[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADTKRG490F5G3H2HHYC5PB":3,"skill-same:01M6ADTKRG490F5G3H2HHYC5PB":113},{"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":23,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M6ADTKRG490F5G3H2HHYC5PB","mlops-tensorboard",null,"Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit","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\u002Fai-research\u002Fmlops-tensorboard\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"tensorboard","mlops tensorboard","mlops","visualization","orchestra","Model Debugging","Training Metrics","Experiment Tracking","experiment","model","performance profiling","metrics","google","toolkit","debugging","training","ml","pytorch","debug","experiments","research","tensorflow","performance","visualize","2026-11-10 10:12:30",false,"---\nname: mlops-tensorboard\ndescription: Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [MLOps, TensorBoard, Visualization, Training Metrics, Model Debugging, PyTorch, TensorFlow, Experiment Tracking, Performance Profiling]\n  dependencies: [tensorboard, torch, tensorflow]\n---\n\n# TensorBoard: Visualization Toolkit for ML\n\n## When to Use This Skill\n\nUse TensorBoard when you need to:\n- **Visualize training metrics** like loss and accuracy over time\n- **Debug models** with histograms and distributions\n- **Compare experiments** across multiple runs\n- **Visualize model graphs** and architecture\n- **Project embeddings** to lower dimensions (t-SNE, PCA)\n- **Track hyperparameter** experiments\n- **Profile performance** and identify bottlenecks\n- **Visualize images and text** during training\n\n**Users**: 20M+ downloads\u002Fyear | **GitHub Stars**: 27k+ | **License**: Apache 2.0\n\n## Installation\n\n```bash\n# Install TensorBoard\npip install tensorboard\n\n# PyTorch integration\npip install torch torchvision tensorboard\n\n# TensorFlow integration (TensorBoard included)\npip install tensorflow\n\n# Launch TensorBoard\ntensorboard --logdir=runs\n# Access at http:\u002F\u002Flocalhost:6006\n```\n\n## Quick Start\n\n### PyTorch\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Create writer\nwriter = SummaryWriter('runs\u002Fexperiment_1')\n\n# Training loop\nfor epoch in range(10):\n    train_loss = train_epoch()\n    val_acc = validate()\n\n    # Log metrics\n    writer.add_scalar('Loss\u002Ftrain', train_loss, epoch)\n    writer.add_scalar('Accuracy\u002Fval', val_acc, epoch)\n\n# Close writer\nwriter.close()\n\n# Launch: tensorboard --logdir=runs\n```\n\n### TensorFlow\u002FKeras\n\n```python\nimport tensorflow as tf\n\n# Create callback\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs\u002Ffit',\n    histogram_freq=1\n)\n\n# Train model\nmodel.fit(\n    x_train, y_train,\n    epochs=10,\n    validation_data=(x_val, y_val),\n    callbacks=[tensorboard_callback]\n)\n\n# Launch: tensorboard --logdir=logs\n```\n\n## Core Concepts\n\n### 1. SummaryWriter (PyTorch)\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Default directory: runs\u002FCURRENT_DATETIME\nwriter = SummaryWriter()\n\n# Custom directory\nwriter = SummaryWriter('runs\u002Fexperiment_1')\n\n# Custom comment (appended to default directory)\nwriter = SummaryWriter(comment='baseline')\n\n# Log data\nwriter.add_scalar('Loss\u002Ftrain', 0.5, step=0)\nwriter.add_scalar('Loss\u002Ftrain', 0.3, step=1)\n\n# Flush and close\nwriter.flush()\nwriter.close()\n```\n\n### 2. Logging Scalars\n\n```python\n# PyTorch\nfrom torch.utils.tensorboard import SummaryWriter\nwriter = SummaryWriter()\n\nfor epoch in range(100):\n    train_loss = train()\n    val_loss = validate()\n\n    # Log individual metrics\n    writer.add_scalar('Loss\u002Ftrain', train_loss, epoch)\n    writer.add_scalar('Loss\u002Fval', val_loss, epoch)\n    writer.add_scalar('Accuracy\u002Ftrain', train_acc, epoch)\n    writer.add_scalar('Accuracy\u002Fval', val_acc, epoch)\n\n    # Learning rate\n    lr = optimizer.param_groups[0]['lr']\n    writer.add_scalar('Learning_rate', lr, epoch)\n\nwriter.close()\n```\n\n```python\n# TensorFlow\nimport tensorflow as tf\n\ntrain_summary_writer = tf.summary.create_file_writer('logs\u002Ftrain')\nval_summary_writer = tf.summary.create_file_writer('logs\u002Fval')\n\nfor epoch in range(100):\n    with train_summary_writer.as_default():\n        tf.summary.scalar('loss', train_loss, step=epoch)\n        tf.summary.scalar('accuracy', train_acc, step=epoch)\n\n    with val_summary_writer.as_default():\n        