Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adaptyvbio/protein-design-skills --skill esmgit clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adaptyvbio/protein-design-skills/esm)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/esm"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/esm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/esm"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/esm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00121 | $0.02236 |
| Opus 5 | $0.00060 | $0.01118 |
| Sonnet 5 | $0.00024 | $0.00447 |
| Haiku 4.5 | $0.00012 | $0.00224 |
Grade A, and why
esm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ESM Protein Language Models
The ESM line is maintained at github.com/Biohub/esm
(Chan Zuckerberg Biohub, MIT license; the older evolutionaryscale/esm URL
redirects here). The current generation ships three artifacts: ESM C (language
model), ESMFold2 (structure prediction), and ESM Atlas (a map of predicted
structures). Weights are on huggingface.co/biohub;
the hosted API is at biohub.ai.
This skill covers ESM C, ESMFold2, and legacy ESM2. ESM3 is not covered because its open weights are non-commercial.
Which model to use
| Task | Model |
|---|---|
| Embeddings, PLL, mutation scoring | ESM C (ESMC-6B), or ESM2 for a lighter run |
| Complex structure prediction | ESMFold2 |
| High-throughput single-sequence folding | ESMFold2 fast mode |
| Binder design | ESMFold2 inversion (see below), or the mosaic / bindcraft skills |
| Variant effect / zero-shot scoring | ESM C or ESM2 |
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| PyTorch | 2.0+ | Latest |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB (ESM2 / small ESMC) | 80GB (ESMC-6B, ESMFold2) |
ESM C: embeddings and scoring
ESM C is the successor to ESM2. It improves long-range structural understanding as model scale grows and is the default choice for embeddings, pseudo-log-likelihood, and mutation-effect scoring.
Python (Hugging Face)
from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
model_id = "biohub/ESMC-6B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(
model_id, output_hidden_states=True, torch_dtype=torch.bfloat16
).eval().cuda()
batch = tok(["MKTAYIAKQRQISFVK..."], return_tensors="pt").to("cuda")
with torch.no_grad():
out = model(**batch)
logits = out.logits # for PLL / mutation scoring
embeddings = out.hidden_states[-1] # per-residue representations
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 202 lines · 121 tokens per session scan A 2934ea52ed34
esm is a skill published in the GitHub repository adaptyvbio/protein-design-skills (159 stars, last pushed 3mo ago), licensed MIT. It adds 121 tokens to every session and 2,236 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
esm2-sequence-scoring
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. 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…
esm2-sequence-scoring
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. 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…
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.…
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.…
bindcraft
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high…
protein-design
Computational protein design toolkit. Use this skill group when: (1) Designing protein binders from scratch, (2) Predicting protein structures (Chai, Boltz, Protenix), (3) Sequence design with ProteinMPNN/LigandMPNN/SolubleMPNN, (4) Quality control and filtering of protein designs, (5) Planning and managing binder…