[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADTPHA1E9ADMV2XQG5MX8A":3,"skill-same:01M6ADTPHA1E9ADMV2XQG5MX8A":137},{"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":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":67,"commitSha":68,"skillDirectory":69,"forksCount":70,"repositoryHtmlUrl":71,"repositoryHomepageUrl":72,"ownerHtmlUrl":73,"ownerBlogUrl":74,"ownerDescription":75,"updatedAt":76,"install":77,"resources":90},"01M6ADTPHA1E9ADMV2XQG5MX8A","mlops-weights-and-biases",null,"Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform","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-weights-and-biases\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"weights and biases","mlops","mlops weights and biases","automatic","visualize","track","Real-Time Visualization","WandB","Model Registry","Experiment Tracking","time","optimize","experiment","model","tuning","HuggingFace","logging","registry","visualization","training","ml","pytorch","collaboration","experiments","research","tensorflow","2026-11-10 10:12:30",false,"---\nname: mlops-weights-and-biases\ndescription: Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [MLOps, Weights And Biases, WandB, Experiment Tracking, Hyperparameter Tuning, Model Registry, Collaboration, Real-Time Visualization, PyTorch, TensorFlow, HuggingFace]\n  dependencies: [wandb]\n---\n\n# Weights & Biases: ML Experiment Tracking & MLOps\n\n## When to Use This Skill\n\nUse Weights & Biases (W&B) when you need to:\n- **Track ML experiments** with automatic metric logging\n- **Visualize training** in real-time dashboards\n- **Compare runs** across hyperparameters and configurations\n- **Optimize hyperparameters** with automated sweeps\n- **Manage model registry** with versioning and lineage\n- **Collaborate on ML projects** with team workspaces\n- **Track artifacts** (datasets, models, code) with lineage\n\n**Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+\n\n## Installation\n\n```bash\n# Install W&B\npip install wandb\n\n# Login (creates API key)\nwandb login\n\n# Or set API key programmatically\nexport WANDB_API_KEY=your_api_key_here\n```\n\n## Quick Start\n\n### Basic Experiment Tracking\n\n```python\nimport wandb\n\n# Initialize a run\nrun = wandb.init(\n    project=\"my-project\",\n    config={\n        \"learning_rate\": 0.001,\n        \"epochs\": 10,\n        \"batch_size\": 32,\n        \"architecture\": \"ResNet50\"\n    }\n)\n\n# Training loop\nfor epoch in range(run.config.epochs):\n    # Your training code\n    train_loss = train_epoch()\n    val_loss = validate()\n\n    # Log metrics\n    wandb.log({\n        \"epoch\": epoch,\n        \"train\u002Floss\": train_loss,\n        \"val\u002Floss\": val_loss,\n        \"train\u002Faccuracy\": train_acc,\n        \"val\u002Faccuracy\": val_acc\n    })\n\n# Finish the run\nwandb.finish()\n```\n\n### With PyTorch\n\n```python\nimport torch\nimport wandb\n\n# Initialize\nwandb.init(project=\"pytorch-demo\", config={\n    \"lr\": 0.001,\n    \"epochs\": 10\n})\n\n# Access config\nconfig = wandb.config\n\n# Training loop\nfor epoch in range(config.epochs):\n    for batch_idx, (data, target) in enumerate(train_loader):\n        # Forward pass\n        output = model(data)\n        loss = criterion(output, target)\n\n        # Backward pass\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # Log every 100 batches\n        if batch_idx % 100 == 0:\n            wandb.log({\n                \"loss\": loss.item(),\n                \"epoch\": epoch,\n                \"batch\": batch_idx\n            })\n\n# Save model\ntorch.save(model.state_dict(), \"model.pth\")\nwandb.save(\"model.pth\")  # Upload to W&B\n\nwandb.finish()\n```\n\n## Core Concepts\n\n### 1. Projects and Runs\n\n**Project**: Collection of related experiments\n**Run**: Single execution of your training script\n\n```python\n# Create\u002Fuse project\nrun = wandb.init(\n    project=\"image-classification\",\n    name=\"resnet50-experiment-1\",  # Optional run name\n    tags=[\"baseline\", \"resnet\"],    # Organize