transformer-lens-interpretability

8.7

303

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.

interpretability

8.7

Rating

Installs

AI & LLM

Quick Review

Excellent skill for mechanistic interpretability with TransformerLens. The description accurately captures the skill's scope (inspecting/manipulating transformer internals via HookPoints). Task knowledge is comprehensive with three detailed workflows (activation patching, circuit analysis, induction heads), concrete code examples, common pitfalls, and clear decision criteria for when to use alternatives. Structure is well-organized with a logical flow from basics to advanced topics, plus references to external files for deeper details. Novelty is strong—mechanistic interpretability workflows require specialized knowledge of activation caching, hook management, and circuit decomposition that would consume significant tokens for a CLI agent to discover independently. Minor improvement possible: the description could explicitly mention 'circuit decomposition' to fully match the workflow depth.