[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71GGBVEPPCBB6DMVXVHFF4T":3,"skill-same:01M71GGBVEPPCBB6DMVXVHFF4T":91},{"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":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M71GGBVEPPCBB6DMVXVHFF4T","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.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"Transformers","speech recognition","when","for","object detection","processing","natural language processing","natural language","translation","vision","question","image","computer","text","Audio","classification","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","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add JSGforever\u002Fskillguard --skill transformers",{"tool":61,"command":62},"bunx","bunx skills add JSGforever\u002Fskillguard --skill transformers",{"tool":64,"command":65},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill transformers","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002FSKILL.md",true,[69,75,79,83,87],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fgeneration.md","REFERENCE","参考文档",9340,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002Freferences\u002Fgeneration.md",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fmodels.md",8648,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002Freferences\u002Fmodels.md",{"path":80,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fpipelines.md",8940,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002Freferences\u002Fpipelines.md",{"path":84,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Ftokenizers.md",9904,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002Freferences\u002Ftokenizers.md",{"path":88,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Ftraining.md",10365,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Ftransformers\u002Freferences\u002Ftraining.md",[92,121,137,164,186,205],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":101,"publishedAt":43},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[102,103,104,105,106,107,108,109,110,111,112,113,25,114,115,116,117,118,119,120],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":125,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":133,"publishedAt":43},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[128],{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[134,135,136],"data","data exfiltrator","files",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":143,"publishedAt":43},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":43},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[153,174,161,162,175,176,177,178,179,180,144,181,182,157,159,183,160,163,184,185],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":193,"publishedAt":43},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[192],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},[194,163,195,196,28,197,153,144,198,199,200,159,160,161,201,162,202,36,203,204],"llm streaming response handler","handler","error","retry","chat ui","error recovery","critical","batch","display","server","read",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":218,"publishedAt":43},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[211,214,217],{"categoryNo":212,"categoryName":213,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":215,"categoryName":216,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":99,"categoryName":100,"taxonomyType":20,"taxonomyTypeDesc":21},[219,220,221,222,223,224,225,151,179,226,227,228,160,185,229,204,230,231,232,233],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]