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Explaining Machine Learning Models

explaining-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Prediction Monitor Assistant

prediction-monitor

jeremylongshore/claude-code-plugins-plus-skills

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.

Replicate Model Automation

replicate-automation

ComposioHQ/awesome-claude-skills

Automate Replicate AI workflows via the Composio MCP integration to inspect model schemas, run predictions (sync or async), upload input files, list versions, and manage prediction history and related assets.

Molecular ML Toolkit

K-Dense-AI/claude-scientific-skills

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

SHAP Model Explainability

K-Dense-AI/claude-scientific-skills

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Yann LeCun's AI Critique and Debates

yann-lecun-debate

sickn33/antigravity-awesome-skills

A specialized module containing Yann LeCun's comprehensive arguments on the limitations of Large Language Models (LLMs). It critically examines the concept of 'emergent reasoning,' contrasts LLMs' statistical prediction with genuine world models, and addresses deep technical rivalries (e.g., LeCun vs. Hinton). Ideal for advanced discussions on AI causality, common sense, and AGI feasibility.

Hard Predict Future

hard-predict-future

davepoon/buildwithclaude

A foresight agent that runs a rigorous 12-step pipeline combining Claude’s qualitative intelligence with Python’s deterministic arithmetic to analyze high-stakes future scenarios, infer reasonable horizons, and surface quantitative signals for prediction requests.

DeepChem Molecular Machine Learning

K-Dense-AI/scientific-agent-skills

A Python library for molecular machine learning, offering diverse featurizers, MoleculeNet benchmarks, graph neural networks, and pretrained models for property and ADMET prediction.

DiffDock Molecular Docking

K-Dense-AI/scientific-agent-skills

Diffusion-based deep learning tool for predicting 3D binding poses of small-molecule ligands to protein targets from PDB files or sequences plus SMILES/SDF/MOL2. Supports single docking, batch virtual screening, and confidence-score analysis; not for binding affinity prediction.

ESM Protein Modeling SDK

K-Dense-AI/scientific-agent-skills

Use the esm Python SDK with ESM3 and ESMC for protein sequence generation, embeddings, function conditioning, structure prediction, inverse folding, and Forge/Biohub inference workflows.

Clinical Deep Learning with PyHealth

K-Dense-AI/scientific-agent-skills

Build clinical and healthcare deep learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train, and evaluate using a modular five-stage workflow.

TorchDrug Graph Learning Workflow

K-Dense-AI/scientific-agent-skills

TorchDrug is a modular PyTorch library for graph learning tasks including molecular property prediction, molecule generation, and knowledge graph reasoning. It provides datasets, models, and training engines for building workflows in computational chemistry and biology. Ensure compatibility with PyTorch 1.8-2.0 and Python 3.7-3.10 before implementation.

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