Does this skill support the Forge API?

Yes, it includes built-in support for the EvolutionaryScale Forge API, enabling access to larger models like ESM3-Large and high-throughput batch processing.

What is inverse folding in the context of this skill?

Inverse folding allows you to provide a 3D protein structure (via PDB) and use ESM3 to generate a protein sequence that is predicted to fold into 그 structure.

Can I generate proteins with specific functions?

Yes, using ESM3's function track, you can condition generation on specific functional annotations to design proteins with desired properties.

What models are supported by the ESM skill?

The skill supports the ESM3 family for generative tasks (sequence, structure, and function) and the ESM C family for efficient protein embeddings and representations.

Can I use this skill for local protein modeling?

Yes, the skill supports local model usage with open-weight models like esm3-sm-open-v1 and esmc-300m for development and testing.

ESM Protein Design & Analysis

Name: ESM Protein Design & Analysis
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Provides a comprehensive toolkit for protein language modeling, including generative design, structure prediction, and high-quality sequence embeddings.

About

The ESM (Evolutionary Scale Modeling) skill empowers researchers and developers to leverage state-of-the-art protein language models within Claude. It supports the ESM3 family for multimodal generative design—covering sequence, structure, and function—alongside ESM C for efficient protein representation learning. This skill enables seamless transitions between local model execution and scalable cloud inference via the Forge API, facilitating complex workflows such as inverse folding, variant generation, and chain-of-thought protein refinement for drug discovery and synthetic biology.

Key Features

Chain-of-thought refinement for complex protein engineering tasks
Support for local model inference and scalable Forge API access
High-quality protein representation learning and embeddings via ESM C
Automated structure prediction and inverse folding workflows
1 GitHub stars
Multimodal generative protein design (sequence, structure, function)

Use Cases

Generating protein embeddings for downstream machine learning and classification
Predicting 3D structures from sequences or performing sequence design for target structures
Designing novel proteins with specific functional properties or structural constraints

About

Key Features

Chain-of-thought refinement for complex protein engineering tasks
Support for local model inference and scalable Forge API access
High-quality protein representation learning and embeddings via ESM C
Automated structure prediction and inverse folding workflows
1 GitHub stars
Multimodal generative protein design (sequence, structure, function)

Use Cases

Generating protein embeddings for downstream machine learning and classification
Predicting 3D structures from sequences or performing sequence design for target structures
Designing novel proteins with specific functional properties or structural constraints