[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGEEWHK4PBYGQQ27AYW8JD":3,"skill-same:01M6XGEEWHK4PBYGQQ27AYW8JD":89},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6XGEEWHK4PBYGQQ27AYW8JD","pytorch-lightning",null,"High-level PyTorch framework with Trainer class, automatic distributed training (DDP\u002FFSDP\u002FDeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"lightning","pytorch lightning","pytorch","FSDP","when","automatic","you","loops","Scalable","Callbacks","High-Level API","Training Framework","DDP","synthetic","clean","api","Transformers","framework","code","training","2026-11-10 10:12:30",false,"---\nname: pytorch-lightning\ndescription: High-level PyTorch framework with Trainer class, automatic distributed training (DDP\u002FFSDP\u002FDeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.\ncategory: ml-training\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [PyTorch Lightning, Training Framework, Distributed Training, DDP, FSDP, DeepSpeed, High-Level API, Callbacks, Best Practices, Scalable]\ndependencies: [lightning, torch, transformers]\n---\n\n# PyTorch Lightning - High-Level Training Framework\n\n## Quick start\n\nPyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.\n\n**Installation**:\n```bash\npip install lightning\n```\n\n**Convert PyTorch to Lightning** (3 steps):\n\n```python\nimport lightning as L\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset\n\n# Step 1: Define LightningModule (organize your PyTorch code)\nclass LitModel(L.LightningModule):\n    def __init__(self, hidden_size=128):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.Linear(28 * 28, hidden_size),\n            nn.ReLU(),\n            nn.Linear(hidden_size, 10)\n        )\n\n    def training_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('train_loss', loss)  # Auto-logged to TensorBoard\n        return loss\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n\n# Step 2: Create data\ntrain_loader = DataLoader(train_dataset, batch_size=32)\n\n# Step 3: Train with Trainer (handles everything else!)\ntrainer = L.Trainer(max_epochs=10, accelerator='gpu', devices=2)\nmodel = LitModel()\ntrainer.fit(model, train_loader)\n```\n\n**That's it!** Trainer handles:\n- GPU\u002FTPU\u002FCPU switching\n- Distributed training (DDP, FSDP, DeepSpeed)\n- Mixed precision (FP16, BF16)\n- Gradient accumulation\n- Checkpointing\n- Logging\n- Progress bars\n\n## Common workflows\n\n### Workflow 1: From PyTorch to Lightning\n\n**Original PyTorch code**:\n```python\nmodel = MyModel()\noptimizer = torch.optim.Adam(model.parameters())\nmodel.to('cuda')\n\nfor epoch in range(max_epochs):\n    for batch in train_loader:\n        batch = batch.to('cuda')\n        optimizer.zero_grad()\n        loss = model(batch)\n        loss.backward()\n        optimizer.step()\n```\n\n**Lightning version**:\n```python\nclass LitModel(L.LightningModule):\n    def __init__(self):\n        super().__init__()\n        self.model = MyModel()\n\n    def training_step(self, batch, batch_idx):\n        loss = self.model(batch)  # No .to('cuda') needed!\n        return loss\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters())\n\n# Train\ntrainer = L.Trainer(max_epochs=10, accelerator='gpu')\ntrainer.fit(LitModel(), train_loader)\n```\n\n**Benefits**: 40+ lines → 15 lines, no device management, automatic distributed\n\n### Workflow 2: Validation and testing\n\n```python\nclass LitModel(L.LightningModule):\n    def __init__(self):\n        super().__init__()\n        self.model = MyModel()\n\n    def training_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('train_loss', loss)\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        val_loss = nn.functional.cross_entropy(y_hat, y)\n        acc = (y_hat.argmax(dim=1) == y).float().mean()\n        self.log('val_loss', val_loss)\n        self.log('val_acc', acc)\n\n    def test_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        test_loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('test_loss', test_loss)\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n\n# Train with validation\ntrainer = L.Trainer(max_epochs=10)\ntrainer.fit(model, train_loader, val_loader)\n\n# Test\ntrainer.test(model, test_loader)\n```\n\n**Automatic features**:\n- Validation runs every epoch by default\n- Metrics logged to TensorBoard\n- Best model checkpointing based on val_loss\n\n### Workflow 3: Distributed training (DDP)\n\n```python\n# Same code as single GPU!