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Sentence Embedding Framework

sentence-transformers

Orchestra-Research/AI-Research-SKILLs

Sentence Transformers provides 5000+ pre-trained models that generate sentence, text, and image embeddings for RAG, semantic search, clustering, and multilingual production workloads without relying on external APIs.

Shopify App Development with Remix and React

sickn33/antigravity-awesome-skills

A comprehensive guide providing expert patterns for building modern, robust Shopify applications. This skill covers the entire development lifecycle, including React Router app setup, handling complex webhooks, embedding the app within the Shopify Admin using App Bridge, and interacting with the GraphQL Admin API. Ideal for developers building e-commerce integrations.

Vector Database and Semantic Search Expert

vector-database-engineer

sickn33/antigravity-awesome-skills

Expert capability for designing and implementing complex semantic search systems using vector databases. Proficient in embedding strategies, index optimization (HNSW, IVF), and advanced retrieval methods like RAG and hybrid search. Masters major platforms including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for building sophisticated recommendation and similarity search applications.

Vector CLI for Semantic File Search

sickn33/antigravity-awesome-skills

Vexor is a powerful command-line interface designed for advanced semantic search across local files. By utilizing vector embeddings and integrating with large language models (like Claude/Codex), it allows users to search files based on meaning and context, rather than just exact keywords. This is ideal for large codebases or document repositories where traditional keyword search methods are insufficient.

Cellxgene Census Explorer

cellxgene-census

K-Dense-AI/claude-scientific-skills

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

ESM Protein Modeling Toolkit

K-Dense-AI/claude-scientific-skills

Combines ESM3 and ESM C protein language models to generate sequences, predict structures, condition functions, and extract embeddings via local or Forge API inference for protein design and engineering workflows.

Genomic Interval Embeddings

K-Dense-AI/claude-scientific-skills

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

Molecular Featurization Hub

K-Dense-AI/claude-scientific-skills

Molfeat is a Python toolkit that converts SMILES/RDKit molecules into embeddings via 100+ calculators, transformers, and pretrained models for QSAR, screening, similarity search, and deep learning pipelines.

Focused Treatment Plan Writer

treatment-plans

K-Dense-AI/claude-scientific-skills

Generates concise 3-4 page LaTeX/PDF treatment plans across medical specialties, embedding SMART goals, evidence-based interventions, HIPAA-compliant formatting, and mandated AI-generated schematics for quick clinical actionability.

Fast UMAP Dimensionality Reduction

K-Dense-AI/claude-scientific-skills

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Integrate SwiftUI Views in Expo

expo-ui-swift-ui

sickn33/antigravity-awesome-skills

This skill guides developers on using the `@expo/ui/swift-ui` package to integrate native SwiftUI views and modifiers into Expo applications. It is essential for building authentic, platform-specific iOS user interfaces within a cross-platform React Native context, especially when targeting Expo SDK 55. Learn how to wrap SwiftUI trees and embed React Native components.

Umap Visualization Tool

sickn33/antigravity-awesome-skills

Umap provides uniform manifold approximation for dimension reduction and visualization, helping data scientists explore high-dimensional embeddings through interpretable 2D/3D layouts.

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