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W&B Skills are reusable instruction sets that teach coding agents how to use W&B effectively. Instead of manually guiding your agent through W&B APIs and best practices, install Skills so that the agent can work with experiment tracking, tracing, evaluations, and monitoring on its own. Skills work with several major coding agents, including:
  • Claude Code
  • Codex
  • Cursor
  • GitHub Copilot
  • Gemini CLI
For a full list of supported agents, see the W&B Skills CLI documentation.

W&B Skills capabilities

Skills covers both the W&B Models SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (traces, evaluations, scorers). It includes helper libraries, reference docs, and data analysis patterns so your agent can handle the following workflows.

Prerequisites

Skills requires the following:
  • Node.js (for the npx command).
  • A W&B API key. Create one at wandb.ai/authorize and then set it as an environment variable. Replace [YOUR-API-KEY] with your API key:
  • Optional: Set your W&B project name as a WANDB_PROJECT environment variable. This lets your agent target the correct W&B project without you specifying it each time.

Install W&B Skills

Choose a global installation to make Skills available to all your projects, or a project-specific installation to scope Skills to a single project. To install W&B Skills globally for all your projects, use the --global flag:
To install Skills only for the current project, run the install command from your project directory without the --global flag:
You can also install Skills for specific agents using the --agent flag:
For a list of --agent and --skill options, see the skills CLI documentation. After installation completes, your agent has access to W&B Skills and is ready to handle W&B-related tasks.

Use W&B Skills

Once installed, you can ask the agent to perform W&B-related tasks for your project. The following example prompts demonstrate some of the tasks your agent can do with W&B Skills:
  • “Log training metrics for my PyTorch model to W&B.”
  • “Analyze the loss curves for my last 10 runs and identify the best performing configuration.”
  • “Trace my LangChain agent and log the results to Weave.”
  • “Run an evaluation on my agent using the test dataset and summarize the results.”
  • “Find the failure modes in my last evaluation and classify them.”
  • “Compare the configs of run A and run B and show me the differences.”

Usage tips

Skills performs better with specific queries than with broad, open-ended questions. The following table compares recommended prompts with prompts that are too vague.