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Tensorboard

Checked
davila7·vlatest·ai-ml·MIT

This skill integrates TensorBoard for visualizing machine learning metrics during training, aiding in model debugging and performance analysis.

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 tensorboard -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-tensorboard/SKILL.md
Scanned
4 files, 2,236 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

What it does

This skill integrates TensorBoard, a visualization toolkit for machine learning, into your AI agent's workflow. It enables you to visualize training metrics (like loss and accuracy), debug models with histograms and distributions, compare different experiments, and analyze model architecture. TensorBoard helps understand the performance of your machine learning models during training and identify areas for improvement.

When to use it

  • Visualize how training metrics like loss and accuracy change over time.
  • Debug machine learning models by examining histograms and distributions of activations or weights.
  • Compare results from different experimental runs with varying hyperparameters.
  • Gain insights into model architecture through visualization of the computational graph.

Key capabilities

  • Visualization of training metrics (loss, accuracy).
  • Model debugging using histograms and distributions.
  • Experiment comparison across multiple runs.
  • Visualization of model graphs and architectures.
  • Projection of embeddings to lower dimensions (t-SNE, PCA).
  • Tracking hyperparameter experiments.
  • Performance profiling to identify bottlenecks.
  • Visualization of images and text during training.

Example prompts

  • "Can you show me the loss curve for my last experiment?"
  • "Visualize the distribution of weights in layer X."
  • "Compare the accuracy results between run A and run B."
  • "Show me a graph of the model architecture."

Tips & gotchas

  • Requires tensorboard to be installed (pip install tensorboard).
  • For PyTorch integration, also requires torch, torchvision, and tensorboard. For TensorFlow/Keras, it's included in the installation.
  • You need to specify a log directory (e.g., --logdir=runs) when launching TensorBoard after your training process has generated data.