\nmodel = LitModel()\n\n# 8 GPUs with DDP (automatic!)\ntrainer = L.Trainer(\n    accelerator='gpu',\n    devices=8,\n    strategy='ddp'  # Or 'fsdp', 'deepspeed'\n)\n\ntrainer.fit(model, train_loader)\n```\n\n**Launch**:\n```bash\n# Single command, Lightning handles the rest\npython train.py\n```\n\n**No changes needed**:\n- Automatic data distribution\n- Gradient synchronization\n- Multi-node support (just set `num_nodes=2`)\n\n### Workflow 4: Callbacks for monitoring\n\n```python\nfrom lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor\n\n# Create callbacks\ncheckpoint = ModelCheckpoint(\n    monitor='val_loss',\n    mode='min',\n    save_top_k=3,\n    filename='model-{epoch:02d}-{val_loss:.2f}'\n)\n\nearly_stop = EarlyStopping(\n    monitor='val_loss',\n    patience=5,\n    mode='min'\n)\n\nlr_monitor = LearningRateMonitor(logging_interval='epoch')\n\n# Add to Trainer\ntrainer = L.Trainer(\n    max_epochs=100,\n    callbacks=[checkpoint, early_stop, lr_monitor]\n)\n\ntrainer.fit(model, train_loader, val_loader)\n```\n\n**Result**:\n- Auto-saves best 3 models\n- Stops early if no improvement for 5 epochs\n- Logs learning rate to TensorBoard\n\n### Workflow 5: Learning rate scheduling\n\n```python\nclass LitModel(L.LightningModule):\n    # ... (training_step, etc.)\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n\n        # Cosine annealing\n        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n            optimizer,\n            T_max=100,\n            eta_min=1e-5\n        )\n\n        return {\n            'optimizer': optimizer,\n            'lr_scheduler': {\n                'scheduler': scheduler,\n                'interval': 'epoch',  # Update per epoch\n                'frequency': 1\n            }\n        }\n\n# Learning rate auto-logged!\ntrainer = L.Trainer(max_epochs=100)\ntrainer.fit(model, train_loader)\n```\n\n## When to use vs alternatives\n\n**Use PyTorch Lightning when**:\n- Want clean, organized code\n- Need production-ready training loops\n- Switching between single GPU, multi-GPU, TPU\n- Want built-in callbacks and logging\n- Team collaboration (standardized structure)\n\n**Key advantages**:\n- **Organized**: Separates research code from engineering\n- **Automatic**: DDP, FSDP, DeepSpeed with 1 line\n- **Callbacks**: Modular training extensions\n- **Reproducible**: Less boilerplate = fewer bugs\n- **Tested**: 1M+ downloads\u002Fmonth, battle-tested\n\n**Use alternatives instead**:\n- **Accelerate**: Minimal changes to existing code, more flexibility\n- **Ray Train**: Multi-node orchestration, hyperparameter tuning\n- **Raw PyTorch**: Maximum control, learning purposes\n- **Keras**: TensorFlow ecosystem\n\n## Common issues\n\n**Issue: Loss not decreasing**\n\nCheck data and model setup:\n```python\n# Add to training_step\ndef training_step(self, batch, batch_idx):\n    if batch_idx == 0:\n        print(f\"Batch shape: {batch[0].shape}\")\n        print(f\"Labels: {batch[1]}\")\n    loss = ...\n    return loss\n```\n\n**Issue: Out of memory**\n\nReduce batch size or use gradient accumulation:\n```python\ntrainer = L.Trainer(\n    accumulate_grad_batches=4,  # Effective batch = batch_size × 4\n    precision='bf16'  # Or 'fp16', reduces memory 50%\n)\n```\n\n**Issue: Validation not running**\n\nEnsure you pass val_loader:\n```python\n# WRONG\ntrainer.fit(model, train_loader)\n\n# CORRECT\ntrainer.fit(model, train_loader, val_loader)\n```\n\n**Issue: DDP spawns multiple processes unexpectedly**\n\nLightning auto-detects GPUs. Explicitly set devices:\n```python\n# Test on CPU first\ntrainer = L.Trainer(accelerator='cpu', devices=1)\n\n# Then GPU\ntrainer = L.Trainer(accelerator='gpu', devices=1)\n```\n\n## Advanced topics\n\n**Callbacks**: See [references\u002Fcallbacks.md](references\u002Fcallbacks.md) for EarlyStopping, ModelCheckpoint, custom callbacks, and callback hooks.\n\n**Distributed strategies**: See [references\u002Fdistributed.md](references\u002Fdistributed.md) for DDP, FSDP, DeepSpeed ZeRO integration, multi-node setup.