Results for “model-interpretability”

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

Build reproducible ML experiment plans with hypotheses, metrics, and ablations. Use when: (1) planning experiments, (2) comparing variants, (3) defining acceptance thresholds. NOT for: long-running experiment execution.

"Feature importance" is ambiguous.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.

Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

Big Data Based Modeling Skill

当需要分析一个观念、文化现象或行为模式为何能广泛传播并被人接受时

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

Calculate TAM, SAM, and SOM with explicit assumptions, methods, and caveats for market sizing.

Applied Optics Modeling Skill

贾宝玉（古典虚构）认知与表达框架（压缩蒸馏）：情不情、反仕途经济、女儿崇拜叙事… 触发：红楼梦 等。虚构；非性别刻板侮辱

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.

Imported skill models from agentskills

用于编写和编辑高质量 Skill 的参考框架：通过调用设计、信息层级、引导词与剪枝提高执行过程的可预测性。

Use when reviewer comments arrive and each one needs a tracked decision, when a revise and resubmit letter must map every comment to a manuscript change, or when contradictory reviewer demands need evidence-based, editor-facing framing.

Ownership models are usually inherited rather than chosen, and the inherited one