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AI EngineeringTeX

TransformerLens Interpretability

by Orchestra-Research

TransformerLens Interpretability is an AI Engineering skill for Claude Code, published by Orchestra-Research in AI-Research-SKILLs.

11.6K stars838 forkson Orchestra-Research/AI-Research-SKILLsAdded 2026/07/19+1% in starsRepository updated 2026/06/16
View source on GitHubDownload repo ZIP
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Install in seconds
Install TransformerLens Interpretability
Copy TransformerLens Interpretability into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/04-mechanistic-interpretability/transformer-lens ~/.claude/skills/transformer-lens

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Orchestra-Research/AI-Research-SKILLs.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
04-mechanistic-interpretability/transformer-lens/SKILL.md in Orchestra-Research/AI-Research-SKILLs
Installs to
~/.claude/skills/transformer-lens
Collection
One of 50 skills cataloged from this repository
Category
AI Engineering — 3670 skills

What TransformerLens Interpretability does

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

TransformerLens Interpretability is cataloged under AI Engineering on DirSkills. TransformerLens Interpretability comes from a repository tagged ai, ai-research, claude, claude-code and claude-skills.

Documentation

README

TransformerLens: Mechanistic Interpretability for Transformers

TransformerLens is the de facto standard library for mechanistic interpretability research on GPT-style language models. Created by Neel Nanda and maintained by Bryce Meyer, it provides clean interfaces to inspect and manipulate model internals via HookPoints on every activation.

GitHub: TransformerLensOrg/TransformerLens (2,900+ stars)

When to Use TransformerLens

This is the opening of the README. Read the full README on GitHub.

Frequently asked about TransformerLens Interpretability

  • What else does Orchestra-Research publish alongside TransformerLens Interpretability?

    TransformerLens Interpretability is one of 50 skills that DirSkills catalogs from Orchestra-Research/AI-Research-SKILLs, the repository it ships in. Its siblings there include AWQ Quantization, Autoresearch and Axolotl. Each one is a separate skill with its own page in this directory, installs the same way TransformerLens Interpretability does, and is maintained by Orchestra-Research in that same repository. The rest of the collection is listed on the Orchestra-Research/AI-Research-SKILLs page.

  • How does TransformerLens Interpretability compare to other AI Engineering skills?

    TransformerLens Interpretability ranks #367 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of TransformerLens Interpretability against them. Open each page to compare what they document and how they install.

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