[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFQ7WPC4E8YTWQ3ZA1GSH4":3,"skill-same:01M6AFQ7WPC4E8YTWQ3ZA1GSH4":159},{"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":26,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6AFQ7WPC4E8YTWQ3ZA1GSH4","transformers",null,"This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftransformers\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"Transformers","text","when","for","object detection","processing","natural language processing","natural language","translation","vision","question","speech recognition","computer","image","Audio","classification","tuning","detection","Computer Vision","Fine-Tuning","multimodal","speech","tasks","2026-11-10 10:12:30",false,"---\nname: transformers\ndescription: This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.\n---\n\n# Transformers\n\n## Overview\n\nThe Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.\n\n## Installation\n\nInstall transformers and core dependencies:\n\n```bash\nuv pip install torch transformers datasets evaluate accelerate\n```\n\nFor vision tasks, add:\n```bash\nuv pip install timm pillow\n```\n\nFor audio tasks, add:\n```bash\nuv pip install librosa soundfile\n```\n\n## Authentication\n\nMany models on the Hugging Face Hub require authentication. Set up access:\n\n```python\nfrom huggingface_hub import login\nlogin()  # Follow prompts to enter token\n```\n\nOr set environment variable:\n```bash\nexport HUGGINGFACE_TOKEN=\"your_token_here\"\n```\n\nGet tokens at: https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Ftokens\n\n## Quick Start\n\nUse the Pipeline API for fast inference without manual configuration:\n\n```python\nfrom transformers import pipeline\n\n# Text generation\ngenerator = pipeline(\"text-generation\", model=\"gpt2\")\nresult = generator(\"The future of AI is\", max_length=50)\n\n# Text classification\nclassifier = pipeline(\"text-classification\")\nresult = classifier(\"This movie was excellent!\")\n\n# Question answering\nqa = pipeline(\"question-answering\")\nresult = qa(question=\"What is AI?\", context=\"AI is artificial intelligence...\")\n```\n\n## Core Capabilities\n\n### 1. Pipelines for Quick Inference\n\nUse for simple, optimized inference across many tasks. Supports text generation, classification, NER, question answering, summarization, translation, image classification, object detection, audio classification, and more.\n\n**When to use**: Quick prototyping, simple inference tasks, no custom preprocessing needed.\n\nSee `references\u002Fpipelines.md` for comprehensive task coverage and optimization.\n\n### 2. Model Loading and Management\n\nLoad pre-trained models with fine-grained control over configuration, device placement, and precision.\n\n**When to use**: Custom model initialization, advanced device management, model inspection.\n\nSee `references\u002Fmodels.md` for loading patterns and best practices.\n\n### 3. Text Generation\n\nGenerate text with LLMs using various decoding strategies (greedy, beam search, sampling) and control parameters (temperature, top-k, top-p).\n\n**When to use**: Creative text generation, code generation, conversational AI, text completion.\n\nSee `references\u002Fgeneration.md` for generation strategies and parameters.\n\n### 4. Training and Fine-Tuning\n\nFine-tune pre-trained models on custom datasets using the Trainer API with automatic mixed precision, distributed training, and logging.\n\n**When to use**: Task-specific model adaptation, domain adaptation, improving model performance.\n\nSee `references\u002Ftraining.md` for training workflows and best practices.\n\n### 5. Tokenization\n\nConvert text to tokens and token IDs for model input, with padding, truncation, and special token handling.\n\n**When to use**: Custom preprocessing pipelines, understanding model inputs, batch processing.\n\nSee `references\u002Ftokenizers.md` for tokenization details.\n\n## Common Patterns\n\n### Pattern 1: Simple Inference\nFor straightforward tasks, use pipelines:\n```python\npipe = pipeline(\"task-name\", model=\"model-id\")\noutput = pipe(input_data)\n```\n\n### Pattern 2: Custom Model Usage\nFor advanced control, load model and tokenizer separately:\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained(\"model-id\")\nmodel = AutoModelForCausalLM.from_pretrained(\"model-id\", device_map=\"auto\")\n\ninputs = tokenizer(\"text\", return_tensors=\"pt\")\noutputs = model.generate(**inputs, max_new_tokens=100)\nresult = tokenizer.decode(outputs[0])\n```\n\n### Pattern 3: Fine-Tuning\nFor task adaptation, use Trainer:\n```python\nfrom transformers import Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Fresults\",\n    num_train_epochs=3,\n    per_device_train_batch_size=8,\n)\n\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n)\n\ntrainer.train()\n```\n\n## Reference Documentation\n\nFor detailed information on specific components:\n- **Pipelines**: `references\u002Fpipelines.md` - All supported tasks and optimization\n- **Models**: `references\u002Fmodels.md` - Loading, saving, and configuration\n- **Generation**: `references\u002Fgeneration.md` - Text generation strategies and parameters\n- **Training**: `references\u002Ftraining.md` - Fine-tuning with Trainer API\n- **Tokenizers**: `references\u002Ftokenizers.md` - Tokenization and preprocessing\n","# Transformers\n\n## Overview\n\nThe Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.\n\n## Installation\n\nInstall transformers and core dependencies:\n\n```bash\nuv pip install torch transformers datasets evaluate accelerate\n```\n\nFor vision tasks, add:\n```bash\nuv pip install timm pillow\n```\n\nFor audio tasks, add:\n```bash\nuv pip install librosa soundfile\n```\n\n## Authentication\n\nMany models on the Hugging Face Hub require authentication. Set up access:\n\n```python\nfrom huggingface_hub import login\nlogin()  # Follow prompts to enter token\n```\n\nOr set environment variable:\n```bash\nexport HUGGINGFACE_TOKEN=\"your_token_here\"\n```\n\nGet tokens at: https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Ftokens\n\n## Quick Start\n\nUse the Pipeline API for fast inference without manual configuration:\n\n```python\nfrom transformers import pipeline\n\n# Text generation\ngenerator = pipeline(\"text-generation\", model=\"gpt2\")\nresult = generator(\"The future of AI is\", max_length=50)\n\n# Text classification\nclassifier = pipeline(\"text-classification\")\nresult = classifier(\"This movie was excellent!