Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
7.6
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0
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Machine Learning
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Excellent skill for pyvene interventions with comprehensive coverage of causal tracing, activation patching, and IIT workflows. The description accurately captures the scope, and the skill provides detailed, executable code examples for all major use cases. Structure is clear with four well-defined workflows, organized tables, and proper separation of quick reference from detailed documentation in the references folder. Task knowledge is outstanding with complete code snippets, troubleshooting sections, and clear comparisons to alternative tools. Novelty is strong—causal intervention frameworks require specialized knowledge of intervention types, component targeting, and coordinate systems that would consume significant tokens for a CLI agent to derive independently. Minor improvement areas: could benefit from a quick decision tree for choosing intervention types, and the workflow examples could include expected output snippets for validation. Overall, this is a highly useful skill that meaningfully reduces the complexity of performing reproducible causal interventions.
zechenzhangAGI
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