[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71GD928VX0FKJTQ38KX1FJ2":3,"skill-same:01M71GD928VX0FKJTQ38KX1FJ2":113},{"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},"01M71GD928VX0FKJTQ38KX1FJ2","pytorch-lightning",null,"Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU\u002FTPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.","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\u002Fpytorch-lightning\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","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],"pytorch","lightning","pytorch lightning","data","Scalable","Callbacks","DDP","FSDP","neural network","deep","organize","configure","tpu","network","logging","data pipelines","framework","code","training","deep learning","2026-11-10 10:12:30",false,"---\nname: pytorch-lightning\ndescription: \"Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU\u002FTPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.\"\n---\n\n# PyTorch Lightning\n\n## Overview\n\nPyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs\u002FTPUs.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Building, training, or deploying neural networks using PyTorch Lightning\n- Organizing PyTorch code into LightningModules\n- Configuring Trainers for multi-GPU\u002FTPU training\n- Implementing data pipelines with LightningDataModules\n- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)\n- Structuring deep learning projects professionally\n\n## Core Capabilities\n\n### 1. LightningModule - Model Definition\n\nOrganize PyTorch models into six logical sections:\n\n1. **Initialization** - `__init__()` and `setup()`\n2. **Training Loop** - `training_step(batch, batch_idx)`\n3. **Validation Loop** - `validation_step(batch, batch_idx)`\n4. **Test Loop** - `test_step(batch, batch_idx)`\n5. **Prediction** - `predict_step(batch, batch_idx)`\n6. **Optimizer Configuration** - `configure_optimizers()`\n\n**Quick template reference:** See `scripts\u002Ftemplate_lightning_module.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Flightning_module.md` for comprehensive method documentation, hooks, properties, and best practices.\n\n### 2. Trainer - Training Automation\n\nThe Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:\n\n- Multi-GPU\u002FTPU support with strategy selection (DDP, FSDP, DeepSpeed)\n- Automatic mixed precision training\n- Gradient accumulation and clipping\n- Checkpointing and early stopping\n- Progress bars and logging\n\n**Quick setup reference:** See `scripts\u002Fquick_trainer_setup.py` for common Trainer configurations.\n\n**Detailed documentation:** Read `references\u002Ftrainer.md` for all parameters, methods, and configuration options.\n\n### 3. LightningDataModule - Data Pipeline Organization\n\nEncapsulate all data processing steps in a reusable class:\n\n1. `prepare_data()` - Download and process data (single-process)\n2. `setup()` - Create datasets and apply transforms (per-GPU)\n3. `train_dataloader()` - Return training DataLoader\n4. `val_dataloader()` - Return validation DataLoader\n5. `test_dataloader()` - Return test DataLoader\n\n**Quick template reference:** See `scripts\u002Ftemplate_datamodule.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Fdata_module.md` for method details and usage patterns.\n\n### 4. Callbacks - Extensible Training Logic\n\nAdd custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:\n\n- **ModelCheckpoint** - Save best\u002Flatest models\n- **EarlyStopping** - Stop when metrics plateau\n- **LearningRateMonitor** - Track LR scheduler changes\n- **BatchSizeFinder** - Auto-determine optimal batch size\n\n**Detailed documentation:** Read `references\u002Fcallbacks.md` for built-in callbacks and custom callback creation.\n\n### 5. Logging - Experiment Tracking\n\nIntegrate with multiple logging platforms:\n\n- TensorBoard (default)\n- Weights & Biases (WandbLogger)\n- MLflow (MLFlowLogger)\n- Neptune (NeptuneLogger)\n- Comet (CometLogger)\n- CSV (CSVLogger)\n\nLog metrics using `self.log(\"metric_name\", value)` in any LightningModule method.\n\n**Detailed documentation:** Read `references\u002Flogging.md` for logger setup and configuration.\n\n### 6. Distributed Training - Scale to Multiple Devices\n\nChoose the right strategy based on model size:\n\n- **DDP** - For models \u003C500M parameters (ResNet, smaller transformers)\n- **FSDP** - For models 500M+ parameters (large transformers, recommended for Lightning users)\n- **DeepSpeed** - For cutting-edge features and fine-grained control\n\nConfigure with: `Trainer(strategy=\"ddp\", accelerator=\"gpu\", devices=4)`\n\n**Detailed documentation:** Read `references\u002Fdistributed_training.md` for strategy comparison and configuration.\n\n### 7. Best Practices\n\n- Device agnostic code - Use `self.device` instead of `.cuda()`\n- Hyperparameter saving - Use `self.save_hyperparameters()` in `__init__()`\n- Metric logging - Use `self.log()` for automatic aggregation across devices\n- Reproducibility - Use `seed_everything()` and `Trainer(deterministic=True)`\n- Debugging - Use `Trainer(fast_dev_run=True)` to test with 1 batch\n\n**Detailed documentation:** Read `references\u002Fbest_practices.md` for common patterns and pitfalls.