> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use W&B Skills

> Install W&B Skills to teach your coding agent how to train models, build agents, and analyze experiments using W&B's AI development platform.

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](https://github.com/vercel-labs/skills#supported-agents).

## W\&B Skills capabilities

Skills covers both the [W\&B Models SDK](/models/ref) (training runs, metrics, artifacts, sweeps) and the [Weave SDK](/weave/reference/python-sdk) (traces, evaluations, scorers). Includes helper libraries, reference docs, and data analysis patterns.

| Workflow           | Capabilities                                                                                                                                                                                                                  |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Model training** | <ul><li>Log metrics and rich media during training and fine-tuning.</li><li>Track and compare experiments.</li><li>Analyze runs and results, such as loss curves and accuracy scores.</li><li>Tune hyperparameters.</li></ul> |
| **Agent building** | <ul><li>Trace agentic AI applications.</li><li>Analyze traces and classify failure modes.</li><li>Evaluate models and agents with labeled datasets.</li><li>Run online evaluations for production monitoring.</li></ul>       |

## Prerequisites

Skills requires the following:

* [Node.js](https://nodejs.org/) (for the `npx` command).
* A W\&B API key. Create one at [wandb.ai/authorize](https://wandb.ai/authorize) and then set it as an environment variable:
  ```shell theme={null}
  export WANDB_API_KEY=<your-api-key>
  ```
* (Optional) Set your W\&B project name as a `WANDB_PROJECT` environment variable. This allows your agent to target the correct W\&B project without you specifying it each time.

## Install W\&B Skills

To install W\&B Skills globally, run the following command with the `--global` flag:

```bash theme={null}
npx skills add wandb/skills --skill '*' --yes --global
```

To install Skills for a specific project, run the following command from your project directory:

```bash theme={null}
npx skills add wandb/skills --skill '*' --yes
```

You can also install Skills for specific agents using the `--agent` flag:

```bash theme={null}
npx skills add wandb/skills --agent claude-code --skill '*' --yes --global
```

For list of `--agent` and `--skill` options, see the [skills CLI documentation](https://github.com/vercel-labs/skills#supported-agents).

## 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 when you use more specific queries versus broader open-ended questions. The following table provides some recommended example prompts versus prompts that are too vague.

| Recommended                                             | Not recommended                 |
| ------------------------------------------------------- | ------------------------------- |
| "What is the final validation loss for my last 5 runs?" | "How is my model doing?"        |
| "Summarize the token usage across my last 10 traces."   | "Show me all my traces."        |
| "Compare the configs of run A and run B."               | "What are my best runs?"        |
| "What eval had the highest F1 score?"                   | "How are my evaluations going?" |