Use Weights & Biases (W&B) when you need to:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionweights-and-biasesExecute the skills CLI command in your project's root directory to begin installation:
Fetches weights-and-biases from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate weights-and-biases. Access via /weights-and-biases in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Use Weights & Biases (W&B) when you need to:
Users: 200,000+ ML practitioners | GitHub Stars: 10.5k+ | Integrations: 100+
# 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
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": val_loss,
"train/accuracy": train_acc,
"val/accuracy": val_acc
})
# Finish the run
wandb.finish()
import torch
import wandb
# Initialize
wandb.init(project="pytorch-demo", config={
"lr": 0.001,
"epochs": 10
})
# Access config
config = wandb.config
# Training loop
for epoch in range(config.epochs):
for batch_idx, (data, target) in enumerate(train_loader):
# Forward pass
output = model(data)
loss = criterion(output, target)
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Log every 100 batches
if batch_idx % 100 == 0:
wandb.log({
"loss": loss.item(),
"epoch": epoch,
"batch": batch_idx
})
# Save model
torch.save(model.state_dict(), "model.pth")
wandb.save("model.pth") # Upload to W&B
wandb.finish()
Project: Collection of related experiments Run: Single execution of your training script
# Create/use project
run = wandb.init(
project="image-classification",
name="resnet50-experiment-1", # Optional run name
tags=["baseline", "resnet"], # Organize with tags
notes="First baseline run" # Add notes
)
# Each run has unique ID
print(f"Run ID: {run.id}")
print(f"Run URL: {run.url}")
Track hyperparameters automatically:
config = {
# Model architecture
"model": "ResNet50",
"pretrained": True,
# Training params
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 50,
"optimizer": "Adam",
# Data params
"dataset": "ImageNet",
"augmentation": "standard"
}
wandb.init(project="my-project", config=config)
# Access config during training
lr = wandb.config.learning_rate
batch_size = wandb.config.batch_size
# Log scalars
wandb.log({"loss": 0.5, "accuracy": 0.92})
# Log multiple metrics
wandb.log({
"train/loss": train_loss,
"train/accuracy": train_acc,
"val/loss": val_loss,
"val/accuracy": val_acc,
"learning_rate": current_lr,
"epoch": epoch
})
# Log with custom x-axis
wandb.log({"loss": loss}, step=global_step)
# Log media (images, audio, video)
wandb.log({"examples": [wandb.Image(img) for img in images]})
# Log histograms
wandb.log({"gradients": wandb.Histogram(gradients)})
# Log tables
table = wandb.Table(columns=["id", "prediction", "ground_truth"])
wandb.log({"predictions": table})
import torch
import wandb
# Save model checkpoint
checkpoint = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}
torch.save(checkpoint, 'checkpoint.pth')Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
weights-and-biases fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for weights-and-biases matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend weights-and-biases for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in weights-and-biases — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for weights-and-biases matched our evaluation — installs cleanly and behaves as described in the markdown.
weights-and-biases is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in weights-and-biases — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
weights-and-biases reduced setup friction for our internal harness; good balance of opinion and flexibility.
weights-and-biases has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: weights-and-biases is focused, and the summary matches what you get after install.
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