Esm

What it does

When your agent uses it

• Tasks that involve Embeddings

Example prompts

• “Use the esm skill to use the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding”
• “/esm”

Requirements

• Python 3
• A credential in ESM_API_KEY
• Compatibility (from SKILL.md): Requires Python 3.12+ and esm 3.4.1.post1. Local pretrained inference needs model weights and sufficient RAM or GPU memory; hosted inference needs network access and ESM_API_KEY. Use an isolated environment, separate from fair-esm.

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Specify the sequence/chain/residue mapping and whether the objective is
2. 2Choose a local checkpoint or explicit hosted model ID. Record SDK, checkpoint
3. 3Check every SDK result for ESMProteinError. Hosted failures may be returned
4. 4Validate the output contract: sequence length and fixed residues, residue-only
5. 5Save sequences/structures with settings and identifiers. Avoid caches keyed

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv
  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • biohub.ai

  Also links to:

  • arxiv.org
  • biohub.org
  • doi.org
  • export.arxiv.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • ESM_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.12+ and esm 3.4.1.post1. Local pretrained inference needs model weights and sufficient RAM or GPU memory; hosted inference needs network access and ESM_API_KEY. Use an isolated environment, separate from fair-esm.

  From compatibility in the SKILL.md frontmatter.

Context cost

Esm loads about 2.7k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 976 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~66

When it runs · the whole SKILL.md, loaded when a task matches

~2.7k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 976 words, ~2,685 tokens.

name

esm

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.

compatibility

Requires Python 3.12+ and esm 3.4.1.post1. Local pretrained inference needs model weights and sufficient RAM or GPU memory; hosted inference needs network access and ESM_API_KEY. Use an isolated environment, separate from fair-esm.

license

MIT license

metadata.version

2.0

metadata.skill-author

K-Dense Inc.

metadata.last-reviewed

2026-09-30

metadata.upstream-version

3.4.1.post1

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

import torch
from esm.models.esm3 import ESM3
from esm.sdk.api import ESMProtein, ESMProteinError, GenerationConfig

model = ESM3.from_pretrained("esm3-sm-open-v1", device=torch.device("cpu"))
prompt = "MPRT___KEND"
completed = model.generate(
    ESMProtein(sequence=prompt),
    GenerationConfig(track="sequence", num_steps=3, temperature=0.7),
)
if isinstance(completed, ESMProteinError):
    raise completed
assert completed.sequence is not None and len(completed.sequence) == len(prompt)
assert "_" not in completed.sequence
assert all(a == "_" or a == b for a, b in zip(prompt, completed.sequence))

# A fresh sequence-only prompt prevents old coordinates conditioning the check.
folded = model.generate(
    ESMProtein(sequence=completed.sequence),
    GenerationConfig(track="structure", num_steps=8),
)
if isinstance(folded, ESMProteinError):
    raise folded
assert folded.coordinates is not None
folded.to_pdb("candidate.pdb")

from esm.models.esmc import EsmcForMaskedLM, EsmcTokenizer
from esm_embeddings import embed_sequences

model = EsmcForMaskedLM.from_pretrained("biohub/ESMC-300M", device="cpu").eval()
tokenizer = EsmcTokenizer()
sequences = ["MPRTKEINDAGLIVHSPQWFYK", "ACDEFGHIK"]
features = embed_sequences(model, tokenizer, sequences)
assert features.shape == (2, 960)

import os
from esm.sdk import esmfold2_client
from esm.sdk.api import ESMProteinError, FoldingConfig
from esm.utils.structure.input_builder import ProteinInput, StructurePredictionInput

fold_input = StructurePredictionInput(
    sequences=[ProteinInput(id="A", sequence="MPRTKEINDAGLIVHSPQWFYK")]
)
with esmfold2_client(
    model="esmfold2-fast-2026-05", url="https://biohub.ai",
    token=os.environ["ESM_API_KEY"], request_timeout=300,
) as client:
    result = client.fold_all_atom(fold_input, config=FoldingConfig())
if isinstance(result, ESMProteinError):
    raise result
with open("candidate.cif", "w") as handle:
    handle.write(result.complex.to_mmcif())

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/esm of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/biohub-platform.md
• references/esm-c-api.md
• references/esm3-api.md
• references/forge-api.md
• references/review.md
• references/workflows.md
• scripts/esm_embeddings.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/esm