with tags\n    notes=\"First baseline run\"      # Add notes\n)\n\n# Each run has unique ID\nprint(f\"Run ID: {run.id}\")\nprint(f\"Run URL: {run.url}\")\n```\n\n### 2. Configuration Tracking\n\nTrack hyperparameters automatically:\n\n```python\nconfig = {\n    # Model architecture\n    \"model\": \"ResNet50\",\n    \"pretrained\": True,\n\n    # Training params\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n    \"epochs\": 50,\n    \"optimizer\": \"Adam\",\n\n    # Data params\n    \"dataset\": \"ImageNet\",\n    \"augmentation\": \"standard\"\n}\n\nwandb.init(project=\"my-project\", config=config)\n\n# Access config during training\nlr = wandb.config.learning_rate\nbatch_size = wandb.config.batch_size\n```\n\n### 3. Metric Logging\n\n```python\n# Log scalars\nwandb.log({\"loss\": 0.5, \"accuracy\": 0.92})\n\n# Log multiple metrics\nwandb.log({\n    \"train\u002Floss\": train_loss,\n    \"train\u002Faccuracy\": train_acc,\n    \"val\u002Floss\": val_loss,\n    \"val\u002Faccuracy\": val_acc,\n    \"learning_rate\": current_lr,\n    \"epoch\": epoch\n})\n\n# Log with custom x-axis\nwandb.log({\"loss\": loss}, step=global_step)\n\n# Log media (images, audio, video)\nwandb.log({\"examples\": [wandb.Image(img) for img in images]})\n\n# Log histograms\nwandb.log({\"gradients\": wandb.Histogram(gradients)})\n\n# Log tables\ntable = wandb.Table(columns=[\"id\", \"prediction\", \"ground_truth\"])\nwandb.log({\"predictions\": table})\n```\n\n### 4. Model Checkpointing\n\n```python\nimport torch\nimport wandb\n\n# Save model checkpoint\ncheckpoint = {\n    'epoch': epoch,\n    'model_state_dict': model.state_dict(),\n    'optimizer_state_dict': optimizer.state_dict(),\n    'loss': loss,\n}\n\ntorch.save(checkpoint, 'checkpoint.pth')\n\n# Upload to W&B\nwandb.save('checkpoint.pth')\n\n# Or use Artifacts (recommended)\nartifact = wandb.Artifact('model', type='model')\nartifact.add_file('checkpoint.pth')\nwandb.log_artifact(artifact)\n```\n\n## Hyperparameter Sweeps\n\nAutomatically search for optimal hyperparameters.\n\n### Define Sweep Configuration\n\n```python\nsweep_config = {\n    'method': 'bayes',  # or 'grid', 'random'\n    'metric': {\n        'name': 'val\u002Faccuracy',\n        'goal': 'maximize'\n    },\n    'parameters': {\n        'learning_rate': {\n            'distribution': 'log_uniform',\n            'min': 1e-5,\n            'max': 1e-1\n        },\n        'batch_size': {\n            'values': [16, 32, 64, 128]\n        },\n        'optimizer': {\n            'values': ['adam', 'sgd', 'rmsprop']\n        },\n        'dropout': {\n            'distribution': 'uniform',\n            'min': 0.1,\n            'max': 0.5\n        }\n    }\n}\n\n# Initialize sweep\nsweep_id = wandb.sweep(sweep_config, project=\"my-project\")\n```\n\n### Define Training Function\n\n```python\ndef train():\n    # Initialize run\n    run = wandb.init()\n\n    # Access sweep parameters\n    lr = wandb.config.learning_rate\n    batch_size = wandb.config.batch_size\n    optimizer_name = wandb.config.optimizer\n\n    # Build model with sweep config\n    model = build_model(wandb.config)\n    optimizer = get_optimizer(optimizer_name, lr)\n\n    # Training loop\n    for epoch in range(NUM_EPOCHS):\n        train_loss = train_epoch(model, optimizer, batch_size)\n        val_acc = validate(model)\n\n        # Log metrics\n        wandb.log({\n            \"train\u002Floss\": train_loss,\n            \"val\u002Faccuracy\": val_acc\n        })\n\n# Run sweep\nwandb.agent(sweep_id, function=train, count=50)  # Run 50 trials\n```\n\n### Sweep Strategies\n\n```python\n# Grid search - exhaustive\nsweep_config = {\n    'method': 'grid',\n    'parameters': {\n        'lr': {'values': [0.001, 0.01, 0.1]},\n        'batch_size': {'values': [16, 32, 64]}\n    }\n}\n\n# Random search\nsweep_config = {\n    'method': 'random',\n    'parameters': {\n        'lr': {'distribution': 'uniform', 'min': 0.0001, 'max': 0.1},\n        'dropout': {'distribution': 'uniform', 'min': 0.1, 'max': 0.5}\n    }\n}\n\n# Bayesian optimization (recommended)\nsweep_config = {\n    'method': 'bayes',\n    'metric': {'name': 'val\u002Floss', 'goal': 'minimize'},\n    'parameters': {\n        'lr': {'distribution': 'log_uniform', 'min': 1e-5, 'max': 1e-1}\n    }\n}\n```\n\n## Artifacts\n\nTrack datasets, models, and other files with lineage.