\")\n\n# Question answering\nqa = pipeline(\"question-answering\")\nresult = qa(question=\"What is AI?\", context=\"AI is artificial intelligence...\")\n```\n\n## Core Capabilities\n\n### 1. Pipelines for Quick Inference\n\nUse for simple, optimized inference across many tasks. Supports text generation, classification, NER, question answering, summarization, translation, image classification, object detection, audio classification, and more.\n\n**When to use**: Quick prototyping, simple inference tasks, no custom preprocessing needed.\n\nSee `references\u002Fpipelines.md` for comprehensive task coverage and optimization.\n\n### 2. Model Loading and Management\n\nLoad pre-trained models with fine-grained control over configuration, device placement, and precision.\n\n**When to use**: Custom model initialization, advanced device management, model inspection.\n\nSee `references\u002Fmodels.md` for loading patterns and best practices.\n\n### 3. Text Generation\n\nGenerate text with LLMs using various decoding strategies (greedy, beam search, sampling) and control parameters (temperature, top-k, top-p).\n\n**When to use**: Creative text generation, code generation, conversational AI, text completion.\n\nSee `references\u002Fgeneration.md` for generation strategies and parameters.\n\n### 4. Training and Fine-Tuning\n\nFine-tune pre-trained models on custom datasets using the Trainer API with automatic mixed precision, distributed training, and logging.\n\n**When to use**: Task-specific model adaptation, domain adaptation, improving model performance.\n\nSee `references\u002Ftraining.md` for training workflows and best practices.\n\n### 5. Tokenization\n\nConvert text to tokens and token IDs for model input, with padding, truncation, and special token handling.\n\n**When to use**: Custom preprocessing pipelines, understanding model inputs, batch processing.\n\nSee `references\u002Ftokenizers.md` for tokenization details.\n\n## Common Patterns\n\n### Pattern 1: Simple Inference\nFor straightforward tasks, use pipelines:\n```python\npipe = pipeline(\"task-name\", model=\"model-id\")\noutput = pipe(input_data)\n```\n\n### Pattern 2: Custom Model Usage\nFor advanced control, load model and tokenizer separately:\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained(\"model-id\")\nmodel = AutoModelForCausalLM.from_pretrained(\"model-id\", device_map=\"auto\")\n\ninputs = tokenizer(\"text\", return_tensors=\"pt\")\noutputs = model.generate(**inputs, max_new_tokens=100)\nresult = tokenizer.decode(outputs[0])\n```\n\n### Pattern 3: Fine-Tuning\nFor task adaptation, use Trainer:\n```python\nfrom transformers import Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Fresults\",\n    num_train_epochs=3,\n    per_device_train_batch_size=8,\n)\n\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n)\n\ntrainer.train()\n```\n\n## Reference Documentation\n\nFor detailed information on specific components:\n- **Pipelines**: `references\u002Fpipelines.md` - All supported tasks and optimization\n- **Models**: `references\u002Fmodels.md` - Loading, saving, and configuration\n- **Generation**: `references\u002Fgeneration.md` - Text generation strategies and parameters\n- **Training**: `references\u002Ftraining.md` - Fine-tuning with Trainer API\n- **Tokenizers**: `references\u002Ftokenizers.md` - Tokenization and preprocessing\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftransformers",3931,"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","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add davila7\u002Fclaude-code-templates --skill transformers",{"tool":71,"command":72},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill transformers",{"tool":74,"command":75},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill 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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",[166],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[168,169,170,171,172,30,173,174,175,29,176,177,178,179,180,181,182,183,184,185],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":193,"publishedAt":50},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. 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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",[192],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[194,195,196,197,198,199,200,201,202,203,204,29,205,206,207,208,209,210,211,212],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":228,"publishedAt":50},"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",[219,222,223],{"categoryNo":220,"categoryName":221,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":224,"categoryName":225,"taxonomyType":226,"taxonomyTypeDesc":227},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[229,230,231,232,233,234,235,30,236,237,238,239,240,241,242,175,243,244,29,245],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":250,"licenseFileUrl":251,"sourceHtmlUrl":252,"categories":253,"keywords":257,"publishedAt":50},"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",[254],{"categoryNo":255,"categoryName":256,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,30,280],"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":282,"name":283,"nameZh":6,"description":284,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":250,"licenseFileUrl":285,"sourceHtmlUrl":286,"categories":287,"keywords":292,"publishedAt":50},"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",[288,291],{"categoryNo":289,"categoryName":290,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":255,"categoryName":256,"taxonomyType":21,"taxonomyTypeDesc":22},[293,294,295,296,297,259,298,268,269,299,300,301,302,30,303,177,304],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":306,"name":307,"nameZh":6,"description":308,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":250,"licenseFileUrl":309,"sourceHtmlUrl":310,"categories":311,"keywords":319,"publishedAt":50},"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",[312,315,316],{"categoryNo":313,"categoryName":314,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":317,"categoryName":318,"taxonomyType":226,"taxonomyTypeDesc":227},"01JBSEEDCAT000000000000036","内容创作者",[320,321,322,323,324,325,326,327,261,328,271,205,329,330,331,332,333,241,334,29,335,336,337,338,339,183,340],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]