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Agent skill

Fair Esm2

by JimLiu in JimLiu/science-skills

Embed proteins with Meta AI's ESM-2 (fair-esm package). An agent skill from JimLiu/science-skills.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Fair Esm2

skills CLI
$ npx skills add JimLiu/science-skills --skill fair-esm2 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JimLiu/science-skills fair-esm2 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fair-esm2 .claude/skills/fair-esm2 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fair-esm2
GitHub stars
227
Used in
4 other repos
Token cost
~1.2k tokens
SKILL.md length
268 words
Files
1
Skills in repo
27
Repo updated
First seen
Licence
Apache-2.0

At a glance

Embed proteins with Meta AI's ESM-2 (fair-esm package). An agent skill from JimLiu/science-skills.

  • Extracting per-residue
  • SKILL.md covers Prerequisites, How to run, Models and Output format, plus 2 more sections
  • Calls pip
  • Per-sequence embeddings for downstream ML

What it does

Fair Esm2 is an agent skill from JimLiu/science-skills. Embed proteins with Meta AI's ESM-2 (fair-esm package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Embeddings. The licence is Apache-2.0.

When your agent uses it

  • Extracting per-residue
  • Per-sequence embeddings for downstream ML
  • Masked-LM likelihood / mutation effect scoring
  • Contact prediction from a sequence

Example prompts

  • “/fair-esm2”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit fb309c3. 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

    Shell commands in SKILL.md call:

    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Fair Esm2 loads about 1.2k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 268 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~63
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from JimLiu/science-skills at commit fb309c3, republished under its Apache-2.0 licence (© JimLiu). 268 words, ~1,224 tokens.

Download SKILL.mdSave it as .claude/skills/fair-esm2/SKILL.md (or your agent's skills folder).
name
fair-esm2
description
Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.
license
Apache-2.0
category
biomodels
requirements
gpu
metadata.display-name
ESM-2

fair-esm2 — ESM-2 (Meta AI)

ESM-2 code and weights are MIT (Meta AI, github.com/facebookresearch/esm).

Package disambiguation. pip install fair-esm gives you import esm with esm.pretrained.* (ESM-1/2). Biohub's github.com/Biohub/esm fork (MIT) gives you from esm.models.esmfold2 import ESMFold2InputBuilder — see the esmfold2 skill. Both share the esm namespace but are different libraries. This skill covers fair-esm (the Meta package).

Prerequisites

RequirementMinimumRecommended
Python3.8+3.11
CUDA11.7+12.x
GPU VRAM8 GB (8M), 16 GB (650M)24 GB+ (650M / 3B)

How to run

Embeddings
python
import torch, esm

model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
model = model.eval().cuda()
bc = alphabet.get_batch_converter()

_, _, toks = bc([("ubq", "MQIFVKTLTGKTITLEVEPSDTIENVK")])
with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33])
emb = out["representations"][33]      # (1, L+2, 1280) — includes BOS/EOS
seq_emb = emb[0, 1:-1].mean(0)        # per-sequence mean
Masked-LM scoring
python
with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33])
logits = out["logits"][0, 1:-1]       # (L, |vocab|)
# WT marginal log-likelihood; for mutation scoring, mask the position and
# compare logit[mut] − logit[wt].
Contact prediction
python
with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33], return_contacts=True)
contacts = out["contacts"][0]         # (L, L)

Models

NameLayersDimParamsUse
esm2_t6_8M_UR50D63208 MFast smoke / tiny embeddings
esm2_t33_650M_UR50D331280650 MDefault embedding model
esm2_t36_3B_UR50D3625603 BBest embeddings, 24 GB+

Output format

out["representations"][layer] is (B, L+2, D); slice [ :, 1:-1, : ] to drop BOS/EOS. out["contacts"] (when return_contacts=True) is (B, L, L).