\n\n### Log Artifacts\n\n```python\n# Create artifact\nartifact = wandb.Artifact(\n    name='training-dataset',\n    type='dataset',\n    description='ImageNet training split',\n    metadata={'size': '1.2M images', 'split': 'train'}\n)\n\n# Add files\nartifact.add_file('data\u002Ftrain.csv')\nartifact.add_dir('data\u002Fimages\u002F')\n\n# Log artifact\nwandb.log_artifact(artifact)\n```\n\n### Use Artifacts\n\n```python\n# Download and use artifact\nrun = wandb.init(project=\"my-project\")\n\n# Download artifact\nartifact = run.use_artifact('training-dataset:latest')\nartifact_dir = artifact.download()\n\n# Use the data\ndata = load_data(f\"{artifact_dir}\u002Ftrain.csv\")\n```\n\n### Model Registry\n\n```python\n# Log model as artifact\nmodel_artifact = wandb.Artifact(\n    name='resnet50-model',\n    type='model',\n    metadata={'architecture': 'ResNet50', 'accuracy': 0.95}\n)\n\nmodel_artifact.add_file('model.pth')\nwandb.log_artifact(model_artifact, aliases=['best', 'production'])\n\n# Link to model registry\nrun.link_artifact(model_artifact, 'model-registry\u002Fproduction-models')\n```\n\n## Integration Examples\n\n### HuggingFace Transformers\n\n```python\nfrom transformers import Trainer, TrainingArguments\nimport wandb\n\n# Initialize W&B\nwandb.init(project=\"hf-transformers\")\n\n# Training arguments with W&B\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Fresults\",\n    report_to=\"wandb\",  # Enable W&B logging\n    run_name=\"bert-finetuning\",\n    logging_steps=100,\n    save_steps=500\n)\n\n# Trainer automatically logs to W&B\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset\n)\n\ntrainer.train()\n```\n\n### PyTorch Lightning\n\n```python\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.loggers import WandbLogger\nimport wandb\n\n# Create W&B logger\nwandb_logger = WandbLogger(\n    project=\"lightning-demo\",\n    log_model=True  # Log model checkpoints\n)\n\n# Use with Trainer\ntrainer = Trainer(\n    logger=wandb_logger,\n    max_epochs=10\n)\n\ntrainer.fit(model, datamodule=dm)\n```\n\n### Keras\u002FTensorFlow\n\n```python\nimport wandb\nfrom wandb.keras import WandbCallback\n\n# Initialize\nwandb.init(project=\"keras-demo\")\n\n# Add callback\nmodel.fit(\n    x_train, y_train,\n    validation_data=(x_val, y_val),\n    epochs=10,\n    callbacks=[WandbCallback()]  # Auto-logs metrics\n)\n```\n\n## Visualization & Analysis\n\n### Custom Charts\n\n```python\n# Log custom visualizations\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nax.plot(x, y)\nwandb.log({\"custom_plot\": wandb.Image(fig)})\n\n# Log confusion matrix\nwandb.log({\"conf_mat\": wandb.plot.confusion_matrix(\n    probs=None,\n    y_true=ground_truth,\n    preds=predictions,\n    class_names=class_names\n)})\n```\n\n### Reports\n\nCreate shareable reports in W&B UI:\n- Combine runs, charts, and text\n- Markdown support\n- Embeddable visualizations\n- Team collaboration\n\n## Best Practices\n\n### 1. Organize with Tags and Groups\n\n```python\nwandb.init(\n    project=\"my-project\",\n    tags=[\"baseline\", \"resnet50\", \"imagenet\"],\n    group=\"resnet-experiments\",  # Group related runs\n    job_type=\"train\"             # Type of job\n)\n```\n\n### 2. Log Everything Relevant\n\n```python\n# Log system metrics\nwandb.log({\n    \"gpu\u002Futil\": gpu_utilization,\n    \"gpu\u002Fmemory\": gpu_memory_used,\n    \"cpu\u002Futil\": cpu_utilization\n})\n\n# Log code version\nwandb.log({\"git_commit\": git_commit_hash})\n\n# Log data splits\nwandb.log({\n    \"data\u002Ftrain_size\": len(train_dataset),\n    \"data\u002Fval_size\": len(val_dataset)\n})\n```\n\n### 3. Use Descriptive Names\n\n```python\n# ✅ Good: Descriptive run names\nwandb.init(\n    