\n\n## Quick Workflow\n\n1. **Define model:**\n   ```python\n   class MyModel(L.LightningModule):\n       def __init__(self):\n           super().__init__()\n           self.save_hyperparameters()\n           self.model = YourNetwork()\n\n       def training_step(self, batch, batch_idx):\n           x, y = batch\n           loss = F.cross_entropy(self.model(x), y)\n           self.log(\"train_loss\", loss)\n           return loss\n\n       def configure_optimizers(self):\n           return torch.optim.Adam(self.parameters())\n   ```\n\n2. **Prepare data:**\n   ```python\n   # Option 1: Direct DataLoaders\n   train_loader = DataLoader(train_dataset, batch_size=32)\n\n   # Option 2: LightningDataModule (recommended for reusability)\n   dm = MyDataModule(batch_size=32)\n   ```\n\n3. **Train:**\n   ```python\n   trainer = L.Trainer(max_epochs=10, accelerator=\"gpu\", devices=2)\n   trainer.fit(model, train_loader)  # or trainer.fit(model, datamodule=dm)\n   ```\n\n## Resources\n\n### scripts\u002F\nExecutable Python templates for common PyTorch Lightning patterns:\n\n- `template_lightning_module.py` - Complete LightningModule boilerplate\n- `template_datamodule.py` - Complete LightningDataModule boilerplate\n- `quick_trainer_setup.py` - Common Trainer configuration examples\n\n### references\u002F\nDetailed documentation for each PyTorch Lightning component:\n\n- `lightning_module.md` - Comprehensive LightningModule guide (methods, hooks, properties)\n- `trainer.md` - Trainer configuration and parameters\n- `data_module.md` - LightningDataModule patterns and methods\n- `callbacks.md` - Built-in and custom callbacks\n- `logging.md` - Logger integrations and usage\n- `distributed_training.md` - DDP, FSDP, DeepSpeed comparison and setup\n- `best_practices.md` - Common patterns, tips, and pitfalls\n","# PyTorch Lightning\n\n## Overview\n\nPyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs\u002FTPUs.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Building, training, or deploying neural networks using PyTorch Lightning\n- Organizing PyTorch code into LightningModules\n- Configuring Trainers for multi-GPU\u002FTPU training\n- Implementing data pipelines with LightningDataModules\n- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)\n- Structuring deep learning projects professionally\n\n## Core Capabilities\n\n### 1. LightningModule - Model Definition\n\nOrganize PyTorch models into six logical sections:\n\n1. **Initialization** - `__init__()` and `setup()`\n2. **Training Loop** - `training_step(batch, batch_idx)`\n3. **Validation Loop** - `validation_step(batch, batch_idx)`\n4. **Test Loop** - `test_step(batch, batch_idx)`\n5. **Prediction** - `predict_step(batch, batch_idx)`\n6. **Optimizer Configuration** - `configure_optimizers()`\n\n**Quick template reference:** See `scripts\u002Ftemplate_lightning_module.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Flightning_module.md` for comprehensive method documentation, hooks, properties, and best practices.\n\n### 2. Trainer - Training Automation\n\nThe Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:\n\n- Multi-GPU\u002FTPU support with strategy selection (DDP, FSDP, DeepSpeed)\n- Automatic mixed precision training\n- Gradient accumulation and clipping\n- Checkpointing and early stopping\n- Progress bars and logging\n\n**Quick setup reference:** See `scripts\u002Fquick_trainer_setup.py` for common Trainer configurations.\n\n**Detailed documentation:** Read `references\u002Ftrainer.md` for all parameters, methods, and configuration options.\n\n### 3. LightningDataModule - Data Pipeline Organization\n\nEncapsulate all data processing steps in a reusable class:\n\n1. `prepare_data()` - Download and process data (single-process)\n2. `setup()` - Create datasets and apply transforms (per-GPU)\n3. `train_dataloader()` - Return training DataLoader\n4. `val_dataloader()` - Return validation DataLoader\n5. `test_dataloader()` - Return test DataLoader\n\n**Quick template reference:** See `scripts\u002Ftemplate_datamodule.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Fdata_module.md` for method details and usage patterns.\n\n### 4. Callbacks - Extensible Training Logic\n\nAdd custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:\n\n- **ModelCheckpoint** - Save best\u002Flatest models\n- **EarlyStopping** - Stop when metrics plateau\n- **LearningRateMonitor** - Track LR scheduler changes\n- **BatchSizeFinder** - Auto-determine optimal batch size\n\n**Detailed documentation:** Read `references\u002Fcallbacks.md` for built-in callbacks and custom callback creation.\n\n### 5. Logging - Experiment Tracking\n\nIntegrate with multiple logging platforms:\n\n- TensorBoard (default)\n- Weights & Biases (WandbLogger)\n- MLflow (MLFlowLogger)\n- Neptune (NeptuneLogger)\n- Comet (CometLogger)\n- CSV (CSVLogger)\n\nLog metrics using `self.log(\"metric_name\", value)` in any LightningModule method.\n\n**Detailed documentation:** Read `references\u002Flogging.md` for logger setup and configuration.