Agent skill · research science · k-dense-ai
esm
Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
Why this skill is useful
Adds specific Python SDK commands and workflows for protein modeling that are not widely known or documented.
What it needs
Requires python installed locally. Requires huggingface account access. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
ESM: Evolutionary Scale Modeling Overview ESM provides protein language models for understanding, generating, and designing proteins. Use this skill for current EvolutionaryScale/Biohub workflows: ESM3 for generative design, ESMC for representation learning and embeddings, hosted Forge/Biohub inference, and ESMFold2 all-atom structure prediction. 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 k-dense-ai/esm