Weights & Biases Experiment Tracking
Log ML training runs, metrics, and artifacts to W&B and run hyperparameter sweeps.
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Stars
- 14,195
- Licence
- MIT
- Files
- 4 files
What it does
When this skill is installed, the agent knows how to add Weights & Biases (W&B), a hosted tool for machine learning experiment tracking, to a training script. It can write code that starts a run, records settings and metrics such as loss and accuracy, saves checkpoints and datasets as versioned artifacts, and sets up automated hyperparameter sweeps. The results appear in an online dashboard where runs can be compared and shared with a team.
Great for
- ML engineers who want to track and compare training runs in a web dashboard
- Researchers setting up hyperparameter searches with grid, random, or Bayesian sweeps
- Teams that need shared records of experiments, model checkpoints, and dataset versions
Skip it if
- Projects that must keep training logs fully offline or private, since the workflow sends metrics and uploaded files to W&B servers and requires an account and API key
- Non-ML software work, because the skill only covers machine learning experiment tracking
Try asking
- “Add W&B logging to my PyTorch training loop to track loss and accuracy per epoch”
- “Set up a Bayesian hyperparameter sweep over learning rate, batch size and dropout”
- “Save my model checkpoint as a W&B artifact with versioning”
- “Compare my last five runs by config and validation accuracy”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 4 original files, the MIT licence and an attribution note.
weights-and-biases.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip weights-and-biases.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip weights-and-biases.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Store your API key in an environment variable rather than pasting it into code or chat.
- Review what the agent logs and uploads with wandb.save or artifacts, since uploaded files leave your machine.
- Ask for a small test sweep count first to confirm the setup before launching large searches.
What's inside
- references/artifacts.md
- references/integrations.md
- references/sweeps.md
- SKILL.md
Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.