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lib-esm

Generate protein sequences and embeddings using ESM3 and ESM C.

Updated Mar 13, 2026
Search Tags:#bioinformatics#protein engineering#protein design#generative models#esm3#esm c
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-esm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lib-esm
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-esm
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-esm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for advanced protein design, generation, and analysis using state-of-the-art AI models like ESM3 and ESM C.

Core Features & Use Cases

  • Protein Sequence Generation: Design novel protein sequences with desired properties using ESM3's generative capabilities.
  • Structure Prediction & Inverse Folding: Predict 3D protein structures from sequences or design sequences that fold into a target structure.
  • Protein Embeddings: Generate high-quality embeddings for downstream tasks like classification, similarity analysis, and clustering using ESM C.
  • Function Conditioning: Generate proteins with specific functional annotations or predict function from sequence.
  • Use Case: A researcher needs to design a new enzyme with enhanced catalytic activity. They can use this Skill to generate candidate sequences, predict their structures, and analyze their functional potential.

Quick Start

Use the lib-esm skill to generate a novel protein sequence of 100 residues with moderate diversity.

Frequently Asked Questions about lib-esm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate novel protein sequences with specific functional properties?▼

You can generate novel protein sequences with desired properties using ESM3's generative capabilities. This toolkit supports function conditioning, allowing you to specify functional annotations to guide the sequence generation process for targeted protein design.

Can I predict 3D protein structures and perform inverse folding?▼

Yes, this toolkit supports both 3D structure prediction from amino acid sequences and inverse folding. Inverse folding allows you to design novel sequences that are highly likely to fold into a target 3D structure.

How do I extract protein embeddings for downstream machine learning tasks?▼

You can generate high-quality protein embeddings using ESM C representation learning. These embeddings are designed for downstream tasks like sequence classification, similarity analysis, and clustering.

Does this protein analysis toolkit support scalable cloud-based inference?▼

Yes, the toolkit integrates with both local models and the cloud-based Forge API. This allows you to scale your protein language modeling and inference workloads according to your computational needs.

What is the best way to design a new enzyme with enhanced catalytic activity?▼

The best approach is to use this toolkit to generate candidate sequences with ESM3, predict their 3D structures, and analyze functional potential. This workflow supports iterative design for enhanced catalytic activity.

Do I need local models to use ESM3 and ESM C for protein language modeling?▼

No, you do not strictly need local models. The toolkit integrates with local models but also supports the cloud-based Forge API, providing flexible options for running ESM3 and ESM C inference.

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