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameter-tuning.md](references\u002Fhyperparameter-tuning.md) for integration with Optuna, Ray Tune, and WandB sweeps.\n\n## Hardware requirements\n\n- **CPU**: Works (good for debugging)\n- **Single GPU**: Works\n- **Multi-GPU**: DDP (default), FSDP, or DeepSpeed\n- **Multi-node**: DDP, FSDP, DeepSpeed\n- **TPU**: Supported (8 cores)\n- **Apple MPS**: Supported\n\n**Precision options**:\n- FP32 (default)\n- FP16 (V100, older GPUs)\n- BF16 (A100\u002FH100, recommended)\n- FP8 (H100)\n\n## Resources\n\n- Docs: https:\u002F\u002Flightning.ai\u002Fdocs\u002Fpytorch\u002Fstable\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning ⭐ 29,000+\n- Version: 2.5.5+\n- Examples: https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning\u002Ftree\u002Fmaster\u002Fexamples\n- Discord: https:\u002F\u002Fdiscord.gg\u002Flightning-ai\n- Used by: Kaggle winners, research labs, production teams\n\n\n","# PyTorch Lightning - High-Level Training Framework\n\n## Quick start\n\nPyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.\n\n**Installation**:\n```bash\npip install lightning\n```\n\n**Convert PyTorch to Lightning** (3 steps):\n\n```python\nimport lightning as L\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset\n\n# Step 1: Define LightningModule (organize your PyTorch code)\nclass LitModel(L.LightningModule):\n    def __init__(self, hidden_size=128):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.Linear(28 * 28, hidden_size),\n            nn.ReLU(),\n            nn.Linear(hidden_size, 10)\n        )\n\n    def training_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('train_loss', loss)  # Auto-logged to TensorBoard\n        return loss\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n\n# Step 2: Create data\ntrain_loader = DataLoader(train_dataset, batch_size=32)\n\n# Step 3: Train with Trainer (handles everything else!)\ntrainer = L.Trainer(max_epochs=10, accelerator='gpu', devices=2)\nmodel = LitModel()\ntrainer.fit(model, train_loader)\n```\n\n**That's it!** Trainer handles:\n- GPU\u002FTPU\u002FCPU switching\n- Distributed training (DDP, FSDP, DeepSpeed)\n- Mixed precision (FP16, BF16)\n- Gradient accumulation\n- Checkpointing\n- Logging\n- Progress bars\n\n## Common workflows\n\n### Workflow 1: From PyTorch to Lightning\n\n**Original PyTorch code**:\n```python\nmodel = MyModel()\noptimizer = torch.optim.Adam(model.parameters())\nmodel.to('cuda')\n\nfor epoch in range(max_epochs):\n    for batch in train_loader:\n        batch = batch.to('cuda')\n        optimizer.zero_grad()\n        loss = model(batch)\n        loss.backward()\n        optimizer.step()\n```\n\n**Lightning version**:\n```python\nclass LitModel(L.LightningModule):\n    def __init__(self):\n        super().__init__()\n        self.model = MyModel()\n\n    def training_step(self, batch, batch_idx):\n        loss = self.model(batch)  # No .to('cuda') needed!\n        return loss\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters())\n\n# Train\ntrainer = L.Trainer(max_epochs=10, accelerator='gpu')\ntrainer.fit(LitModel(), train_loader)\n```\n\n**Benefits**: 40+ lines → 15 lines, no device management, automatic distributed\n\n### Workflow 2: Validation and testing\n\n```python\nclass LitModel(L.LightningModule):\n    def __init__(self):\n        super().__init__()\n        self.model = MyModel()\n\n    def training_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('train_loss', loss)\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        val_loss = nn.functional.cross_entropy(y_hat, y)\n        acc = (y_hat.argmax(dim=1) == y).float().mean()\n        self.log('val_loss', val_loss)\n        self.log('val_acc', acc)\n\n    def test_step(self, batch, batch_idx):\n        x, y = batch\n        y_hat = self.model(x)\n        test_loss = nn.functional.cross_entropy(y_hat, y)\n        self.log('test_loss', test_loss)\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n\n# Train with validation\ntrainer = L.Trainer(max_epochs=10)\ntrainer.fit(model, train_loader, val_loader)\n\n# Test\ntrainer.test(model, test_loader)\n```\n\n**Automatic features**:\n- Validation runs every epoch by default\n- Metrics logged to TensorBoard\n- Best model checkpointing based on val_loss\n\n### Workflow 3: Distributed training (DDP)\n\n```python\n# Same code as single GPU!