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.
7.6
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0
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AI & LLM
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Excellent mechanistic interpretability skill with comprehensive workflows for activation patching, circuit analysis, and induction head detection. The SKILL.md provides clear, executable code examples and troubleshooting guidance that would enable a CLI agent to perform complex interpretability research. Structure is well-organized with three detailed workflows, common pitfalls section, and proper delegation to reference files. The skill addresses a genuinely complex domain where a naive CLI agent would struggle significantly with the nuances of hook management, tokenization gotchas, and causal tracing experiments. Minor deductions: description could be slightly more explicit about when to invoke each workflow, and novelty is somewhat limited by the fact that this wraps an existing well-documented library (though the curated workflows and pitfall warnings add substantial value).
zechenzhangAGI
Skill Author
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