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Weights And Biases

Checked
davila7·vlatest·other·MIT

This skill streamlines ML workflow by automating data tracking & visualization within Weights & Biases, boosting experiment efficiency.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill weights-and-biases -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/mlops-weights-and-biases/SKILL.md
Scanned
4 files, 2,555 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

weights-and-biases

What it does

This skill enables AI agents to interact with Weights & Biases (W&B) for experiment tracking, model management, and visualization. It allows agents to log metrics, store artifacts, and analyze training runs directly within the workflow.

When to use it

  • Tracking hyperparameters and loss curves during iterative model development.
  • Storing and versioning large model checkpoints or generated datasets as artifacts.
  • Comparing performance across multiple training runs to identify optimal configurations.
  • Generating interactive dashboards for real-time monitoring of experiment health.

Key capabilities

  • Logging custom metrics and scalars to W&B experiments.
  • Uploading files, models, and data as tracked artifacts.
  • Creating and managing W&B projects programmatically.
  • Visualizing training history and comparing run distributions.

Example prompts

  • "Log the current accuracy and loss values for this training run in a new W&B experiment."
  • "Upload the latest model checkpoint to the 'production' project and tag it with version 2.1."
  • "Compare the training curves of the last three runs and highlight the one with the lowest final loss."

Tips & gotchas

Ensure your API key is securely configured in the agent's environment before attempting to log data or create projects. Large artifact uploads may require additional authentication scopes or bandwidth considerations depending on your W&B plan.