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Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
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</> SKILL.md
esm.SKILL.md
---name: esm
description: Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
license: MIT license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# 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:**
```python
from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
# Load local open weights after accepting the license on Hugging Face.
- `esmc-6b-2024-12` (80 layers) - Maximum quality (Forge API; local 6B weights require Forge or SageMaker)
Local `ESMC.from_pretrained()` examples use underscore aliases (`esmc_300m`, `esmc_600m`). Hosted API clients use dated model IDs such as `esmc-600m-2024-12`.
**Selection criteria:**
- **Local development/testing:** Use `esm3-open` or `esmc_300m`
- **Production quality:** Use `esm3-medium-2024-08` via Forge
- **Maximum accuracy:** Use `esm3-large-2024-03` or `esmc-6b-2024-12` via Forge
- **High throughput:** Use Forge or Biohub APIs with explicit async concurrency limits
- **Cost optimization:** Use smaller models, implement caching strategies
## Installation
Install from PyPI ([`esm` on PyPI](https://pypi.org/project/esm/) by EvolutionaryScale). Current PyPI release: **3.2.3** (Oct 14, 2025). Requires **Python >=3.12,<3.13**.
**Basic installation:**
```bash
uv pip install "esm==3.2.3"
```
**With Flash Attention (recommended for faster inference on NVIDIA GPUs):**
```bash
uv pip install "esm==3.2.3"
uv pip install flash-attn --no-build-isolation
```
The Forge client ships with the `esm` package - no extra install for ESM3 or ESMC Forge inference.
## Authentication
Forge API access requires an API key. Never hardcode tokens in scripts or commit them to version control.
1. Check whether `ESM_API_KEY` is already set in the environment.
2. If not, check a local `.env` for `ESM_API_KEY` only (do not load unrelated secrets).
3. If still missing, create a key in the [Biohub developer console](https://biohub.ai/developer-console/api-keys) for Biohub APIs or [Forge](https://forge.evolutionaryscale.ai) for legacy Forge-hosted ESM3/ESMC access.
```python
import os
token = os.environ["ESM_API_KEY"] # raises KeyError if unset
```
`esm.sdk.client()` reads `ESM_API_KEY` automatically when `token` is omitted. Keep endpoint URLs fixed to trusted hosts such as `https://forge.evolutionaryscale.ai` or `https://biohub.ai`; do not take API hosts from untrusted user input.
**Biohub platform:** EvolutionaryScale and Forge now surface current hosted models through [biohub.ai](https://biohub.ai). SDK class names may still reference "Forge". See `references/biohub-platform.md` for ESMFold2 and Biohub-specific setup.
## Common Workflows
For detailed examples and complete workflows, see `references/workflows.md` which includes:
- Novel GFP design with chain-of-thought
- Protein variant generation and screening
- Structure-based sequence optimization
- Function prediction pipelines
- Embedding-based clustering and analysis
## References
This skill includes comprehensive reference documentation:
- `references/esm3-api.md` - ESM3 model architecture, API reference, generation parameters, and multimodal prompting
- `references/esm-c-api.md` - ESM C model details, embedding strategies, and performance optimization
- ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian
- **Community:** Slack community at https://bit.ly/3FKwcWd
- **Model Weights:** Hugging Face EvolutionaryScale and Biohub organizations
## Responsible Use
ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) and Biohub Acceptable Use Policy (https://biohub.org/acceptable-use-policy/) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.