project=\"nlp-classification\",\n    name=\"bert-base-lr0.001-bs32-epoch10\"\n)\n\n# ❌ Bad: Generic names\nwandb.init(project=\"nlp\", name=\"run1\")\n```\n\n### 4. Save Important Artifacts\n\n```python\n# Save final model\nartifact = wandb.Artifact('final-model', type='model')\nartifact.add_file('model.pth')\nwandb.log_artifact(artifact)\n\n# Save predictions for analysis\npredictions_table = wandb.Table(\n    columns=[\"id\", \"input\", \"prediction\", \"ground_truth\"],\n    data=predictions_data\n)\nwandb.log({\"predictions\": predictions_table})\n```\n\n### 5. Use Offline Mode for Unstable Connections\n\n```python\nimport os\n\n# Enable offline mode\nos.environ[\"WANDB_MODE\"] = \"offline\"\n\nwandb.init(project=\"my-project\")\n# ... your code ...\n\n# Sync later\n# wandb sync \u003Crun_directory>\n```\n\n## Team Collaboration\n\n### Share Runs\n\n```python\n# Runs are automatically shareable via URL\nrun = wandb.init(project=\"team-project\")\nprint(f\"Share this URL: {run.url}\")\n```\n\n### Team Projects\n\n- Create team account at wandb.ai\n- Add team members\n- Set project visibility (private\u002Fpublic)\n- Use team-level artifacts and model registry\n\n## Pricing\n\n- **Free**: Unlimited public projects, 100GB storage\n- **Academic**: Free for students\u002Fresearchers\n- **Teams**: $50\u002Fseat\u002Fmonth, private projects, unlimited storage\n- **Enterprise**: Custom pricing, on-prem options\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.wandb.ai\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb (10.5k+ stars)\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fwandb\u002Fexamples\n- **Community**: https:\u002F\u002Fwandb.ai\u002Fcommunity\n- **Discord**: https:\u002F\u002Fwandb.me\u002Fdiscord\n\n## See Also\n\n- `references\u002Fsweeps.md` - Comprehensive hyperparameter optimization guide\n- `references\u002Fartifacts.md` - Data and model versioning patterns\n- `references\u002Fintegrations.md` - Framework-specific examples\n\n\n","# Weights & Biases: ML Experiment Tracking & MLOps\n\n## When to Use This Skill\n\nUse Weights & Biases (W&B) when you need to:\n- **Track ML experiments** with automatic metric logging\n- **Visualize training** in real-time dashboards\n- **Compare runs** across hyperparameters and configurations\n- **Optimize hyperparameters** with automated sweeps\n- **Manage model registry** with versioning and lineage\n- **Collaborate on ML projects** with team workspaces\n- **Track artifacts** (datasets, models, code) with lineage\n\n**Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+\n\n## Installation\n\n```bash\n# Install W&B\npip install wandb\n\n# Login (creates API key)\nwandb login\n\n# Or set API key programmatically\nexport WANDB_API_KEY=your_api_key_here\n```\n\n## Quick Start\n\n### Basic Experiment Tracking\n\n```python\nimport wandb\n\n# Initialize a run\nrun = wandb.init(\n    project=\"my-project\",\n    config={\n        \"learning_rate\": 0.001,\n        \"epochs\": 10,\n        \"batch_size\": 32,\n        \"architecture\": \"ResNet50\"\n    }\n)\n\n# Training loop\nfor epoch in range(run.config.epochs):\n    # Your training code\n    train_loss = train_epoch()\n    val_loss = validate()\n\n    # Log metrics\n    wandb.log({\n        \"epoch\": epoch,\n        \"train\u002Floss\": train_loss,\n        \"val\u002Floss\": val_loss,\n        \"train\u002Faccuracy\": train_acc,\n        \"val\u002Faccuracy\": val_acc\n    })\n\n# Finish the run\nwandb.finish()\n```\n\n### With PyTorch\n\n```python\nimport torch\nimport wandb\n\n# Initialize\nwandb.init(project=\"pytorch-demo\", config={\n    \"lr\": 0.001,\n    \"epochs\": 10\n})\n\n# Access config\nconfig = wandb.config\n\n# Training loop\nfor epoch in range(config.epochs):\n    for batch_idx, (data, target) in enumerate(train_loader):\n        # Forward pass\n        output = model(data)\n        loss = criterion(output, target)\n\n        # Backward pass\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # Log every 100 batches\n        if batch_idx % 100 == 0:\n            