tf.summary.scalar('loss', val_loss, step=epoch)\n        tf.summary.scalar('accuracy', val_acc, step=epoch)\n```\n\n### 3. Logging Multiple Scalars\n\n```python\n# PyTorch: Group related metrics\nwriter.add_scalars('Loss', {\n    'train': train_loss,\n    'validation': val_loss,\n    'test': test_loss\n}, epoch)\n\nwriter.add_scalars('Metrics', {\n    'accuracy': accuracy,\n    'precision': precision,\n    'recall': recall,\n    'f1': f1_score\n}, epoch)\n```\n\n### 4. Logging Images\n\n```python\n# PyTorch\nimport torch\nfrom torchvision.utils import make_grid\n\n# Single image\nwriter.add_image('Input\u002Fsample', img_tensor, epoch)\n\n# Multiple images as grid\nimg_grid = make_grid(images[:64], nrow=8)\nwriter.add_image('Batch\u002Finputs', img_grid, epoch)\n\n# Predictions visualization\npred_grid = make_grid(predictions[:16], nrow=4)\nwriter.add_image('Predictions', pred_grid, epoch)\n```\n\n```python\n# TensorFlow\nimport tensorflow as tf\n\nwith file_writer.as_default():\n    # Encode images as PNG\n    tf.summary.image('Training samples', images, step=epoch, max_outputs=25)\n```\n\n### 5. Logging Histograms\n\n```python\n# PyTorch: Track weight distributions\nfor name, param in model.named_parameters():\n    writer.add_histogram(name, param, epoch)\n\n    # Track gradients\n    if param.grad is not None:\n        writer.add_histogram(f'{name}.grad', param.grad, epoch)\n\n# Track activations\nwriter.add_histogram('Activations\u002Frelu1', activations, epoch)\n```\n\n```python\n# TensorFlow\nwith file_writer.as_default():\n    tf.summary.histogram('weights\u002Flayer1', layer1.kernel, step=epoch)\n    tf.summary.histogram('activations\u002Frelu1', activations, step=epoch)\n```\n\n### 6. Logging Model Graph\n\n```python\n# PyTorch\nimport torch\n\nmodel = MyModel()\ndummy_input = torch.randn(1, 3, 224, 224)\n\nwriter.add_graph(model, dummy_input)\nwriter.close()\n```\n\n```python\n# TensorFlow (automatic with Keras)\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs',\n    write_graph=True\n)\n\nmodel.fit(x, y, callbacks=[tensorboard_callback])\n```\n\n## Advanced Features\n\n### Embedding Projector\n\nVisualize high-dimensional data (embeddings, features) in 2D\u002F3D.\n\n```python\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Get embeddings (e.g., word embeddings, image features)\nembeddings = model.get_embeddings(data)  # Shape: (N, embedding_dim)\n\n# Metadata (labels for each point)\nmetadata = ['class_1', 'class_2', 'class_1', ...]\n\n# Images (optional, for image embeddings)\nlabel_images = torch.stack([img1, img2, img3, ...])\n\n# Log to TensorBoard\nwriter.add_embedding(\n    embeddings,\n    metadata=metadata,\n    label_img=label_images,\n    global_step=epoch\n)\n```\n\n**In TensorBoard:**\n- Navigate to \"Projector\" tab\n- Choose PCA, t-SNE, or UMAP visualization\n- Search, filter, and explore clusters\n\n### Hyperparameter Tuning\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Try different hyperparameters\nfor lr in [0.001, 0.01, 0.1]:\n    for batch_size in [16, 32, 64]:\n        # Create unique run directory\n        writer = SummaryWriter(f'runs\u002Flr{lr}_bs{batch_size}')\n\n        # Log hyperparameters\n        writer.add_hparams(\n            {'lr': lr, 'batch_size': batch_size},\n            {'hparam\u002Faccuracy': final_acc, 'hparam\u002Floss': final_loss}\n        )\n\n        # Train and log\n        for epoch in range(10):\n            loss = train(lr, batch_size)\n            writer.add_scalar('Loss\u002Ftrain', loss, epoch)\n\n        writer.close()\n\n# Compare in TensorBoard's \"HParams\" tab\n```\n\n### Text Logging\n\n```python\n# PyTorch: Log text (e.g., model predictions, summaries)\nwriter.add_text('Predictions', f'Epoch {epoch}: {predictions}', epoch)\nwriter.add_text('Config', str(config), 0)\n\n# Log markdown tables\nmarkdown_table = \"\"\"\n| Metric | Value |\n|--------|-------|\n| Accuracy | 0.95 |\n| F1 Score | 0.93 |\n\"\"\"\nwriter.add_text('Results', markdown_table, epoch)\n```\n\n### PR Curves\n\nPrecision-Recall curves for classification.