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transformer-lens-interpretability

Analyze and manipulate transformer internals for mechanistic interpretability.

Updated May 4, 2026
Search Tags:#mechanistic interpretability#activation patching#transformerlens#circuit analysis#logit lens#induction heads
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill transformer-lens-interpretability-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: transformer-lens-interpretability
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/transformer-lens
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill transformer-lens-interpretability-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps researchers inspect how transformer models make decisions by exposing internal activations, attention patterns, and component contributions in a structured workflow.

Core Features & Use Cases

  • Activation Inspection: Load models and cache residual stream, attention, and MLP activations for layer-by-layer analysis.
  • Causal Intervention: Patch, ablate, or steer activations to test which internals actually change predictions.
  • Circuit Discovery: Study attention heads, induction behavior, and logit attribution when reverse-engineering model behavior.
  • Use Case: A researcher can compare clean and corrupted prompts, identify the layers or heads driving a target token, and validate the result with patching experiments.

Quick Start

Ask for a TransformerLens workflow that loads a model, caches activations for a chosen prompt, and highlights the most influential layers or heads for the target prediction.

Frequently Asked Questions about transformer-lens-interpretability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform activation patching to identify which transformer layers drive a specific prediction?▼

Activation patching identifies influential transformer layers by comparing clean and corrupted prompts, selectively patching cached activations via HookedTransformer hooks to test which internals actually change target token predictions.

What is mechanistic interpretability and how does circuit discovery work on GPT-style models?▼

Mechanistic interpretability reverse-engineers model behavior through circuit discovery, studying attention heads, induction behavior, and logit attribution to expose component contributions within transformer internals.

How can I detect induction heads and inspect attention patterns in a language model?▼

Detect induction heads by loading GPT-style models into TransformerLens, caching attention pattern activations, and analyzing logit lens inspection across layers and positions to reveal induction behavior.

Can I steer or ablate transformer activations to test causal interventions on model behavior?▼

Steering and ablating transformer activations tests causal interventions by selectively injecting hooks to patch, ablate, or manipulate cached residual stream and MLP activations, validating which components drive predictions.

Does this mechanistic interpretability workflow require specific dependencies or environments to run?▼

This workflow requires no external dependencies and operates by loading GPT-style models into a HookedTransformer environment, utilizing activation caching and matrix-based attribution across layers, heads, and positions.

What is the best way to analyze logit attribution and residual stream contributions across transformer layers?▼

Analyze logit attribution and residual stream contributions by caching activations layer-by-layer, applying matrix-based attribution across layers and positions, and highlighting the most influential components for target predictions.

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