wandb.log({\n                \"loss\": loss.item(),\n                \"epoch\": epoch,\n                \"batch\": batch_idx\n            })\n\n# Save model\ntorch.save(model.state_dict(), \"model.pth\")\nwandb.save(\"model.pth\")  # Upload to W&B\n\nwandb.finish()\n```\n\n## Core Concepts\n\n### 1. Projects and Runs\n\n**Project**: Collection of related experiments\n**Run**: Single execution of your training script\n\n```python\n# Create\u002Fuse project\nrun = wandb.init(\n    project=\"image-classification\",\n    name=\"resnet50-experiment-1\",  # Optional run name\n    tags=[\"baseline\", \"resnet\"],    # Organize with tags\n    notes=\"First baseline run\"      # Add notes\n)\n\n# Each run has unique ID\nprint(f\"Run ID: {run.id}\")\nprint(f\"Run URL: {run.url}\")\n```\n\n### 2. Configuration Tracking\n\nTrack hyperparameters automatically:\n\n```python\nconfig = {\n    # Model architecture\n    \"model\": \"ResNet50\",\n    \"pretrained\": True,\n\n    # Training params\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n    \"epochs\": 50,\n    \"optimizer\": \"Adam\",\n\n    # Data params\n    \"dataset\": \"ImageNet\",\n    \"augmentation\": \"standard\"\n}\n\nwandb.init(project=\"my-project\", config=config)\n\n# Access config during training\nlr = wandb.config.learning_rate\nbatch_size = wandb.config.batch_size\n```\n\n### 3. Metric Logging\n\n```python\n# Log scalars\nwandb.log({\"loss\": 0.5, \"accuracy\": 0.92})\n\n# Log multiple metrics\nwandb.log({\n    \"train\u002Floss\": train_loss,\n    \"train\u002Faccuracy\": train_acc,\n    \"val\u002Floss\": val_loss,\n    \"val\u002Faccuracy\": val_acc,\n    \"learning_rate\": current_lr,\n    \"epoch\": epoch\n})\n\n# Log with custom x-axis\nwandb.log({\"loss\": loss}, step=global_step)\n\n# Log media (images, audio, video)\nwandb.log({\"examples\": [wandb.Image(img) for img in images]})\n\n# Log histograms\nwandb.log({\"gradients\": wandb.Histogram(gradients)})\n\n# Log tables\ntable = wandb.Table(columns=[\"id\", \"prediction\", \"ground_truth\"])\nwandb.log({\"predictions\": table})\n```\n\n### 4. Model Checkpointing\n\n```python\nimport torch\nimport wandb\n\n# Save model checkpoint\ncheckpoint = {\n    'epoch': epoch,\n    'model_state_dict': model.state_dict(),\n    'optimizer_state_dict': optimizer.state_dict(),\n    'loss': loss,\n}\n\ntorch.save(checkpoint, 'checkpoint.pth')\n\n# Upload to W&B\nwandb.save('checkpoint.pth')\n\n# Or use Artifacts (recommended)\nartifact = wandb.Artifact('model', type='model')\nartifact.add_file('checkpoint.pth')\nwandb.log_artifact(artifact)\n```\n\n## Hyperparameter Sweeps\n\nAutomatically search for optimal hyperparameters.\n\n### Define Sweep Configuration\n\n```python\nsweep_config = {\n    'method': 'bayes',  # or 'grid', 'random'\n    'metric': {\n        'name': 'val\u002Faccuracy',\n        'goal': 'maximize'\n    },\n    'parameters': {\n        'learning_rate': {\n            'distribution': 'log_uniform',\n            'min': 1e-5,\n            'max': 1e-1\n        },\n        'batch_size': {\n            'values': [16, 32, 64, 128]\n        },\n        'optimizer': {\n            'values': ['adam', 'sgd', 'rmsprop']\n        },\n        'dropout': {\n            'distribution': 'uniform',\n            'min': 0.1,\n            'max': 0.5\n        }\n    }\n}\n\n# Initialize sweep\nsweep_id = wandb.sweep(sweep_config, project=\"my-project\")\n```\n\n### Define Training Function\n\n```python\ndef train():\n    # Initialize run\n    run = wandb.init()\n\n    # Access sweep parameters\n    lr = wandb.config.learning_rate\n    batch_size = wandb.config.batch_size\n    optimizer_name = wandb.config.optimizer\n\n    # Build model with sweep config\n    model = build_model(wandb.config)\n    optimizer = get_optimizer(optimizer_name, lr)\n\n    # Training loop\n    for epoch in range(NUM_EPOCHS):\n        train_loss = train_epoch(model, optimizer, batch_size)\n        val_acc = validate(model)\n\n        # Log metrics\n        wandb.log({\n            \"train\u002Floss\": train_loss,\n            \"val\u002Faccuracy\": val_acc\n        })\n\n# Run sweep\nwandb.agent(sweep_id, function=train, count=50)  # Run 50 trials\n```\n\n### Sweep Strategies\n\n```python\n# Grid search - exhaustive\nsweep_config = {\n    'method': 'grid',\n    'parameters': {\n        'lr': {'values': [0.001, 0.01, 0.1]},\n        'batch_size': {'values': [16, 32, 64]}\n    }\n}\n\n# Random search\nsweep_config = {\n    'method': 'random',\n    'parameters': {\n        'lr': {'distribution': 'uniform', 'min': 0.0001, 'max': 0.1},\n        'dropout': {'distribution': 'uniform', 'min': 0.1, 'max': 0.5}\n    }\n}\n\n# Bayesian optimization (recommended)\nsweep_config = {\n    'method': 'bayes',\n    'metric': {'name': 'val\u002Floss', 'goal': 'minimize'},\n    'parameters': {\n        'lr': {'distribution': 'log_uniform', 'min': 1e-5, 'max': 1e-1}\n    }\n}\n```\n\n## Artifacts\n\nTrack datasets, models, and other files with lineage.\n\n### Log Artifacts\n\n```python\n# Create artifact\nartifact = wandb.Artifact(\n    name='training-dataset',\n    type='dataset',\n    description='ImageNet training split',\n    metadata={'size': '1.2M images', 'split': 'train'}\n)\n\n# Add files\nartifact.add_file('data\u002Ftrain.csv')\nartifact.add_dir('data\u002Fimages\u002F')\n\n# Log artifact\nwandb.log_artifact(artifact)\n```\n\n### Use Artifacts\n\n```python\n# Download and use artifact\nrun = wandb.init(project=\"my-project\")\n\n# Download artifact\nartifact = run.use_artifact('training-dataset:latest')\nartifact_dir = artifact.download()\n\n# Use the data\ndata = load_data(f\"{artifact_dir}\u002Ftrain.csv\")\n```\n\n### Model Registry\n\n```python\n# Log model as artifact\nmodel_artifact = wandb.Artifact(\n    name='resnet50-model',\n    type='model',\n    metadata={'architecture': 'ResNet50', 'accuracy': 0.95}\n)\n\nmodel_artifact.add_file('model.pth')\nwandb.log_artifact(model_artifact, aliases=['best', 'production'])\n\n# Link to model registry\nrun.link_artifact(model_artifact, 'model-registry\u002Fproduction-models')\n```\n\n## Integration Examples\n\n### HuggingFace Transformers\n\n```python\nfrom transformers import Trainer, TrainingArguments\nimport wandb\n\n# Initialize W&B\nwandb.init(project=\"hf-transformers\")\n\n# Training arguments with W&B\ntraining_args = TrainingArguments(\n    output_dir=\".\u002Fresults\",\n    report_to=\"wandb\",  # Enable W&B logging\n    run_name=\"bert-finetuning\",\n    logging_steps=100,\n    save_steps=500\n)\n\n# Trainer automatically logs to W&B\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset\n)\n\ntrainer.train()\n```\n\n### PyTorch Lightning\n\n```python\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.loggers import WandbLogger\nimport wandb\n\n# Create W&B logger\nwandb_logger = WandbLogger(\n    project=\"lightning-demo\",\n    log_model=True  # Log model checkpoints\n)\n\n# Use with Trainer\ntrainer = Trainer(\n    logger=wandb_logger,\n    max_epochs=10\n)\n\ntrainer.fit(model, datamodule=dm)\n```\n\n### Keras\u002FTensorFlow\n\n```python\nimport wandb\nfrom wandb.keras import WandbCallback\n\n# Initialize\nwandb.init(project=\"keras-demo\")\n\n# Add callback\nmodel.fit(\n    x_train, y_train,\n    validation_data=(x_val, y_val),\n    epochs=10,\n    callbacks=[WandbCallback()]  # Auto-logs metrics\n)\n```\n\n## Visualization & Analysis\n\n### Custom Charts\n\n```python\n# Log custom visualizations\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nax.plot(x, y)\nwandb.log({\"custom_plot\": wandb.Image(fig)})\n\n# Log confusion matrix\nwandb.log({\"conf_mat\": wandb.plot.confusion_matrix(\n    probs=None,\n    y_true=ground_truth,\n    preds=predictions,\n    class_names=class_names\n)})\n```\n\n### Reports\n\nCreate shareable reports in W&B UI:\n- Combine runs, charts, and text\n- Markdown support\n- Embeddable visualizations\n- Team collaboration\n\n## Best Practices\n\n### 1. Organize with Tags and Groups\n\n```python\nwandb.init(\n    project=\"my-project\",\n    tags=[\"baseline\", \"resnet50\", \"imagenet\"],\n    group=\"resnet-experiments\",  # Group related runs\n    job_type=\"train\"             # Type of job\n)\n```\n\n### 2. Log Everything Relevant\n\n```python\n# Log system metrics\nwandb.log({\n    \"gpu\u002Futil\": gpu_utilization,\n    \"gpu\u002Fmemory\": gpu_memory_used,\n    \"cpu\u002Futil\": cpu_utilization\n})\n\n# Log code version\nwandb.log({\"git_commit\": git_commit_hash})\n\n# Log data splits\nwandb.log({\n    \"data\u002Ftrain_size\": len(train_dataset),\n    \"data\u002Fval_size\": len(val_dataset)\n})\n```\n\n### 3. Use Descriptive Names\n\n```python\n# ✅ Good: Descriptive run names\nwandb.init(\n    project=\"nlp-classification\",\n    name=\"bert-base-lr0.001-bs32-epoch10\"\n)\n\n# ❌ Bad: Generic names\nwandb.init(project=\"nlp\", name=\"run1\")\n```\n\n### 4. Save Important Artifacts\n\n```python\n# Save final model\nartifact = wandb.Artifact('final-model', type='model')\nartifact.add_file('model.pth')\nwandb.log_artifact(artifact)\n\n# Save predictions for analysis\npredictions_table = wandb.Table(\n    columns=[\"id\", \"input\", \"prediction\", \"ground_truth\"],\n    data=predictions_data\n)\nwandb.log({\"predictions\": predictions_table})\n```\n\n### 5. Use Offline Mode for Unstable Connections\n\n```python\nimport os\n\n# Enable offline mode\nos.environ[\"WANDB_MODE\"] = \"offline\"\n\nwandb.init(project=\"my-project\")\n# ... your code ...\n\n# Sync later\n# wandb sync \u003Crun_directory>\n```\n\n## Team Collaboration\n\n### Share Runs\n\n```python\n# Runs are automatically shareable via URL\nrun = wandb.init(project=\"team-project\")\nprint(f\"Share this URL: {run.url}\")\n```\n\n### Team Projects\n\n- Create team account at wandb.ai\n- Add team members\n- Set project visibility (private\u002Fpublic)\n- Use team-level artifacts and model registry\n\n## Pricing\n\n- **Free**: Unlimited public projects, 100GB storage\n- **Academic**: Free for students\u002Fresearchers\n- **Teams**: $50\u002Fseat\u002Fmonth, private projects, unlimited storage\n- **Enterprise**: Custom pricing, on-prem options\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.wandb.ai\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb (10.5k+ stars)\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fwandb\u002Fexamples\n- **Community**: https:\u002F\u002Fwandb.ai\u002Fcommunity\n- **Discord**: https:\u002F\u002Fwandb.me\u002Fdiscord\n\n## See Also\n\n- `references\u002Fsweeps.md` - Comprehensive hyperparameter optimization guide\n- `references\u002Fartifacts.md` - Data and model versioning patterns\n- `references\u002Fintegrations.md` - Framework-specific examples\n\n\n",{"name":5,"license":15,"metadata":55,"description":7},{"tags":56,"author":63,"version":64,"dependencies":65},[57,58,31,33,59,32,60,30,61,62,39],"MLOps","Weights And Biases","Hyperparameter Tuning","Collaboration","PyTorch","TensorFlow","Orchestra Research","1.0.0",[66],"wandb","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-weights-and-biases",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":78,"prompt":88,"zipAvailable":89},[79,82,85],{"tool":80,"command":81},"npx","npx skills add davila7\u002Fclaude-code-templates --skill mlops-weights-and-biases",{"tool":83,"command":84},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill mlops-weights-and-biases",{"tool":86,"command":87},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill mlops-weights-and-biases","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-weights-and-biases\u002FSKILL.md",true,[91,97,101,105,109,115,119,123,129,133],{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"REFERENCE.md","REFERENCE","参考文档",9729,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002FREFERENCE.md",{"path":98,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fartifacts.md",13425,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-weights-and-biases\u002Freferences\u002Fartifacts.md",{"path":102,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fintegrations.md",16219,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-weights-and-biases\u002Freferences\u002