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RamanEbrahimi
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esm

Generate protein sequences and predict structures using ESM3 and ESM C models.

3|Updated Apr 17, 2026
Search Tags:#sequence generation#structure prediction#protein design#embedding generation#protein language models#function prediction
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill esm-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: esm
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/esm
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill esm-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires esm, esmc, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for protein language models, empowering users to perform a wide range of tasks from protein sequence generation to function prediction and embedding generation.

Core Features & Use Cases

  • Protein Sequence Generation: Generate novel protein sequences with specific properties using multimodal generative modeling.
  • Structure Prediction and Inverse Folding: Predict protein structures from sequences or design sequences for target structures.
  • Protein Embeddings: Generate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.
  • Function Conditioning and Annotation: Generate proteins with specific functional annotations or predict function from sequence.
  • Chain-of-Thought Generation: Iteratively refine protein designs using a chain-of-thought approach.
  • Batch Processing with Forge API: Process multiple proteins efficiently using the Forge API for scalability and access to the latest models.
  • Model Selection Guide: Choose the appropriate model based on the required balance between quality, speed, and memory usage.
  • Installation and Usage: Provides instructions for installing and using the Skill, including basic installation and API access.

Quick Start

Use the esm skill to generate a protein sequence with specific properties. For example: 'Generate a sequence for a fluorescent protein with the label "fluorescent_protein" and a sequence length of 200 residues.'

Frequently Asked Questions about esm

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

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

To generate a novel protein sequence with specific properties, you can employ protein language models for multimodal generative modeling. This Skill supports function conditioning, allowing you to specify desired labels and sequence lengths for targeted protein design.

Can I predict protein structure and perform inverse folding using protein language models?▼

Yes, protein language models can predict protein structures from sequences and perform inverse folding to design sequences for target structures. This enables comprehensive structural analysis and engineering for drug discovery applications.

How do I generate protein embeddings for downstream classification tasks?▼

You can generate high-quality protein embeddings using language models to facilitate downstream tasks like function prediction, classification, or similarity analysis. These embeddings capture complex biological features for scientific research.

What is the best way to process multiple protein sequences at scale?▼

The best way to process multiple protein sequences at scale is by utilizing the cloud-based Forge API. This approach supports batch processing for scalable inference and provides access to the latest protein language models.

Do I need ESM3 and ESM C models for protein function prediction?▼

Yes, you need ESM3 and ESM C models to perform protein function prediction and sequence generation. A model selection guide helps you choose the appropriate model based on your required balance between quality, speed, and memory usage.

How does chain-of-thought generation work for protein design?▼

Chain-of-thought generation for protein design works by iteratively refining protein sequences using multimodal language models. This approach allows you to progressively enhance the design to meet specific structural or functional criteria.

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