\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Get predictions and labels\npredictions = model(test_data)  # Shape: (N, num_classes)\nlabels = test_labels  # Shape: (N,)\n\n# Log PR curve for each class\nfor i in range(num_classes):\n    writer.add_pr_curve(\n        f'PR_curve\u002Fclass_{i}',\n        labels == i,\n        predictions[:, i],\n        global_step=epoch\n    )\n```\n\n## Integration Examples\n\n### PyTorch Training Loop\n\n```python\nimport torch\nimport torch.nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Setup\nwriter = SummaryWriter('runs\u002Fresnet_experiment')\nmodel = ResNet50()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\ncriterion = nn.CrossEntropyLoss()\n\n# Log model graph\ndummy_input = torch.randn(1, 3, 224, 224)\nwriter.add_graph(model, dummy_input)\n\n# Training loop\nfor epoch in range(50):\n    model.train()\n    train_loss = 0.0\n    train_correct = 0\n\n    for batch_idx, (data, target) in enumerate(train_loader):\n        optimizer.zero_grad()\n        output = model(data)\n        loss = criterion(output, target)\n        loss.backward()\n        optimizer.step()\n\n        train_loss += loss.item()\n        pred = output.argmax(dim=1)\n        train_correct += pred.eq(target).sum().item()\n\n        # Log batch metrics (every 100 batches)\n        if batch_idx % 100 == 0:\n            global_step = epoch * len(train_loader) + batch_idx\n            writer.add_scalar('Loss\u002Ftrain_batch', loss.item(), global_step)\n\n    # Epoch metrics\n    train_loss \u002F= len(train_loader)\n    train_acc = train_correct \u002F len(train_loader.dataset)\n\n    # Validation\n    model.eval()\n    val_loss = 0.0\n    val_correct = 0\n\n    with torch.no_grad():\n        for data, target in val_loader:\n            output = model(data)\n            val_loss += criterion(output, target).item()\n            pred = output.argmax(dim=1)\n            val_correct += pred.eq(target).sum().item()\n\n    val_loss \u002F= len(val_loader)\n    val_acc = val_correct \u002F len(val_loader.dataset)\n\n    # Log epoch metrics\n    writer.add_scalars('Loss', {'train': train_loss, 'val': val_loss}, epoch)\n    writer.add_scalars('Accuracy', {'train': train_acc, 'val': val_acc}, epoch)\n\n    # Log learning rate\n    writer.add_scalar('Learning_rate', optimizer.param_groups[0]['lr'], epoch)\n\n    # Log histograms (every 5 epochs)\n    if epoch % 5 == 0:\n        for name, param in model.named_parameters():\n            writer.add_histogram(name, param, epoch)\n\n    # Log sample predictions\n    if epoch % 10 == 0:\n        sample_images = data[:8]\n        writer.add_image('Sample_inputs', make_grid(sample_images), epoch)\n\nwriter.close()\n```\n\n### TensorFlow\u002FKeras Training\n\n```python\nimport tensorflow as tf\n\n# Define model\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),\n    tf.keras.layers.MaxPooling2D(),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\n\nmodel.compile(\n    optimizer='adam',\n    loss='sparse_categorical_crossentropy',\n    metrics=['accuracy']\n)\n\n# TensorBoard callback\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs\u002Ffit',\n    histogram_freq=1,          # Log histograms every epoch\n    write_graph=True,          # Visualize model graph\n    write_images=True,         # Visualize weights as images\n    update_freq='epoch',       # Log metrics every epoch\n    profile_batch='500,520',   # Profile batches 500-520\n    embeddings_freq=1          # Log embeddings every epoch\n)\n\n# Train\nmodel.fit(\n    x_train, y_train,\n    epochs=10,\n    validation_data=(x_val, y_val),\n    callbacks=[tensorboard_callback]\n)\n```\n\n## Comparing Experiments\n\n### Multiple Runs\n\n```bash\n# Run experiments with different configs\npython train.py --lr 0.001 --logdir runs\u002Fexp1\npython train.py --lr 0.01 --logdir runs\u002Fexp2\npython train.py --lr 0.1 --logdir runs\u002Fexp3\n\n# View all runs together\ntensorboard --logdir=runs\n```\n\n**In TensorBoard:**\n- All runs appear in the same dashboard\n- Toggle runs on\u002Foff for comparison\n- Use regex to filter run names\n- Overlay charts to compare metrics\n\n### Organizing Experiments\n\n```python\n# Hierarchical organization\nruns\u002F\n├── baseline\u002F\n│   ├── run_1\u002F\n│   └── run_2\u002F\n├── improved\u002F\n│   ├── run_1\u002F\n│   └── run_2\u002F\n└── final\u002F\n    └── run_1\u002F\n\n# Log with hierarchy\nwriter = SummaryWriter('runs\u002Fbaseline\u002Frun_1')\n```\n\n## Best Practices\n\n### 1. Use Descriptive Run Names\n\n```python\n# ✅ Good: Descriptive names\nfrom datetime import datetime\ntimestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\nwriter = SummaryWriter(f'runs\u002Fresnet50_lr0.001_bs32_{timestamp}')\n\n# ❌ Bad: Auto-generated names\nwriter = SummaryWriter()  # Creates runs\u002FJan01_12-34-56_hostname\n```\n\n### 2. Group Related Metrics\n\n```python\n# ✅ Good: Grouped