Remote compute

Needs ≥16 GB VRAM (650M model) and either pre-cached .pt checkpoints or egress to dl.fbaipublicfiles.com. Read compute_details({provider, mode:'read'}) for an environment with fair-esm and a torch-hub weight cache, then:

python
c = host.compute.create(provider)
job = c.submit_job(
    intent="ESM-2 650M embeddings for 200 sequences — 1×GPU, ~2 min",
    inputs=[
        {"src": "seqs.fasta", "dst_filename": "seqs.fasta"},
        {"src": "embed_esm2.py", "dst_filename": "embed_esm2.py"},
    ],
    command="python3 embed_esm2.py",
    environment=...,   # env name from compute_details
    outputs=["embeddings.pt"],
    timeout_seconds=1800,
)
print(job.job_id)   # cell ends here — kernel never blocks on compute

Then call the wait_for_notification brain-tool. When the compute_done notification arrives, act on its payload:

python
save_artifacts(payload["featured_files"])   # paths under hpc/<job_id>/

For the full result dict (output_files, remote_workdir, …), re-enter the kernel: c.attach_job(job_id).result() then c.close(). See the remote-compute-ssh / remote-compute-modal skill for the orchestration details.

Inside embed_esm2.py, set TORCH_HOME to the provider's torch-hub cache mount (path is in compute_details) so esm.pretrained.* resolves locally.

Troubleshooting

SymptomCauseFix
ModuleNotFoundError: No module named 'esm.models'You want Biohub's esm fork, not fair-esmSee esmfold2 skill; this skill uses esm.pretrained.*
Slow first callDownloading weights via torch.hubSet TORCH_HOME to a cached location

Next: feed embeddings to a classifier. For structure prediction, use esmfold2.

© JimLiu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/fair-esm2 of JimLiu/science-skills.

Open the folder on GitHubat commit fb309c3

Used in 4 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in JimLiu/science-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Fair Esm2 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fair Esm2 compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fair Esm2 this skillJimLiu/science-skills2274 repos~1.2kAutomated safety check: PassApache-2.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs13k7 repos~1.7kAutomated safety check: PassMIT
Sentence-Transformers Training Routerhuggingface/skills11k1 repos~2.6kAutomated safety check: PassApache-2.0

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Categories

Questions about Fair Esm2

What does Fair Esm2 do?

Embed proteins with Meta AI's ESM-2 (fair-esm package). An agent skill from JimLiu/science-skills. Fair Esm2 is an agent skill from JimLiu/science-skills. Embed proteins with Meta AI's ESM-2 (fair-esm package).

When should I use Fair Esm2?

Fair Esm2 fits situations like: extracting per-residue; per-sequence embeddings for downstream ML; masked-LM likelihood / mutation effect scoring; contact prediction from a sequence.

How do I install Fair Esm2 in Claude Code?

Run `npx skills add JimLiu/science-skills --skill fair-esm2 -a claude-code`. Or copy the skill folder (skills/fair-esm2 in JimLiu/science-skills) into .claude/skills/fair-esm2 in your project. Claude Code loads it when a task matches its description.

How do I install Fair Esm2 in Codex?

Run `npx skills add JimLiu/science-skills --skill fair-esm2 -a codex`. Or copy the skill folder (skills/fair-esm2 in JimLiu/science-skills) into .agents/skills/fair-esm2 in your project. Codex loads it when a task matches its description.

Can I use Fair Esm2 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JimLiu/science-skills --skill fair-esm2 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fair-esm2, .gemini/skills/fair-esm2, .github/skills/fair-esm2 and .opencode/skills/fair-esm2 in your project.

What does Fair Esm2 need to run?

Going by SKILL.md and its folder, Fair Esm2 needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Fair Esm2 access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Fair Esm2 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fair Esm2 use?

Fair Esm2 is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fair Esm2 use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fair Esm2?

Skills that share tags, products or a category with Fair Esm2: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars) and CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fair Esm2?

JimLiu (a GitHub user) maintains it in JimLiu/science-skills, which has 227 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on July 1, 2026.

Source: JimLiu/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.