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Found 83 Skills
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
Datumbox integration. Manage Organizations, Users, Goals, Filters. Use when the user wants to interact with Datumbox data.
Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.
Comprehensive guide for Qiskit - IBM's quantum computing framework. Use for quantum circuit design, quantum algorithms (VQE, QAOA, Grover, Shor), quantum simulation, noise modeling, quantum machine learning, and quantum chemistry calculations. Essential for quantum computing research and applications.
This skill should be used when the user asks to "predictive intelligence", "machine learning", "ML", "classification", "similarity", "clustering", "prediction", "AI", or any ServiceNow Predictive Intelligence development.
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
ModelScope integration. Manage data, records, and automate workflows. Use when the user wants to interact with ModelScope data.
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
Composable transformations of Python+NumPy programs. Differentiate, vectorize, JIT-compile to GPU/TPU. Built for high-performance machine learning research and complex scientific simulations. Use for automatic differentiation, GPU/TPU acceleration, higher-order derivatives, physics-informed machine learning, differentiable simulations, and automatic vectorization.
Self-hosted ML coding practice platform with 68 problems covering Transformers, diffusion, RLHF, and more — instant browser feedback, no GPU required.