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Transformer Lens Interpretability

Listed
davila7·vlatest·ai-ml·MIT

This skill provides insights into Transformer models’ decision-making by highlighting influential input tokens, aiding in debugging and understanding predictions.

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 transformer-lens-interpretability -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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • cli-tool/components/skills/ai-research/mechanistic-interpretability-transformer-lens/SKILL.md:41pip install straight from a URL
      pip install git+https://github.com/TransformerLensOrg/TransformerLens

  • 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.

  • 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-transformer-lens/SKILL.md
Scanned
4 files, 1,027 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.
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About this skill

What it does

This skill leverages the Transformer Lens library to provide interpretability tools for large language models. It allows users to visualize attention patterns, identify important neurons, and understand how different parts of a model contribute to its output. This facilitates debugging, understanding biases, and generally gaining insights into LLM behavior.

When to use it

  • Debugging unexpected model behavior: Investigate why an LLM is generating incorrect or nonsensical outputs by examining attention weights.
  • Identifying bias in language models: Pinpoint neurons that are strongly correlated with specific demographic groups or sensitive topics.
  • Understanding feature attribution: Determine which parts of the input sequence most influence a particular prediction made by the model.
  • Educational purposes: Visualize and explain how transformer networks process information to students or colleagues.

Key capabilities

  • Attention visualization
  • Neuron importance analysis
  • Feature attribution mapping
  • Integration with Transformer Lens library

Example prompts

  • "Show me the attention weights for this input sequence."
  • "Which neurons are most active when generating text about [topic]?"
  • "Explain how this model arrived at its prediction given this prompt."

Tips & gotchas

  • Requires a working environment with Transformer Lens installed.
  • Interpretability results can be complex and require some understanding of transformer architecture to interpret effectively.