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Sparse Autoencoder Training

Checked
davila7·vlatest·ai-ml·MIT

This skill efficiently trains sparse autoencoders for dimensionality reduction and feature learning by optimizing for sparsity, saving time & resources.

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 sparse-autoencoder-training -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/mechanistic-interpretability-saelens/SKILL.md
Scanned
4 files, 1,037 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 trains sparse autoencoders. Sparse autoencoders are a type of neural network used for unsupervised learning, aiming to learn efficient representations of data while enforcing sparsity in the hidden layer activations. This can be useful for dimensionality reduction and feature extraction.

When to use it

  • Dimensionality Reduction: Reduce the number of features in a dataset while preserving important information.
  • Feature Extraction: Automatically discover meaningful features from raw data, such as images or text.
  • Anomaly Detection: Identify unusual patterns in data based on reconstruction error.
  • Data Denoising: Remove noise from datasets by learning to reconstruct clean versions of the input.

Key capabilities

  • Sparse autoencoder training
  • Unsupervised learning
  • Dimensionality reduction
  • Feature extraction

Example prompts

  • "Train a sparse autoencoder on this image dataset with a sparsity constraint of 0.1."
  • "Reduce the dimensionality of my data using a sparse autoencoder, aiming for 50 hidden units."
  • “Extract features from this text corpus using a sparse autoencoder.”

Tips & gotchas

  • Requires a suitable dataset for training. The quality and size of the dataset significantly impact performance.