esm
About
The esm skill provides ESM2 protein language model capabilities for sequence analysis and embeddings. Use it for computing pseudo-log-likelihood scores, generating protein embeddings for clustering, and performing zero-shot variant effect prediction. It's designed for sequence plausibility filtering and analyzing sequence-function relationships, while structure prediction requires other skills.
Quick Install
Claude Code
Recommendednpx skills add NeverSight/skills_feed -a claude-code/plugin add https://github.com/NeverSight/skills_feedgit clone https://github.com/NeverSight/skills_feed.git ~/.claude/skills/esmCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
boltz
OtherBoltz provides open-source biomolecular structure prediction using Boltz-1/Boltz-2 models, serving as an alternative to AlphaFold2. It specializes in predicting protein complexes, validating designed binders, and handling protein-ligand interactions. This skill is particularly useful when you need open-source structure prediction or want to leverage local GPU resources.
alphafold
OtherThe alphafold skill uses AlphaFold2 to validate protein designs by predicting structures and calculating confidence metrics. It supports single-chain validation, binder-target complexes, and multi-chain predictions with AlphaFold-Multimer. For faster single-chain predictions, developers should use the esm skill instead.
boltzgen
OtherBoltzGen is an all-atom diffusion model for protein design that generates both backbone and side-chain coordinates simultaneously. It is particularly suited for designing proteins around small molecules or ligands where precise binding geometries are required. Use this skill when you need side-chain-aware design from the start and are working with a YAML-based configuration.
bindcraft
OtherBindCraft provides end-to-end protein binder design with joint backbone and sequence optimization and built-in AlphaFold2 validation. It's ideal for production-quality binder campaigns offering different speed protocols to balance design quality and computational cost. Use this skill when you need high experimental success rates for binder design rather than just backbone generation.
