weights-and-biases
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
npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Weights & Biases: ML Experiment Tracking & MLOps ## When to Use This Skill Use Weights & Biases (W&B) when you need to: - **Track ML experiments** with automatic metric logging - **Visualize training** in real-time dashboards - **Compare runs** across hyperparameters and configurations - **Optimize hyperparameters** with automated sweeps - **Manage model registry** with versioning and lineage - **Collaborate on ML projects** with team workspaces - **Track artifacts** (datasets, models, code) with lineage **Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+ ## Installation ```bash # Install W&B pip install wandb # Login (creates API key) wandb login # Or set API key programmatically export WANDB_API_KEY=your_api_key_here ``` ## Quick Start ### Basic Experiment Tracking ```python import wandb # Initialize a run run = wandb.init( project="my-project", config={ "learning_rate": 0.001, "epochs": 10, "batch_size": 32, "architecture": "ResNet50" } ) # Training loop for epoch in range(run.config.epochs): # Your training code train_loss = train_epoch() val_loss = validate() # Log metrics wandb.log({ "epoch": epoch, "train/loss": train_loss, "val/loss":
- When to Use This Skill
- Installation
- Quick Start
- Basic Experiment Tracking
- With PyTorch
- Core Concepts
- 1. Projects and Runs
- 2. Configuration Tracking
- 3. Metric Logging
- 4. Model Checkpointing
- Hyperparameter Sweeps
- Define Sweep Configuration
- Define Training Function
- Sweep Strategies
Install W&B pip install wandb Login (creates API key) wandb login Or set API key programmatically export WANDB_API_KEY=your_api_key_here
What does the weights-and-biases skill do?
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
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.