metrics\nwriter.add_scalar('Loss\u002Ftrain', train_loss, step)\nwriter.add_scalar('Loss\u002Fval', val_loss, step)\nwriter.add_scalar('Accuracy\u002Ftrain', train_acc, step)\nwriter.add_scalar('Accuracy\u002Fval', val_acc, step)\n\n# ❌ Bad: Flat namespace\nwriter.add_scalar('train_loss', train_loss, step)\nwriter.add_scalar('val_loss', val_loss, step)\n```\n\n### 3. Log Regularly but Not Too Often\n\n```python\n# ✅ Good: Log epoch metrics always, batch metrics occasionally\nfor epoch in range(100):\n    for batch_idx, (data, target) in enumerate(train_loader):\n        loss = train_step(data, target)\n\n        # Log every 100 batches\n        if batch_idx % 100 == 0:\n            writer.add_scalar('Loss\u002Fbatch', loss, global_step)\n\n    # Always log epoch metrics\n    writer.add_scalar('Loss\u002Fepoch', epoch_loss, epoch)\n\n# ❌ Bad: Log every batch (creates huge log files)\nfor batch in train_loader:\n    writer.add_scalar('Loss', loss, step)  # Too frequent\n```\n\n### 4. Close Writer When Done\n\n```python\n# ✅ Good: Use context manager\nwith SummaryWriter('runs\u002Fexp1') as writer:\n    for epoch in range(10):\n        writer.add_scalar('Loss', loss, epoch)\n# Automatically closes\n\n# Or manually\nwriter = SummaryWriter('runs\u002Fexp1')\n# ... logging ...\nwriter.close()\n```\n\n### 5. Use Separate Writers for Train\u002FVal\n\n```python\n# ✅ Good: Separate log directories\ntrain_writer = SummaryWriter('runs\u002Fexp1\u002Ftrain')\nval_writer = SummaryWriter('runs\u002Fexp1\u002Fval')\n\ntrain_writer.add_scalar('loss', train_loss, epoch)\nval_writer.add_scalar('loss', val_loss, epoch)\n```\n\n## Performance Profiling\n\n### TensorFlow Profiler\n\n```python\n# Enable profiling\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs',\n    profile_batch='10,20'  # Profile batches 10-20\n)\n\nmodel.fit(x, y, callbacks=[tensorboard_callback])\n\n# View in TensorBoard Profile tab\n# Shows: GPU utilization, kernel stats, memory usage, bottlenecks\n```\n\n### PyTorch Profiler\n\n```python\nimport torch.profiler as profiler\n\nwith profiler.profile(\n    activities=[\n        profiler.ProfilerActivity.CPU,\n        profiler.ProfilerActivity.CUDA\n    ],\n    on_trace_ready=torch.profiler.tensorboard_trace_handler('.\u002Fruns\u002Fprofiler'),\n    record_shapes=True,\n    with_stack=True\n) as prof:\n    for batch in train_loader:\n        loss = train_step(batch)\n        prof.step()\n\n# View in TensorBoard Profile tab\n```\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fwww.tensorflow.org\u002Ftensorboard\n- **PyTorch Integration**: https:\u002F\u002Fpytorch.org\u002Fdocs\u002Fstable\u002Ftensorboard.html\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorboard (27k+ stars)\n- **TensorBoard.dev**: https:\u002F\u002Ftensorboard.dev (share experiments publicly)\n\n## See Also\n\n- `references\u002Fvisualization.md` - Comprehensive visualization guide\n- `references\u002Fprofiling.md` - Performance profiling patterns\n- `references\u002Fintegrations.md` - Framework-specific integration examples\n\n\n","# TensorBoard: Visualization Toolkit for ML\n\n## When to Use This Skill\n\nUse TensorBoard when you need to:\n- **Visualize training metrics** like loss and accuracy over time\n- **Debug models** with histograms and distributions\n- **Compare experiments** across multiple runs\n- **Visualize model graphs** and architecture\n- **Project embeddings** to lower dimensions (t-SNE, PCA)\n- **Track hyperparameter** experiments\n- **Profile performance** and identify bottlenecks\n- **Visualize images and text** during training\n\n**Users**: 20M+ downloads\u002Fyear | **GitHub Stars**: 27k+ | **License**: Apache 2.0\n\n## Installation\n\n```bash\n# Install TensorBoard\npip install tensorboard\n\n# PyTorch integration\npip install torch torchvision tensorboard\n\n# TensorFlow integration (TensorBoard included)\npip install tensorflow\n\n# Launch TensorBoard\ntensorboard --logdir=runs\n# Access at http:\u002F\u002Flocalhost:6006\n```\n\n## Quick Start\n\n### PyTorch\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Create writer\nwriter = SummaryWriter('runs\u002Fexperiment_1')\n\n# Training loop\nfor epoch in range(10):\n    train_loss = train_epoch()\n    val_acc = validate()\n\n    # Log metrics\n    writer.add_scalar('Loss\u002Ftrain', train_loss, epoch)\n    writer.add_scalar('Accuracy\u002Fval', val_acc, epoch)\n\n# Close writer\nwriter.close()\n\n# Launch: tensorboard --logdir=runs\n```\n\n### TensorFlow\u002FKeras\n\n```python\nimport tensorflow as