Agent skill · research science · aiskillstore
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
Why this skill is useful
Adds executable scripts for protein sequence generation, structure prediction, and embedding extraction that extend the AI's capabilities in protein engineering.
What it needs
Requires torch installed locally. Requires forge.evolutionaryscale.ai account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: For remote/cloud usage via Forge API: See references/esm3-api.md for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples. 2. Structure Prediction and Inverse Folding Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure). Structure prediction: Inverse folding (sequence from structure): 3. Protein Embeddings with ESM C Generate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis. When to use: Extracting protein representations for machine learning Computing sequence similarities Feature extraction for protein classification Transfer learning for protein-related tasks Basic usage: Batch processing: See references/esm-c-api.md for ESM C model details, efficiency comparisons, and advanced embedding strategies. 4. Function Conditioning and Annotation Use ESM3's function track to generate proteins with specific functional annotations or predict function from sequence. Function-conditioned generation: 5. Chain-of-Thought Generation Iteratively refine protein designs using ESM3's chain-of-thought generation approach. 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/esm