Agent skill · Backend & API

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.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill esm-aiskillstore-marketplace-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 11 KB
Bundled scripts: none
Path: skills/ai-ml/esm-aiskillstore-marketplace-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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:** ```python 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 # Generate completion protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8)) print(protein.sequence)

What's inside
Steps it walks through
  1. Overview
  2. Core Capabilities
  3. 1. Protein Sequence Generation with ESM3
  4. 2. Structure Prediction and Inverse Folding
  5. 3. Protein Embeddings with ESM C
  6. 4. Function Conditioning and Annotation
  7. 5. Chain-of-Thought Generation
  8. 6. Batch Processing with Forge API
  9. Model Selection Guide
  10. Installation
  11. Common Workflows
  12. References
  13. Best Practices
  14. Resources and Documentation
Ships with 1 file
  • metadata.json
Commands it runs
uv pip install esm
uv pip install flash-attn --no-build-isolation
uv pip install esm  # SDK includes Forge client
More from claude-skill-registry
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About this skill
What does the esm skill do?

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.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill esm-aiskillstore-marketplace-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going