Search Results for "machine learning"
ml-integration-patterns
Machine learning integration patterns for rRNA-Phylo covering three use cases - rRNA sequence classification (supervised learning with sklearn/PyTorch), multi-tree consensus (ensemble methods), and generative tree synthesis (GNNs/transformers). Includes feature engineering, model training, hyperparameter tuning, model serving, versioning, and evaluation metrics for bioinformatics ML workflows.
moai-domain-ml
Machine learning model training, evaluation, deployment, and MLOps workflows.
ff-ml-modeling
Expert guidance on machine learning and feature engineering for fantasy football player projection models. Use this skill when building predictive models, engineering features from player statistics, selecting appropriate ML algorithms, or addressing sports-specific ML challenges. Covers feature engineering patterns, model selection frameworks, validation strategies, and interpretability techniques for fantasy football analytics.
aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
gtars
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
moai-domain-ml
Machine learning model training, evaluation, deployment, and MLOps workflows.
ml-systems
Machine Learning Systems - comprehensive knowledge for building production ML systems from data engineering through deployment and operations. Based on Harvard ML Systems course and Designing ML Systems by Chip Huyen.
data-engineering
ML data engineering covering data pipelines, data quality, collection strategies, storage, and versioning for machine learning systems.
ai-ml-engineer
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOpsTrigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learningUse when: User requests involve ai ml engineer tasks.
dataset-curator
Curate and clean training datasets for high-quality machine learning
data-science
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
foundation-models
Apple Foundation Models framework for on-device AI, @Generable macro, guided generation, tool calling, and streaming. Use when user asks about on-device AI, Apple Intelligence, Foundation Models, @Generable, LLM, or local machine learning.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
ai-training-data-generation
Generate high-quality training datasets from documents, text corpora, and structured content. Use when creating AI training data from dictionaries, documents, or when generating examples for machine learning models. Optimized for low-resource languages and domain-specific knowledge extraction.
jupyter
Specialized assistant for working with Jupyter notebooks (.ipynb files). Use for analyzing, editing, debugging, or executing code in notebooks. Helps with data analysis, machine learning, deep learning, data visualization, and scientific computing workflows. Can read notebook contents, modify cells, execute Python code in the kernel, add documentation, and troubleshoot errors.
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