown before exposure.\n\nModified for catalog review on 2026-10-23; upstream attribution and Apache-2.0 metadata retained.\n","# Transformers.js - Machine Learning for JavaScript\n\nTransformers.js enables running state-of-the-art machine learning models directly in JavaScript across browsers and server-side runtimes (Node.js, Bun, Deno), with no Python server required.\n\n## Detailed Guide\n\nRead [the detailed guide](references\u002Fdetailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Run ML models for text analysis, generation, or translation in JavaScript\n- Perform image classification, object detection, or segmentation\n- Implement speech recognition or audio processing\n- Build multimodal AI applications (text-to-image, image-to-text, etc.)\n- Run models client-side in the browser without a backend\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n\n### Runtime and model boundaries\n\nThe references mix stable APIs with v4\u002Fmain-only examples (including ModelRegistry and `env.fetch`). Pin a tested library version and check its matching documentation; the unversioned main docs may require a source build. Model repositories need compatible ONNX exports, tokenizer\u002Fconfig files and supported backend operators; an `onnx\u002F` folder alone does not prove compatibility. Respect each model's license and gated-access terms. Keep tokens server-side. Offline operation also requires local runtime\u002FWASM assets; cache presence alone is insufficient.\n\nExamples are illustrative and were not executed against downloaded models in this review. Larger models and lower precision do not guarantee better quality or faster inference. Browser unload handlers cannot guarantee asynchronous disposal; await explicit cleanup while the application is active. React examples require cancellation\u002Funmount handling for pending loads and inference. Server deployments need authentication, input\u002Frate limits, readiness checks and bounded shutdown before exposure.\n\nModified for catalog review on 2026-10-23; upstream attribution and Apache-2.0 metadata retained.\n",{"name":5,"risk":25,"source":45,"license":14,"date_added":46,"description":7,"source_repo":47,"source_type":36,"license_source":48},"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Ftransformers-js",1782864000000,"huggingface\u002Fskills","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fantigravity-transformers-js",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-13 14:20:47",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add boisenoise\u002Fskills-collections --skill 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transformers-js","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002FSKILL.md",true,[69,75,79,83,87,91,95,99],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002FCACHE.md","REFERENCE","参考文档",9700,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FCACHE.md",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002FCONFIGURATION.md",11425,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FCONFIGURATION.md",{"path":80,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fdetailed-guide.md",23865,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002Fdetailed-guide.md",{"path":84,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002FEXAMPLES.md",16542,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FEXAMPLES.md",{"path":88,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002FMODEL_ARCHITECTURES.md",5590,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FMODEL_ARCHITECTURES.md",{"path":92,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002FMODEL_REGISTRY.md",6448,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FMODEL_REGISTRY.md",{"path":96,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002FPIPELINE_OPTIONS.md",15659,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FPIPELINE_OPTIONS.md",{"path":100,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002FTEXT_GENERATION.md",8172,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-transformers-js\u002Freferences\u002FTEXT_GENERATION.md",[104,131,155,180,199,222],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":108,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":40},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[112,113,114,36,115,116,117,118,119,120,121,122,123,124,125,25,126,127,128,129,130],"web","access","web access","development","browser","search","data","Tools","read","fetch","only","youtube","free tools","platform","free","cover","normal","when","2026",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":135,"sourceHtmlUrl":136,"categories":137,"keywords":143,"publishedAt":40},"01M6WNF9XAYAB2CZDDJBAKNM93","treg","Use the Treg CLI or MCP catalog to discover, price and call authorized external tools with team-scoped credentials and a user-approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[138],{"categoryNo":139,"categoryName":140,"taxonomyType":141,"taxonomyTypeDesc":142},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[133,144,145,39,36,115,33,119,146,147,148,149,29,150,151,25,152,153,154,22,130],"MCP","cli","Budget","credentials","team","catalog","call","discover","external tools","with","scoped",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":159,"sourceHtmlUrl":160,"categories":161,"keywords":167,"publishedAt":40},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[162],{"categoryNo":163,"categoryName":164,"taxonomyType":165,"taxonomyTypeDesc":166},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[157,39,36,33,32,168,169,170,171,172,25,173,174,175,153,176,177,178,179,22,133,130],"email","enrichment","business","evidence","phone","ranking","post","job description","for","person","candidate","reason",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":184,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":40},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[188,189,190,39,36,33,191,192,32,193,146,194,195,196,171,25,197,198,22,133,130],"growth","rea","rea growth","produce","content","playbook","marketing","analyze","public","observations","company",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":206,"publishedAt":40},"01M6WN2EVE3RQRGWAKBT4S7YT2","make-ugc","Create original AI UGC-style product videos through Treg: research hooks, approve a licensed presenter and voice, generate clips, add captions and report costs.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FSKILL.md",[],[207,208,39,209,36,210,33,211,212,213,214,215,25,216,217,218,219,220,221,22],"make","make ugc","research","create","Video","Captions","ads","short","hooks","head","style","niche","trending","talking-head","approve",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":226,"sourceHtmlUrl":227,"categories":228,"keywords":229,"publishedAt":40},"01M6WN10ARAA63Y8K1YNGXJ2RM","lead-signals","Find and qualify public buying signals through Treg, rank fitting business accounts and optionally enrich authorized business contact information.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FSKILL.md",[],[230,231,232,39,36,33,233,234,170,235,236,25,237,238,239,240,241,242,243,22],"signals","lead","lead signals","how","find","ready","as","how to","qualify","people","information","enrich","changes","funding"]