esm
Comprehensive toolkit for EvolutionaryScale protein language models including ESM3 (generative multimodal design across sequence, structure, and function) and ESM C (efficient embeddings). Use for protein sequence/structure/function tasks, inverse folding, embeddings, variant design, and ESMFold2 structure prediction via Biohub. Supports local open weights (Python 3.12, esm on PyPI) and cloud Forge/Biohub APIs with ESM_API_KEY authentication.
npx skills add majiayu000/claude-skill-registry --skill esm-k-dense-ai-scientific-agent-ski-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)
- Overview
- Core Capabilities
- 1. Protein Sequence Generation with ESM3
- 2. Structure Prediction and Inverse Folding
- 3. Protein Embeddings with ESM C
- 4. Function Conditioning and Annotation
- 5. Chain-of-Thought Generation
- 6. Batch Processing with Forge API
- Model Selection Guide
- Installation
- Authentication
- Common Workflows
- References
- Best Practices
uv pip install "esm==3.2.3" uv pip install flash-attn --no-build-isolation
What does the esm skill do?
Comprehensive toolkit for EvolutionaryScale protein language models including ESM3 (generative multimodal design across sequence, structure, and function) and ESM C (efficient embeddings). Use for protein sequence/structure/function tasks, inverse folding, embeddings, variant design, and ESMFold2 structure prediction via Biohub. Supports local open weights (Python 3.12, esm on PyPI) and cloud Forge/Biohub APIs with ESM_API_KEY authentication.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill esm-k-dense-ai-scientific-agent-ski-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.