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Healthcare AI Toolkit

K-Dense-AI/scientific-agent-skills

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

PyOpenMS Mass Spectrometry

K-Dense-AI/scientific-agent-skills

PyOpenMS provides Python bindings for OpenMS to drive complete mass spectrometry workflows, covering data IO, spectral processing, feature/peptide-protein identification, quantitation, and metabolomics pipelines.

Pysam Genomic Toolkit

K-Dense-AI/scientific-agent-skills

Pysam offers Python bindings to htslib for reading and writing SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, enabling coverage stats, pileup analysis, variant queries, and seamless integration into NGS pipelines.

Scikit Learn Toolkit

K-Dense-AI/scientific-agent-skills

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

scVelo RNA Velocity Workflow

K-Dense-AI/scientific-agent-skills

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

Transformer Models for AI Tasks

K-Dense-AI/scientific-agent-skills

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

Vaex Big Data

K-Dense-AI/scientific-agent-skills

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

Zarr Cloud Array Storage

K-Dense-AI/scientific-agent-skills

Zarr is a Python toolkit for chunked N-D array storage with compression, sharding, and native integrations with NumPy/Dask/Xarray, ideal for parallel I/O and scalable cloud pipelines.

Multi-Domain Scientific Database Query

database-lookup

K-Dense-AI/scientific-agent-skills

Access and query 78 public databases covering diverse fields including biomedical, physical sciences, earth science, and economics. This skill identifies the relevant database(s) for user queries (e.g., genes, compounds, patents, climate data, financial indicators), executes structured REST API calls, and returns raw JSON results, providing a comprehensive data retrieval pipeline.

Algolia CI/CD Integration for Search

algolia-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up robust CI/CD pipelines for Algolia indexing. This includes running unit and integration tests against live indices, validating index settings against defined configurations, and implementing automated reindexing upon deployment (e.g., triggering on version tags). It ensures the stability and data consistency of your search service.

Figma to Code Generation with Anima

anima-hello-world

jeremylongshore/claude-code-plugins-plus-skills

Leverages the Anima SDK to convert visual designs from Figma into production-ready code across multiple modern frameworks (React, Vue, HTML). It supports clean TypeScript integration and Tailwind CSS styling, making it ideal for developers building automated design-to-code pipelines or rapidly prototyping complex UI components directly from design mockups.

Design-to-Code System Architecture

anima-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

This guide provides a reference architecture for automating the design-to-code workflow. It outlines the complete pipeline—from Figma design input to structured React/Vue output—detailing components like Figma API integration, code generation, and crucial post-processing steps (token mapping, normalization). Ideal for establishing scalable design systems and managing large-scale design handoffs in a professional environment.

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