tf\n\n# Create callback\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs\u002Ffit',\n    histogram_freq=1\n)\n\n# Train model\nmodel.fit(\n    x_train, y_train,\n    epochs=10,\n    validation_data=(x_val, y_val),\n    callbacks=[tensorboard_callback]\n)\n\n# Launch: tensorboard --logdir=logs\n```\n\n## Core Concepts\n\n### 1. SummaryWriter (PyTorch)\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Default directory: runs\u002FCURRENT_DATETIME\nwriter = SummaryWriter()\n\n# Custom directory\nwriter = SummaryWriter('runs\u002Fexperiment_1')\n\n# Custom comment (appended to default directory)\nwriter = SummaryWriter(comment='baseline')\n\n# Log data\nwriter.add_scalar('Loss\u002Ftrain', 0.5, step=0)\nwriter.add_scalar('Loss\u002Ftrain', 0.3, step=1)\n\n# Flush and close\nwriter.flush()\nwriter.close()\n```\n\n### 2. Logging Scalars\n\n```python\n# PyTorch\nfrom torch.utils.tensorboard import SummaryWriter\nwriter = SummaryWriter()\n\nfor epoch in range(100):\n    train_loss = train()\n    val_loss = validate()\n\n    # Log individual metrics\n    writer.add_scalar('Loss\u002Ftrain', train_loss, epoch)\n    writer.add_scalar('Loss\u002Fval', val_loss, epoch)\n    writer.add_scalar('Accuracy\u002Ftrain', train_acc, epoch)\n    writer.add_scalar('Accuracy\u002Fval', val_acc, epoch)\n\n    # Learning rate\n    lr = optimizer.param_groups[0]['lr']\n    writer.add_scalar('Learning_rate', lr, epoch)\n\nwriter.close()\n```\n\n```python\n# TensorFlow\nimport tensorflow as tf\n\ntrain_summary_writer = tf.summary.create_file_writer('logs\u002Ftrain')\nval_summary_writer = tf.summary.create_file_writer('logs\u002Fval')\n\nfor epoch in range(100):\n    with train_summary_writer.as_default():\n        tf.summary.scalar('loss', train_loss, step=epoch)\n        tf.summary.scalar('accuracy', train_acc, step=epoch)\n\n    with val_summary_writer.as_default():\n        tf.summary.scalar('loss', val_loss, step=epoch)\n        tf.summary.scalar('accuracy', val_acc, step=epoch)\n```\n\n### 3. Logging Multiple Scalars\n\n```python\n# PyTorch: Group related metrics\nwriter.add_scalars('Loss', {\n    'train': train_loss,\n    'validation': val_loss,\n    'test': test_loss\n}, epoch)\n\nwriter.add_scalars('Metrics', {\n    'accuracy': accuracy,\n    'precision': precision,\n    'recall': recall,\n    'f1': f1_score\n}, epoch)\n```\n\n### 4. Logging Images\n\n```python\n# PyTorch\nimport torch\nfrom torchvision.utils import make_grid\n\n# Single image\nwriter.add_image('Input\u002Fsample', img_tensor, epoch)\n\n# Multiple images as grid\nimg_grid = make_grid(images[:64], nrow=8)\nwriter.add_image('Batch\u002Finputs', img_grid, epoch)\n\n# Predictions visualization\npred_grid = make_grid(predictions[:16], nrow=4)\nwriter.add_image('Predictions', pred_grid, epoch)\n```\n\n```python\n# TensorFlow\nimport tensorflow as tf\n\nwith file_writer.as_default():\n    # Encode images as PNG\n    tf.summary.image('Training samples', images, step=epoch, max_outputs=25)\n```\n\n### 5. Logging Histograms\n\n```python\n# PyTorch: Track weight distributions\nfor name, param in model.named_parameters():\n    writer.add_histogram(name, param, epoch)\n\n    # Track gradients\n    if param.grad is not None:\n        writer.add_histogram(f'{name}.grad', param.grad, epoch)\n\n# Track activations\nwriter.add_histogram('Activations\u002Frelu1', activations, epoch)\n```\n\n```python\n# TensorFlow\nwith file_writer.as_default():\n    tf.summary.histogram('weights\u002Flayer1', layer1.kernel, step=epoch)\n    tf.summary.histogram('activations\u002Frelu1', activations, step=epoch)\n```\n\n### 6. Logging Model Graph\n\n```python\n# PyTorch\nimport torch\n\nmodel = MyModel()\ndummy_input = torch.randn(1, 3, 224, 224)\n\nwriter.add_graph(model, dummy_input)\nwriter.close()\n```\n\n```python\n# TensorFlow (automatic with Keras)\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs',\n    write_graph=True\n)\n\nmodel.fit(x, y, callbacks=[tensorboard_callback])\n```\n\n## Advanced Features\n\n### Embedding Projector\n\nVisualize high-dimensional data (embeddings, features) in 2D\u002F3D.\n\n```python\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Get embeddings (e.g., word embeddings, image features)\nembeddings = model.get_embeddings(data)  # Shape: (N, embedding_dim)\n\n# Metadata (labels for each point)\nmetadata = ['class_1', 'class_2', 'class_1', ...]\n\n# Images (optional, for image embeddings)\nlabel_images = torch.stack([img1, img2, img3, ...])