fair-esm2
Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.
Profile →npx skills add xuzhougeng/wisp-science --skill fair-esm2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 | Requirement | Minimum | Recommended | | ----------- | ------- | ----------- | | Python | 3.8+ | 3.11 | | CUDA | 11.7+ | 12.x | | GPU VRAM | 8 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])
- Prerequisites
- How to run
- Embeddings
- Masked-LM scoring
- Contact prediction
- Models
- Output format
- Remote compute
- Troubleshooting
What does the fair-esm2 skill do?
Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill fair-esm2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.