ml-pipeline-workflow
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
108 skills found
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Statistical visualization library for Python. Create publication-quality graphics like box plots, heatmaps, and violin plots with pandas integration and automatic statistical estimation.
Meta-skill for generating publication-ready scientific figures, multi-panel layouts, and journal-compliant visualizations using Python's matplotlib, seaborn, and plotly libraries.
Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.
Automated single-cell RNA-seq quality control pipeline following scverse best practices. Performs MAD-based outlier detection, cell filtering, and diagnostic visualization for .h5ad and .h5 datasets.
A command-line tool for managing, building, and deploying Agent Skills as OCI artifacts within the Agent Skills ecosystem.
A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.
Connect your AI agent to the Hugging Face Hub via MCP. Search models, datasets, and papers, manage repos, run cloud compute jobs, and invoke Gradio Spaces as functional AI tools.
Normalizes testing defect logs by correcting typos, abbreviations, and ambiguous descriptions based on product-specific codebooks and station validation.
Transit Least Squares (TLS) algorithm for detecting exoplanet transits in light curve data, offering higher sensitivity than Lomb-Scargle for transit-shaped signals.
Systematic methodology for reproducing published academic papers using provided data, including sample selection, statistical verification, and automated reporting.