How do I install the Esm skill?

Run npx skillmds@latest add gabrielmoreira/esm in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with Esm?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Esm by gabrielmoreira — Agent Skill

Name: Esm
Rating: 4.2 (5 reviews)
Author: gabrielmoreira

Esm

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

gabrielmoreira Updated Sep 21, 2026 17 repo stars

File contents

ESM2 Protein Language Model

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
PyTorch	1.10+	2.0+
CUDA	11.0+	11.7+
GPU VRAM	8GB	24GB (A10G)
RAM	16GB	32GB

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal

cd biomodals
modal run modal_esm2_predict_masked.py \
  --input-faa sequences.fasta \
  --out-dir embeddings/

GPU: A10G (24GB) | Timeout: 300s default

Option 2: Python API (recommended)

import torch
import esm

# Load model
model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
batch_converter = alphabet.get_batch_converter()
model = model.eval().cuda()

# Process sequences
data = [("seq1", "MKTAYIAKQRQISFVK...")]
batch_labels, batch_strs, batch_tokens = batch_converter(data)

with torch.no_grad():
    results = model(batch_tokens.cuda(), repr_layers=[33])

# Get embeddings
embeddings = results["representations"][33]

Key parameters

ESM2 Models

Model	Parameters	Speed	Quality
esm2_t6_8M	8M	Fastest	Fast screening
esm2_t12_35M	35M	Fast	Good
esm2_t33_650M	650M	Medium	Better
esm2_t36_3B	3B	Slow	Best

Output format

embeddings/
├── embeddings.npy       # (N, 1280) array
├── pll_scores.csv       # PLL for each sequence
└── metadata.json        # Sequence info

Sample output

Successful run

$ modal run modal_esm2_predict_masked.py --input-faa designs.fasta
[INFO] Loading ESM2-650M model...
[INFO] Processing 100 sequences...
[INFO] Computing pseudo-log-likelihood...

embeddings/pll_scores.csv:
sequence_id,pll,pll_normalized,length
design_0,-0.82,0.15,78
design_1,-0.95,0.08,85
design_2,-1.23,-0.12,72
...

Summary:
  Mean PLL: -0.91
  Sequences with PLL > 0: 42/100 (42%)

What good output looks like:

PLL_normalized: > 0.0 (more natural-like)
Embeddings shape: (N, 1280) for 650M model
Higher PLL = more natural sequence

Decision tree

Should I use ESM2?
│
├─ What do you need?
│  ├─ Sequence plausibility score → ESM2 PLL ✓
│  ├─ Embeddings for clustering → ESM2 ✓
│  ├─ Variant effect prediction → ESM2 ✓
│  └─ Structure prediction → Use ESMFold
│
├─ What model size?
│  ├─ Fast screening → esm2_t12_35M
│  ├─ Standard use → esm2_t33_650M ✓
│  └─ Best quality → esm2_t36_3B
│
└─ Use case?
   ├─ QC filtering → PLL > 0.0 threshold
   ├─ Diversity analysis → Mean-pooled embeddings
   └─ Mutation scanning → Per-position log-odds

PLL interpretation

Normalized PLL	Interpretation
> 0.2	Very natural sequence
0.0 - 0.2	Good, natural-like
-0.5 - 0.0	Acceptable
< -0.5	May be unnatural

Typical performance

Campaign Size	Time (A10G)	Cost (Modal)	Notes
100 sequences	5-10 min	~$1	Quick screen
1000 sequences	30-60 min	~$5	Standard
5000 sequences	2-3h	~$20	Large batch

Throughput: ~100-200 sequences/minute with 650M model.

Verify

wc -l embeddings/pll_scores.csv  # Should match input + 1 (header)

Troubleshooting

OOM errors: Use smaller model or batch sequences Slow processing: Use esm2_t12_35M for speed Low PLL scores: May indicate unusual/designed sequences

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	Sequence too long or large batch	Reduce batch size
`KeyError: representation`	Wrong layer requested	Use layer 33 for 650M model
`ValueError: sequence`	Invalid amino acid	Check for non-standard AAs

Next: Structure prediction with chai or boltz → protein-qc for filtering.