\n\n### 6. Distributed Training - Scale to Multiple Devices\n\nChoose the right strategy based on model size:\n\n- **DDP** - For models \u003C500M parameters (ResNet, smaller transformers)\n- **FSDP** - For models 500M+ parameters (large transformers, recommended for Lightning users)\n- **DeepSpeed** - For cutting-edge features and fine-grained control\n\nConfigure with: `Trainer(strategy=\"ddp\", accelerator=\"gpu\", devices=4)`\n\n**Detailed documentation:** Read `references\u002Fdistributed_training.md` for strategy comparison and configuration.\n\n### 7. Best Practices\n\n- Device agnostic code - Use `self.device` instead of `.cuda()`\n- Hyperparameter saving - Use `self.save_hyperparameters()` in `__init__()`\n- Metric logging - Use `self.log()` for automatic aggregation across devices\n- Reproducibility - Use `seed_everything()` and `Trainer(deterministic=True)`\n- Debugging - Use `Trainer(fast_dev_run=True)` to test with 1 batch\n\n**Detailed documentation:** Read `references\u002Fbest_practices.md` for common patterns and pitfalls.\n\n## Quick Workflow\n\n1. **Define model:**\n   ```python\n   class MyModel(L.LightningModule):\n       def __init__(self):\n           super().__init__()\n           self.save_hyperparameters()\n           self.model = YourNetwork()\n\n       def training_step(self, batch, batch_idx):\n           x, y = batch\n           loss = F.cross_entropy(self.model(x), y)\n           self.log(\"train_loss\", loss)\n           return loss\n\n       def configure_optimizers(self):\n           return torch.optim.Adam(self.parameters())\n   ```\n\n2. **Prepare data:**\n   ```python\n   # Option 1: Direct DataLoaders\n   train_loader = DataLoader(train_dataset, batch_size=32)\n\n   # Option 2: LightningDataModule (recommended for reusability)\n   dm = MyDataModule(batch_size=32)\n   ```\n\n3. **Train:**\n   ```python\n   trainer = L.Trainer(max_epochs=10, accelerator=\"gpu\", devices=2)\n   trainer.fit(model, train_loader)  # or trainer.fit(model, datamodule=dm)\n   ```\n\n## Resources\n\n### scripts\u002F\nExecutable Python templates for common PyTorch Lightning patterns:\n\n- `template_lightning_module.py` - Complete LightningModule boilerplate\n- `template_datamodule.py` - Complete LightningDataModule boilerplate\n- `quick_trainer_setup.py` - Common Trainer configuration examples\n\n### references\u002F\nDetailed documentation for each PyTorch Lightning component:\n\n- `lightning_module.md` - Comprehensive LightningModule guide (methods, hooks, properties)\n- `trainer.md` - Trainer configuration and parameters\n- `data_module.md` - LightningDataModule patterns and methods\n- `callbacks.md` - Built-in and custom callbacks\n- `logging.md` - Logger integrations and usage\n- `distributed_training.md` - DDP, FSDP, DeepSpeed comparison and setup\n- `best_practices.md` - Common patterns, tips, and pitfalls\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fpytorch-lightning",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 pytorch-lightning",{"tool":61,"command":62},"bunx","bunx skills add JSGforever\u002Fskillguard --skill pytorch-lightning",{"tool":64,"command":65},"pnpm","pnpm 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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",[120],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","when","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":146,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":154,"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",[149],{"categoryNo":150,"categoryName":151,"taxonomyType":152,"taxonomyTypeDesc":153},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[26,155,156],"data exfiltrator","files",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":163,"publishedAt":43},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. 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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",[],[164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183],"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":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":193,"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",[190],{"categoryNo":191,"categoryName":192,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[173,194,181,182,195,196,197,198,199,200,164,201,202,177,179,40,180,183,203,204],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","router","strategies",{"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":212,"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",[211],{"categoryNo":191,"categoryName":192,"taxonomyType":20,"taxonomyTypeDesc":21},[213,183,214,215,216,217,173,164,218,219,220,179,180,181,221,182,222,223,224,225],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":239,"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",[232,235,238],{"categoryNo":233,"categoryName":234,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":236,"categoryName":237,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[240,241,242,243,244,245,246,171,199,247,248,249,180,204,250,225,251,252,253,254],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]