Generate and analyze protein sequences, structures, and functions with ESM models.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill esm-jasrajtulsi
Please help me install this Agent Skill. Skill: esm Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/esm Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill esm-jasrajtulsi
This Skill helps you work directly with ESM protein AI workflows without memorizing client variants, model IDs, or API details. It brings sequence generation, structure prediction, embeddings, batch inference, and hosted deployment guidance into one place so you can move from idea to analysis faster and with fewer mistakes.
Ask the ESM skill to design a protein sequence, predict its structure, or generate embeddings for a provided amino-acid sequence.
High-intent search queries and answers about installing and using this skill.
Protein sequence generation with ESM3 produces design, completion, and variant outputs conditioned on functions. You provide validated amino-acid inputs, and the workflow iteratively generates candidate sequences for downstream analysis.
ESMC embeddings extract numerical features from protein sequences for similarity search, clustering, and classification. You provide validated amino-acid sequences to generate dense vector representations suitable for downstream machine learning tasks.
ESM supports structure prediction and inverse folding for sequence-to-structure and structure-to-sequence workflows. You can predict protein structures from amino-acid sequences and generate sequences that fold into target structures.
Authenticated Python SDK access is required for ESM Forge and Biohub batch inference. You must configure authentication, select correct model IDs, and implement rate control to execute scalable batch jobs successfully.
Designing a fluorescent protein involves generating candidate sequences with ESM3, ranking variants by ESMC embeddings, and validating the best sequences with structure prediction. This workflow moves from generation to structural validation efficiently.
ESM workflows require correct model selection, authenticated SDK usage, and validated protein inputs. Limitations include managing rate control for batch inference and ensuring iterative generation inputs meet validation constraints.
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