\n\n# Log to TensorBoard\nwriter.add_embedding(\n    embeddings,\n    metadata=metadata,\n    label_img=label_images,\n    global_step=epoch\n)\n```\n\n**In TensorBoard:**\n- Navigate to \"Projector\" tab\n- Choose PCA, t-SNE, or UMAP visualization\n- Search, filter, and explore clusters\n\n### Hyperparameter Tuning\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Try different hyperparameters\nfor lr in [0.001, 0.01, 0.1]:\n    for batch_size in [16, 32, 64]:\n        # Create unique run directory\n        writer = SummaryWriter(f'runs\u002Flr{lr}_bs{batch_size}')\n\n        # Log hyperparameters\n        writer.add_hparams(\n            {'lr': lr, 'batch_size': batch_size},\n            {'hparam\u002Faccuracy': final_acc, 'hparam\u002Floss': final_loss}\n        )\n\n        # Train and log\n        for epoch in range(10):\n            loss = train(lr, batch_size)\n            writer.add_scalar('Loss\u002Ftrain', loss, epoch)\n\n        writer.close()\n\n# Compare in TensorBoard's \"HParams\" tab\n```\n\n### Text Logging\n\n```python\n# PyTorch: Log text (e.g., model predictions, summaries)\nwriter.add_text('Predictions', f'Epoch {epoch}: {predictions}', epoch)\nwriter.add_text('Config', str(config), 0)\n\n# Log markdown tables\nmarkdown_table = \"\"\"\n| Metric | Value |\n|--------|-------|\n| Accuracy | 0.95 |\n| F1 Score | 0.93 |\n\"\"\"\nwriter.add_text('Results', markdown_table, epoch)\n```\n\n### PR Curves\n\nPrecision-Recall curves for classification.\n\n```python\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Get predictions and labels\npredictions = model(test_data)  # Shape: (N, num_classes)\nlabels = test_labels  # Shape: (N,)\n\n# Log PR curve for each class\nfor i in range(num_classes):\n    writer.add_pr_curve(\n        f'PR_curve\u002Fclass_{i}',\n        labels == i,\n        predictions[:, i],\n        global_step=epoch\n    )\n```\n\n## Integration Examples\n\n### PyTorch Training Loop\n\n```python\nimport torch\nimport torch.nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Setup\nwriter = SummaryWriter('runs\u002Fresnet_experiment')\nmodel = ResNet50()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\ncriterion = nn.CrossEntropyLoss()\n\n# Log model graph\ndummy_input = torch.randn(1, 3, 224, 224)\nwriter.add_graph(model, dummy_input)\n\n# Training loop\nfor epoch in range(50):\n    model.train()\n    train_loss = 0.0\n    train_correct = 0\n\n    for batch_idx, (data, target) in enumerate(train_loader):\n        optimizer.zero_grad()\n        output = model(data)\n        loss = criterion(output, target)\n        loss.backward()\n        optimizer.step()\n\n        train_loss += loss.item()\n        pred = output.argmax(dim=1)\n        train_correct += pred.eq(target).sum().item()\n\n        # Log batch metrics (every 100 batches)\n        if batch_idx % 100 == 0:\n            global_step = epoch * len(train_loader) + batch_idx\n            writer.add_scalar('Loss\u002Ftrain_batch', loss.item(), global_step)\n\n    # Epoch metrics\n    train_loss \u002F= len(train_loader)\n    train_acc = train_correct \u002F len(train_loader.dataset)\n\n    # Validation\n    model.eval()\n    val_loss = 0.0\n    val_correct = 0\n\n    with torch.no_grad():\n        for data, target in val_loader:\n            output = model(data)\n            val_loss += criterion(output, target).item()\n            pred = output.argmax(dim=1)\n            val_correct += pred.eq(target).sum().item()\n\n    val_loss \u002F= len(val_loader)\n    val_acc = val_correct \u002F len(val_loader.dataset)\n\n    # Log epoch metrics\n    writer.add_scalars('Loss', {'train': train_loss, 'val': val_loss}, epoch)\n    writer.add_scalars('Accuracy', {'train': train_acc, 'val': val_acc}, epoch)\n\n    # Log learning rate\n    writer.add_scalar('Learning_rate', optimizer.param_groups[0]['lr'], epoch)\n\n    # Log histograms (every 5 epochs)\n    if epoch % 5 == 0:\n        for name, param in model.named_parameters():\n            writer.add_histogram(name, param, epoch)\n\n    # Log sample predictions\n    if epoch % 10 == 0:\n        sample_images = data[:8]\n        writer.add_image('Sample_inputs', make_grid(sample_images), epoch)\n\nwriter.close()\n```\n\n### TensorFlow\u002FKeras Training\n\n```python\nimport tensorflow as tf\n\n# Define model\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),\n    tf.keras.layers.MaxPooling2D(),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\n\nmodel.compile(\n    optimizer='adam',\n    loss='sparse_categorical_crossentropy',\n    