---

name: esm description: "Use the fair-esm repository skill for protein language-model embeddings, ESMFold structure prediction, ESM-IF1 inverse folding, and zero-shot variant-effect scoring." disable-model-invocation: true metadata: disco-role: operating license: MIT

ESM

Use this repo skill when a task mentions ESM, fair-esm, ESM-2, ESMFold, ESM-IF1, ESM-1v, MSA Transformer, esm-extract, or esm-fold. It routes future agents to self-contained guidance for protein language-model workflows without depending on the original repository checkout.

Quick Install Check

python -m pip install fair-esm
python - <<'PY'
import esm
print(esm.__version__)
print(esm.Alphabet.from_architecture("ESM-1b").mask_idx)
PY

For ESMFold and inverse folding, expect additional optional dependencies beyond base fair-esm. Read references/troubleshooting.md before mutating an environment.

Route By Task

• Use model-embeddings for loading ESM/ESM-2/MSA Transformer models, tokenizing protein sequences/MSAs, computing logits, representations, contacts, and constructing esm-extract commands.
• Use structure-prediction for ESMFold sequence-to-PDB workflows, esm-fold, model.infer_pdb, PDB output, pLDDT/pTM confidence, chunking, recycles, CPU-only, and CPU offload.
• Use inverse-folding for ESM-IF1 fixed-backbone sequence sampling, structure-conditioned log-likelihood scoring, PDB/mmCIF coordinate extraction, single-chain/multichain conditioning, and encoder representations.
• Use variant-effect-prediction for ESM-1v or MSA Transformer zero-shot DMS scoring with wt-marginals, masked-marginals, pseudo-ppl, mutation-offset validation, and A3M MSA inputs.

Model Family Cheat Sheet

User wording	Best route	Typical entry point
embeddings, logits, contacts, esm-extract, ESM-2	model-embeddings	esm.pretrained.load_model_and_alphabet(...), esm-extract
fold a sequence, output PDB, ESMFold, pLDDT, pTM	structure-prediction	esm.pretrained.esmfold_v1(), esm-fold
inverse folding, design sequence from backbone, score sequence against PDB	inverse-folding	esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
variant effect, DMS CSV, ESM-1v, MSA Transformer mutation scores	variant-effect-prediction	bundled variant helper and runner

Shared References

• Read references/troubleshooting.md for cross-cutting install/import, model download/cache, optional dependency, backend, CLI, and data issues.
• Read references/model-downloads-and-caches.md before running workflows that download pretrained weights or use --model-dir.
• Read references/repo-provenance.md to decide whether this generated skill is stale relative to the source repository state.
• references/repo-routing-metadata.json is structured metadata for DisCo import and router generation.

Safe Usage Defaults

1. Start with the smallest model that satisfies the task; use esm2_t6_8M_UR50D for smoke checks and command validation.
2. Treat model loading as a potential network/cache operation unless the weights are already available.
3. Use bundled command-builder helpers before running heavyweight CLIs; helpers default to printing commands or validating inputs.
4. Do not run native ESMFold, IF1, or DMS scoring on large inputs without confirming optional dependencies, device memory, and model-cache policy.
5. Keep public outputs reproducible: record model names, local checkpoint filenames when used, layer selections, input data schemas, offset numbering, chunk/recycle settings, and output paths.

Bundled Root Helper

Use scripts/check_esm_install.py for safe import/signature checks that do not download weights:

python scripts/check_esm_install.py

It verifies base esm import, distribution metadata when available, public alphabet behavior, and whether optional inverse-folding/ESMFold imports are present.