---name: esmdescription: ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.license: MIT---# ESM2 Protein Language Model## Prerequisites| Requirement | Minimum | Recommended ||-------------|---------|-------------|| Python | 3.8+ | 3.10 || PyTorch | 1.10+ | 2.0+ || CUDA | 11.0+ | 11.7+ || GPU VRAM | 8GB | 24GB (A10G) || RAM | 16GB | 32GB |## How to run> **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals.### Option 1: Modal```bashcd biomodalsmodal run modal_esm2_predict_masked.py \ --input-faa sequences.fasta \ --out-dir embeddings/```**GPU**: A10G (24GB) | **Timeout**: 300s default### Option 2: Python API (recommended)```pythonimport torchimport esm# Load modelmodel, alphabet = esm.pretrained.esm2_t33_650M_UR50D()batch_converter = alphabet.get_batch_converter()model = model.eval().cuda()# Process sequencesdata = [("seq1", "MKTAYIAKQRQISFVK...")]batch_labels, batch_strs, batch_tokens = batch_converter(data)with torch.no_grad(): results = model(batch_tokens.cuda(), repr_layers=[33])# Get embeddingsembeddings = results["representations"][33]```## Key parameters### ESM2 Models| Model | Parameters | Speed | Quality ||-------|------------|-------|---------|| esm2_t6_8M | 8M | Fastest | Fast screening || esm2_t12_35M | 35M | Fast | Good || esm2_t33_650M | 650M | Medium | Better || esm2_t36_3B | 3B | Slow | Best |## Output format```embeddings/├── embeddings.npy # (N, 1280) array├── pll_scores.csv # PLL for each sequence└── metadata.json # Sequence info```## Sample output### Successful run```$ modal run modal_esm2_predict_masked.py --input-faa designs.fasta[INFO] Loading ESM2-650M model...[INFO] Processing 100 sequences...[INFO] Computing pseudo-log-likelihood...embeddings/pll_scores.csv:sequence_id,pll,pll_normalized,lengthdesign_0,-0.82,0.15,78design_1,-0.95,0.08,85design_2,-1.23,-0.12,72...Summary: Mean PLL: -0.91 Sequences with PLL > 0: 42/100 (42%)```**What good output looks like:**- PLL_normalized: > 0.0 (more natural-like)- Embeddings shape: (N, 1280) for 650M model- Higher PLL = more natural sequence## Decision tree```Should I use ESM2?│├─ What do you need?│ ├─ Sequence plausibility score → ESM2 PLL ✓│ ├─ Embeddings for clustering → ESM2 ✓│ ├─ Variant effect prediction → ESM2 ✓│ └─ Structure prediction → Use ESMFold│├─ What model size?│ ├─ Fast screening → esm2_t12_35M│ ├─ Standard use → esm2_t33_650M ✓│ └─ Best quality → esm2_t36_3B│└─ Use case? ├─ QC filtering → PLL > 0.0 threshold ├─ Diversity analysis → Mean-pooled embeddings └─ Mutation scanning → Per-position log-odds```## PLL interpretation| Normalized PLL | Interpretation ||----------------|----------------|| > 0.2 | Very natural sequence || 0.0 - 0.2 | Good, natural-like || -0.5 - 0.0 | Acceptable || < -0.5 | May be unnatural |## Typical performance| Campaign Size | Time (A10G) | Cost (Modal) | Notes ||---------------|-------------|--------------|-------|| 100 sequences | 5-10 min | ~$1 | Quick screen || 1000 sequences | 30-60 min | ~$5 | Standard || 5000 sequences | 2-3h | ~$20 | Large batch |**Throughput**: ~100-200 sequences/minute with 650M model.---## Verify```bashwc -l embeddings/pll_scores.csv # Should match input + 1 (header)```---## Troubleshooting**OOM errors**: Use smaller model or batch sequences**Slow processing**: Use esm2_t12_35M for speed**Low PLL scores**: May indicate unusual/designed sequences### Error interpretation| Error | Cause | Fix ||-------|-------|-----|| `RuntimeError: CUDA out of memory` | Sequence too long or large batch | Reduce batch size || `KeyError: representation` | Wrong layer requested | Use layer 33 for 650M model || `ValueError: sequence` | Invalid amino acid | Check for non-standard AAs |---**Next**: Structure prediction with `chai` or `boltz` → `protein-qc` for filtering.

Frequently asked questions

Esm