metrics=['accuracy']\n)\n\n# TensorBoard callback\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs\u002Ffit',\n    histogram_freq=1,          # Log histograms every epoch\n    write_graph=True,          # Visualize model graph\n    write_images=True,         # Visualize weights as images\n    update_freq='epoch',       # Log metrics every epoch\n    profile_batch='500,520',   # Profile batches 500-520\n    embeddings_freq=1          # Log embeddings every epoch\n)\n\n# Train\nmodel.fit(\n    x_train, y_train,\n    epochs=10,\n    validation_data=(x_val, y_val),\n    callbacks=[tensorboard_callback]\n)\n```\n\n## Comparing Experiments\n\n### Multiple Runs\n\n```bash\n# Run experiments with different configs\npython train.py --lr 0.001 --logdir runs\u002Fexp1\npython train.py --lr 0.01 --logdir runs\u002Fexp2\npython train.py --lr 0.1 --logdir runs\u002Fexp3\n\n# View all runs together\ntensorboard --logdir=runs\n```\n\n**In TensorBoard:**\n- All runs appear in the same dashboard\n- Toggle runs on\u002Foff for comparison\n- Use regex to filter run names\n- Overlay charts to compare metrics\n\n### Organizing Experiments\n\n```python\n# Hierarchical organization\nruns\u002F\n├── baseline\u002F\n│   ├── run_1\u002F\n│   └── run_2\u002F\n├── improved\u002F\n│   ├── run_1\u002F\n│   └── run_2\u002F\n└── final\u002F\n    └── run_1\u002F\n\n# Log with hierarchy\nwriter = SummaryWriter('runs\u002Fbaseline\u002Frun_1')\n```\n\n## Best Practices\n\n### 1. Use Descriptive Run Names\n\n```python\n# ✅ Good: Descriptive names\nfrom datetime import datetime\ntimestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\nwriter = SummaryWriter(f'runs\u002Fresnet50_lr0.001_bs32_{timestamp}')\n\n# ❌ Bad: Auto-generated names\nwriter = SummaryWriter()  # Creates runs\u002FJan01_12-34-56_hostname\n```\n\n### 2. Group Related Metrics\n\n```python\n# ✅ Good: Grouped metrics\nwriter.add_scalar('Loss\u002Ftrain', train_loss, step)\nwriter.add_scalar('Loss\u002Fval', val_loss, step)\nwriter.add_scalar('Accuracy\u002Ftrain', train_acc, step)\nwriter.add_scalar('Accuracy\u002Fval', val_acc, step)\n\n# ❌ Bad: Flat namespace\nwriter.add_scalar('train_loss', train_loss, step)\nwriter.add_scalar('val_loss', val_loss, step)\n```\n\n### 3. Log Regularly but Not Too Often\n\n```python\n# ✅ Good: Log epoch metrics always, batch metrics occasionally\nfor epoch in range(100):\n    for batch_idx, (data, target) in enumerate(train_loader):\n        loss = train_step(data, target)\n\n        # Log every 100 batches\n        if batch_idx % 100 == 0:\n            writer.add_scalar('Loss\u002Fbatch', loss, global_step)\n\n    # Always log epoch metrics\n    writer.add_scalar('Loss\u002Fepoch', epoch_loss, epoch)\n\n# ❌ Bad: Log every batch (creates huge log files)\nfor batch in train_loader:\n    writer.add_scalar('Loss', loss, step)  # Too frequent\n```\n\n### 4. Close Writer When Done\n\n```python\n# ✅ Good: Use context manager\nwith SummaryWriter('runs\u002Fexp1') as writer:\n    for epoch in range(10):\n        writer.add_scalar('Loss', loss, epoch)\n# Automatically closes\n\n# Or manually\nwriter = SummaryWriter('runs\u002Fexp1')\n# ... logging ...\nwriter.close()\n```\n\n### 5. Use Separate Writers for Train\u002FVal\n\n```python\n# ✅ Good: Separate log directories\ntrain_writer = SummaryWriter('runs\u002Fexp1\u002Ftrain')\nval_writer = SummaryWriter('runs\u002Fexp1\u002Fval')\n\ntrain_writer.add_scalar('loss', train_loss, epoch)\nval_writer.add_scalar('loss', val_loss, epoch)\n```\n\n## Performance Profiling\n\n### TensorFlow Profiler\n\n```python\n# Enable profiling\ntensorboard_callback = tf.keras.callbacks.TensorBoard(\n    log_dir='logs',\n    profile_batch='10,20'  # Profile batches 10-20\n)\n\nmodel.fit(x, y, callbacks=[tensorboard_callback])\n\n# View in TensorBoard Profile tab\n# Shows: GPU utilization, kernel stats, memory usage, bottlenecks\n```\n\n### PyTorch Profiler\n\n```python\nimport torch.profiler as profiler\n\nwith profiler.profile(\n    activities=[\n        profiler.ProfilerActivity.CPU,\n        profiler.ProfilerActivity.CUDA\n    ],\n    on_trace_ready=torch.profiler.tensorboard_trace_handler('.