Fintegrations.md",{"path":106,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fsweeps.md",17675,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmlops-weights-and-biases\u002Freferences\u002Fsweeps.md",{"path":110,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fi18n-diagnostic.php","SCRIPT","脚本",2523,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Fscripts\u002Fi18n-diagnostic.php",{"path":116,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":117,"sourceHtmlUrl":118},"templates\u002Fci-i18n-job.yml",1643,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Ftemplates\u002Fci-i18n-job.yml",{"path":120,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":121,"sourceHtmlUrl":122},"templates\u002Fcomposer-i18n-scripts.json",637,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Ftemplates\u002Fcomposer-i18n-scripts.json",{"path":124,"resourceType":125,"resourceTypeDesc":126,"sizeBytes":127,"sourceHtmlUrl":128},"templates\u002Flocale-fallback.php","OTHER","其他",4132,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Ftemplates\u002Flocale-fallback.php",{"path":130,"resourceType":125,"resourceTypeDesc":126,"sizeBytes":131,"sourceHtmlUrl":132},"templates\u002Fphpcs-i18n.xml",705,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Ftemplates\u002Fphpcs-i18n.xml",{"path":134,"resourceType":125,"resourceTypeDesc":126,"sizeBytes":135,"sourceHtmlUrl":136},"templates\u002Fplugin-header.php",471,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fwp-plugin-i18n\u002Ftemplates\u002Fplugin-header.php",[138,167,194,227,262,286],{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":147,"publishedAt":50},"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",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[148,149,150,151,152,153,154,155,156,157,158,159,160,29,161,162,163,164,165,166],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","verdict","evm","solana","how","public","look",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":50},"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",[173],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},[175,176,177,178,179,180,181,182,183,184,185,157,186,187,188,189,190,191,192,193],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":209,"publishedAt":50},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[200,203,204],{"categoryNo":201,"categoryName":202,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":205,"categoryName":206,"taxonomyType":207,"taxonomyTypeDesc":208},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[210,211,212,213,214,215,216,153,217,218,219,220,221,222,223,156,224,225,157,226],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":231,"licenseFileUrl":232,"sourceHtmlUrl":233,"categories":234,"keywords":238,"publishedAt":50},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[235],{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,153,261],"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":263,"name":264,"nameZh":6,"description":265,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":231,"licenseFileUrl":266,"sourceHtmlUrl":267,"categories":268,"keywords":273,"publishedAt":50},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[269,272],{"categoryNo":270,"categoryName":271,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},[274,275,276,277,278,240,279,249,250,280,281,282,283,153,284,159,285],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":287,"name":288,"nameZh":6,"description":289,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":231,"licenseFileUrl":290,"sourceHtmlUrl":291,"categories":292,"keywords":300,"publishedAt":50},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[293,296,297],{"categoryNo":294,"categoryName":295,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":298,"categoryName":299,"taxonomyType":207,"taxonomyTypeDesc":208},"01JBSEEDCAT000000000000036","内容创作者",[301,302,303,304,305,306,307,308,242,309,252,186,310,311,312,313,314,222,315,157,316,317,318,319,320,164,321],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]