executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
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生産性・共同作業 · SKILL.md
mas_unlabeled_importance
mas_unlabeled_importanceEstimate Memory Aware Synapses parameter importance from unlabeled inputs using output-sensitivity gradients.
Independent GitHub snapshot · Automated classification · Original documentation governs
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GitHubname: mas_unlabeled_importance description: Estimate Memory Aware Synapses parameter importance from unlabeled inputs using output-sensitivity gradients.
MAS Unlabeled Importance
Use when MAS recovery needs label-free importance. Inputs are weights and unlabeled samples; outputs are nonnegative importance values and sample-count metadata. Compute squared-output-norm gradients, average absolute values, and reject empty streams. Validate with python tests/test_importance.py.