esm
Description
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; desig
How to Use
- Visit the GitHub repository to get the SKILL.md file
- Copy the file to your project root or .cursor/rules directory
- Restart your AI assistant or editor to apply the new skill
Tags
About esm
esm is an AI skill in the rag-search category, designed to help developers and users work more effectively with AI tools. Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; desig
This skill has earned 2,800 stars on GitHub, reflecting strong community adoption and trust. It is compatible with claude.
Key Capabilities
Why Use esm
Adding esm to your AI workflow can significantly enhance your productivity in rag-search tasks. With pre-defined prompt templates and best practices, this skill helps AI assistants better understand your requirements and deliver more accurate responses.
Whether you use claude, you can easily integrate this skill into your existing development environment.
Explore More rag-search Skills
Discover more AI skills in the rag-search category to build a comprehensive AI skill stack.
Related Skills
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications.
hybrid-search-implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems or building search engines.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search or nearest neighbor queries.
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