\nmodel = LitModel()\n\n# 8 GPUs with DDP (automatic!)\ntrainer = L.Trainer(\n    accelerator='gpu',\n    devices=8,\n    strategy='ddp'  # Or 'fsdp', 'deepspeed'\n)\n\ntrainer.fit(model, train_loader)\n```\n\n**Launch**:\n```bash\n# Single command, Lightning handles the rest\npython train.py\n```\n\n**No changes needed**:\n- Automatic data distribution\n- Gradient synchronization\n- Multi-node support (just set `num_nodes=2`)\n\n### Workflow 4: Callbacks for monitoring\n\n```python\nfrom lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor\n\n# Create callbacks\ncheckpoint = ModelCheckpoint(\n    monitor='val_loss',\n    mode='min',\n    save_top_k=3,\n    filename='model-{epoch:02d}-{val_loss:.2f}'\n)\n\nearly_stop = EarlyStopping(\n    monitor='val_loss',\n    patience=5,\n    mode='min'\n)\n\nlr_monitor = LearningRateMonitor(logging_interval='epoch')\n\n# Add to Trainer\ntrainer = L.Trainer(\n    max_epochs=100,\n    callbacks=[checkpoint, early_stop, lr_monitor]\n)\n\ntrainer.fit(model, train_loader, val_loader)\n```\n\n**Result**:\n- Auto-saves best 3 models\n- Stops early if no improvement for 5 epochs\n- Logs learning rate to TensorBoard\n\n### Workflow 5: Learning rate scheduling\n\n```python\nclass LitModel(L.LightningModule):\n    # ... (training_step, etc.)\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n\n        # Cosine annealing\n        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n            optimizer,\n            T_max=100,\n            eta_min=1e-5\n        )\n\n        return {\n            'optimizer': optimizer,\n            'lr_scheduler': {\n                'scheduler': scheduler,\n                'interval': 'epoch',  # Update per epoch\n                'frequency': 1\n            }\n        }\n\n# Learning rate auto-logged!\ntrainer = L.Trainer(max_epochs=100)\ntrainer.fit(model, train_loader)\n```\n\n## When to use vs alternatives\n\n**Use PyTorch Lightning when**:\n- Want clean, organized code\n- Need production-ready training loops\n- Switching between single GPU, multi-GPU, TPU\n- Want built-in callbacks and logging\n- Team collaboration (standardized structure)\n\n**Key advantages**:\n- **Organized**: Separates research code from engineering\n- **Automatic**: DDP, FSDP, DeepSpeed with 1 line\n- **Callbacks**: Modular training extensions\n- **Reproducible**: Less boilerplate = fewer bugs\n- **Tested**: 1M+ downloads\u002Fmonth, battle-tested\n\n**Use alternatives instead**:\n- **Accelerate**: Minimal changes to existing code, more flexibility\n- **Ray Train**: Multi-node orchestration, hyperparameter tuning\n- **Raw PyTorch**: Maximum control, learning purposes\n- **Keras**: TensorFlow ecosystem\n\n## Common issues\n\n**Issue: Loss not decreasing**\n\nCheck data and model setup:\n```python\n# Add to training_step\ndef training_step(self, batch, batch_idx):\n    if batch_idx == 0:\n        print(f\"Batch shape: {batch[0].shape}\")\n        print(f\"Labels: {batch[1]}\")\n    loss = ...\n    return loss\n```\n\n**Issue: Out of memory**\n\nReduce batch size or use gradient accumulation:\n```python\ntrainer = L.Trainer(\n    accumulate_grad_batches=4,  # Effective batch = batch_size × 4\n    precision='bf16'  # Or 'fp16', reduces memory 50%\n)\n```\n\n**Issue: Validation not running**\n\nEnsure you pass val_loader:\n```python\n# WRONG\ntrainer.fit(model, train_loader)\n\n# CORRECT\ntrainer.fit(model, train_loader, val_loader)\n```\n\n**Issue: DDP spawns multiple processes unexpectedly**\n\nLightning auto-detects GPUs. Explicitly set devices:\n```python\n# Test on CPU first\ntrainer = L.Trainer(accelerator='cpu', devices=1)\n\n# Then GPU\ntrainer = L.Trainer(accelerator='gpu', devices=1)\n```\n\n## Advanced topics\n\n**Callbacks**: See [references\u002Fcallbacks.md](references\u002Fcallbacks.md) for EarlyStopping, ModelCheckpoint, custom callbacks, and callback hooks.\n\n**Distributed strategies**: See [references\u002Fdistributed.md](references\u002Fdistributed.md) for DDP, FSDP, DeepSpeed ZeRO integration, multi-node setup.