\u002Fruns\u002Fprofiler'),\n    record_shapes=True,\n    with_stack=True\n) as prof:\n    for batch in train_loader:\n        loss = train_step(batch)\n        prof.step()\n\n# View in TensorBoard Profile tab\n```\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fwww.tensorflow.org\u002Ftensorboard\n- **PyTorch Integration**: https:\u002F\u002Fpytorch.org\u002Fdocs\u002Fstable\u002Ftensorboard.html\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorboard (27k+ stars)\n- **TensorBoard.dev**: https:\u002F\u002Ftensorboard.dev (share experiments publicly)\n\n## See Also\n\n- `references\u002Fvisualization.md` - Comprehensive visualization guide\n- `references\u002Fprofiling.md` - Performance profiling patterns\n- `references\u002Fintegrations.md` - Framework-specific integration examples\n\n\n",{"name":5,"license":15,"metadata":53,"description":7},{"tags":54,"author":61,"version":62,"dependencies":63},[55,56,57,30,29,58,59,31,60],"MLOps","TensorBoard","Visualization","PyTorch","TensorFlow","Performance Profiling","Orchestra Research","1.0.0",[24,64,45],"torch","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-tensorboard",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":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add davila7\u002Fclaude-code-templates --skill mlops-tensorboard",{"tool":81,"command":82},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill mlops-tensorboard",{"tool":84,"command":85},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill mlops-tensorboard","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-tensorboard\u002FSKILL.md",true,[89,95,99,103,107],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"agents\u002Fopenai.yaml","REFERENCE","参考文档",96,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwizard\u002Fagents\u002Fopenai.yaml",{"path":96,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fintegrations.md",16205,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-tensorboard\u002Freferences\u002Fintegrations.md",{"path":100,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fprofiling.md",13337,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-tensorboard\u002Freferences\u002Fprofiling.md",{"path":104,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fvisualization.md",14897,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-tensorboard\u002Freferences\u002Fvisualization.md",{"path":108,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":111,"sourceHtmlUrl":112},"template.sh","SCRIPT","脚本",9498,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwizard\u002Ftemplate.sh",[114,144,171,204,239,261],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":123,"publishedAt":48},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. 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",[120],{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"publishedAt":48},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. 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",[150],{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},[152,153,154,155,156,157,158,159,160,161,162,133,163,164,165,166,167,168,169,170],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":186,"publishedAt":48},"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",[177,180,181],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":182,"categoryName":183,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[187,188,189,190,191,192,193,129,194,195,196,197,198,199,200,132,201,202,133,203],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":209,"sourceHtmlUrl":210,"categories":211,"keywords":215,"publishedAt":48},"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",[212],{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,129,238],"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":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":243,"sourceHtmlUrl":244,"categories":245,"keywords":250,"publishedAt":48},"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",[246,249],{"categoryNo":247,"categoryName":248,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},[251,252,253,254,255,217,256,226,227,38,37,257,258,129,259,135,260],"webapp testing","testing","webapp","browser","playwright","applications","logs","screenshots","local","behavior",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":208,"licenseFileUrl":265,"sourceHtmlUrl":266,"categories":267,"keywords":275,"publishedAt":48},"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",[268,271,272],{"categoryNo":269,"categoryName":270,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":273,"categoryName":274,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000036","内容创作者",[276,277,278,279,280,281,282,283,219,284,229,163,285,286,287,288,289,199,290,133,291,292,293,294,295,141,296],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]