# transformer-lens-interpretability

This skill guides TransformerLens research on transformer internals, from activation caching and attention analysis to activation patching experiments.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 954 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/transformer-lens/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/transformer-lens-interpretability

## What it does

This skill outlines a mechanistic interpretability workflow with TransformerLens. It covers loading models through `HookedTransformer`, using `HookPoints` on activations, caching intermediate results, and reading entries such as `resid_post`, `pattern`, `mlp_out`, `q`, `k`, and `v`.

For activation patching, prepare clean and corrupted prompts, cache the clean run, define a metric, then test layer and position combinations. The material also covers attention patterns, information flow, and head-level logit attribution. The required dependencies are `transformer-lens>=2.0.0` and `torch>=2.0.0`. If you work with a non-transformer architecture, check alternatives such as `nnsight` or `pyvene` first.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens-interpretability -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/transformer-lens/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/transformer-lens/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/transformer-lens/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/transformer-lens/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs transformer-lens
```

2. **Manually: put the files in place**
- File: `.github/skills/transformer-lens/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 04-mechanistic-interpretability/transformer-lens
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/transformer-lens/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/transformer-lens/SKILL.md

## When to use it
- reverse-engineer algorithms learned by a model
- compare clean and corrupted model runs
- locate layers and positions that affect an output
- cache intermediate model activations
- measure attention-head contributions to logits

## Example prompts
- Show me how to run activation patching on `gpt2-small` with clean and corrupted prompts.
- Cache `resid_post` and `pattern` for this run, then explain how to compare them across layers.
- Run direct logit attribution for the IOI example and identify the heads with the largest contribution.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE c2a24bb23140b992
````text
---
name: transformer-lens-interpretability
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Mechanistic Interpretability, TransformerLens, Activation Patching, Circuit Analysis]
dependencies: [transformer-lens>=2.0.0, torch>=2.0.0]
---

# 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](https://github.com/TransformerLensOrg/TransformerLens) (2,900+ stars)

## When to Use TransformerLens

**Use TransformerLens when you need to:**
- Reverse-engineer algorithms learned during training
- Perform activation patching / causal tracing experiments
- Study attention patterns and information flow
- Analyze circuits (e.g., induction heads, IOI circuit)
- Cache and inspect intermediate activations
- Apply direct logit attribution

**Consider alternatives when:**
- You need to work with non-transformer architectures → Use **nnsight** or **pyvene**
- You want to train/analyze Sparse Autoencoders → Use **SAELens**
- You need remote execution on massive models → Use **nnsight** with NDIF
- You want higher-level causal intervention abstractions → Use **pyvene**

## Installation

```bash
pip install transformer-lens
```

For development version:
```bash
pip install git+https://github.com/TransformerLensOrg/TransformerLens
```

## Core Concepts

### HookedTransformer

The main class that wraps transformer models with HookPoints on every activation:

```python
from transformer_lens import HookedTransformer

# Load a model
model = HookedTransformer.from_pretrained("gpt2-small")

# For gated models (LLaMA, Mistral)
import os
os.environ["HF_TOKEN"] = "your_token"
model = HookedTransformer.from_pretrained("meta-llama/Llama-2-7b-hf")
```

### Supported Models (50+)

| Family | Models |
|--------|--------|
| GPT-2 | gpt2, gpt2-medium, gpt2-large, gpt2-xl |
| LLaMA | llama-7b, llama-13b, llama-2-7b, llama-2-13b |
| EleutherAI | pythia-70m to pythia-12b, gpt-neo, gpt-j-6b |
````
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