# Esm

Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and...

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Engineering
- License: MIT license
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill esm -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill esm -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill esm -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill esm -g -a gemini-cli -y`

## Description

Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ESM: protein generation, embeddings, and folding

## When to use

Use for Biohub's `esm` SDK: ESM3 masked multimodal generation, ESMC sequence
representations, and ESMFold2 all-atom prediction. The former Forge platform
migrated to `https://biohub.ai`, including ESM3; SDK class names still contain
`Forge`. This skill targets the released **esm 3.4.1.post1**, verified against
its wheel and current official documentation.

`fair-esm` is Meta's separate, older ESM2/ESMFold/ESM-IF distribution. Both
packages import as `esm`; install them in separate environments. The legacy
`esm.pretrained.esm2_*` interface is not the Biohub ESMC interface. Hugging Face
Transformers' native ESMC implementation is another API: do not interchange its
version requirements or output types with this SDK.

## Setup and model choice

```bash
uv venv --python 3.12 .venv-esm
uv pip install --python .venv-esm/bin/python "esm==3.4.1.post1"
```

The release declares Python >=3.12, Torch >=2.11,<2.12 and Transformers
>=4.57.6,<5. Python 3.12, Torch 2.11.0 and Transformers 4.57.6 were tested on
CPU. Linux x86_64 installs include GPU-specific dependencies; use a supported
inference machine and budget disk space. Flash Attention is optional; it is
unnecessary for the tiny CPU tests. Do not install this stack into a shared
environment containing incompatible Transformers or Torch pins.

| Task | Local model/API | Hosted client/model |
| --- | --- | --- |
| ESM3 sequence/structure/function generation | `ESM3.from_pretrained("esm3-sm-open-v1")` | `client("esm3-medium-2024-08")`; small/large IDs in the ESM3 reference |
| ESMC embeddings | `EsmcForMaskedLM` and `EsmcTokenizer`; `biohub/ESMC-300M`, `biohub/ESMC-600M`, `biohub/ESMC-6B` | `esmc_client("esmc-600m-2024-12")`; 300M/6B also documented |
| ESMFold2 all-atom structures | `EsmFold2Model`, `ESMFold2InputBuilder`; `biohub/ESMFold2` | `esmfold2_client("esmfold2-fast-2026-05")` |

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/esm/SKILL.md