Browse skills

Scikit Bio analyzes biological sequences (DNA, RNA, protein) using scikit-learn for pattern recognition and prediction – useful for bioinformatics research.

Leverages Claude’s advanced reasoning to analyze and synthesize complex scientific data, boosting research efficiency and understanding.

Qdrant enables seamless integration with the Qdrant vector database for efficient semantic search and retrieval within your applications.

Vectordb allows you to efficiently store and search vector embeddings for semantic similarity searches, boosting your AI applications' understanding.

Designs, optimizes, and maintains vector databases for efficient semantic search and AI applications using rmyndharis expertise.

Generates vector embeddings from text using Google Gemini, enabling semantic search, similarity analysis, and powerful AI applications.

Scvi Tools provides streamlined access to SCVI embeddings and related analysis for single-cell data, accelerating research insights.

Deeply understands tool documentation to efficiently locate relevant information and guide usage effectively.

Fine-tunes embedding models to improve semantic search relevance and similarity matching accuracy across diverse datasets.

Designs, optimizes, and troubleshoots vector databases for efficient similarity search and embedding storage.

Seamlessly integrates ChromaDB vector databases into your workflows for enhanced search, retrieval, and knowledge management capabilities.

Gemini Deepresearch — provides AI agent assistance for software development workflows.

Geo Prompt Researcher — provides AI agent assistance for software development workflows.

This skill generates dense vector embeddings from text modules for semantic similarity searches and enhanced retrieval within RAG systems.

Provides search guidance and assistance for software development workflows.

This tool creates vector embeddings from text, enabling semantic search and understanding within AI applications.

This skill leverages Gemini to perform web searches and summarize findings directly within your workflow, boosting research efficiency.

Transcribes audio meetings and calls using OpenAI's Whisper model, providing searchable text for improved collaboration.

This skill extracts key information from Wikipedia articles using LLMs for quick summaries and insights – a valuable research tool.

This skill generates vector embeddings of text, enabling semantic similarity searches and powerful AI applications like clustering & recommendation.

Automates web research tasks, extracting data and insights from online sources based on provided queries.

This skill generates vector embeddings from text, enabling semantic similarity searches and powering AI applications needing contextual understanding.

This skill generates high-quality embeddings for text using Facebook's (FT) transformer model, boosting semantic understanding and similarity search.

Provides LLMs guidance and assistance for building AI and machine learning applications.