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameter-tuning.md](references\u002Fhyperparameter-tuning.md) for integration with Optuna, Ray Tune, and WandB sweeps.\n\n## Hardware requirements\n\n- **CPU**: Works (good for debugging)\n- **Single GPU**: Works\n- **Multi-GPU**: DDP (default), FSDP, or DeepSpeed\n- **Multi-node**: DDP, FSDP, DeepSpeed\n- **TPU**: Supported (8 cores)\n- **Apple MPS**: Supported\n\n**Precision options**:\n- FP32 (default)\n- FP16 (V100, older GPUs)\n- BF16 (A100\u002FH100, recommended)\n- FP8 (H100)\n\n## Resources\n\n- Docs: https:\u002F\u002Flightning.ai\u002Fdocs\u002Fpytorch\u002Fstable\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning ⭐ 29,000+\n- Version: 2.5.5+\n- Examples: https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning\u002Ftree\u002Fmaster\u002Fexamples\n- Discord: https:\u002F\u002Fdiscord.gg\u002Flightning-ai\n- Used by: Kaggle winners, research labs, production teams\n\n\n",{"name":5,"tags":44,"author":49,"license":15,"version":50,"category":51,"description":7,"dependencies":52},[45,30,46,31,22,47,29,28,48,27],"PyTorch Lightning","Distributed Training","DeepSpeed","Best Practices","Synthetic Sciences","1.0.0","ml-training",[19,53,54],"torch","transformers","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add fadrienne\u002FMyGitHub --skill pytorch-lightning",{"tool":67,"command":68},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill pytorch-lightning",{"tool":70,"command":71},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill pytorch-lightning","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002FSKILL.md",true,[75,81,85],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fcallbacks.md","REFERENCE","参考文档",12031,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002Freferences\u002Fcallbacks.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fdistributed.md",10818,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002Freferences\u002Fdistributed.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fhyperparameter-tuning.md",12517,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002Freferences\u002Fhyperparameter-tuning.md",[90,121,147,180,207,233],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":101,"publishedAt":39},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[102,103,104,105,106,107,108,109,23,110,111,112,113,114,115,116,117,118,119,120],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":130,"publishedAt":39},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000008","测试与质量",[112,131,102,132,133,134,23,25,135,136,137,138,139,140,141,142,143,144,145,146],"statistical analysis","academic research","implementing","selection","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":162,"publishedAt":39},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[153,156,157],{"categoryNo":154,"categoryName":155,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[163,164,165,166,167,168,169,170,171,172,110,34,173,174,175,176,177,178,135,179],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":190,"publishedAt":39},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[186,189],{"categoryNo":187,"categoryName":188,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},[191,164,192,193,194,195,196,197,198,171,23,199,200,201,202,203,204,205,137,206],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":219,"publishedAt":39},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[213,216],{"categoryNo":214,"categoryName":215,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":217,"categoryName":218,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000030","开发工程师",[37,220,142,221,222,223,224,109,225,23,110,112,226,227,228,229,230,231,178,232],"research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":237,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":246,"publishedAt":39},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[240,243,244,245],{"categoryNo":241,"categoryName":242,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":154,"categoryName":155,"taxonomyType":99,"taxonomyTypeDesc":100},{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},[163,247,248,249,250,251,191,252,253,23,110,112,254,255,256,257,258,259,164,178],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]