Esm

This gives you EvolutionaryScale's protein language models for generative design and embeddings. ESM3 handles the generative side: you can mask parts of a sequence and have it fill in the gaps, do inverse folding to design sequences for target structures, or condition on function annotations. ESM C is for embeddings when you need representations for downstream ML tasks. The small ESM3 model runs locally, but the better ones (7B and 98B parameters) require their Forge API. If you're doing any serious protein engineering or need to extract features from sequences, this is the current state of the art. The chain-of-thought generation for iterative refinement across sequence, structure, and function is genuinely useful for design work.

skills.sh installs: 214
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill esm --agent claude-code

Installs into .claude/skills of the current project.

Files

ESM: Evolutionary Scale Modeling

Overview

ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.

Core Capabilities

1. Protein Sequence Generation with ESM3

Generate novel protein sequences with desired properties using multimodal generative modeling.

When to use:

Designing proteins with specific functional properties
Completing partial protein sequences
Generating variants of existing proteins
Creating proteins with desired structural characteristics

Basic usage:

from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Load model locally
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-sm-open-v1").to("cuda")

# Create protein prompt
protein = ESMProtein(sequence="MPRT___KEND")  # '_' represents masked positions

SKILL.md on GitHub