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 agentmods add skills/synthetic-sciences/openscience/esmnpx skills add synthetic-sciences/openscience --skill esmgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00086 | $0.02491 |
| Opus 5 | $0.00043 | $0.01246 |
| Sonnet 5 | $0.00017 | $0.00498 |
| Haiku 4.5 | $0.00009 | $0.00249 |
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 2d 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 — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ESM: Evolutionary Scale Modeling
Overview
ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.
Core Capabilities
1. Protein Sequence Generation with ESM3
Generate novel protein sequences with desired properties using multimodal generative modeling.
When to use:
- Designing proteins with specific functional properties
- Completing partial protein sequences
- Generating variants of existing proteins
- Creating proteins with desired structural characteristics
Basic usage:
from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
# Load model locally
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-sm-open-v1").to("cuda")
# Create protein prompt
protein = ESMProtein(sequence="MPRT___KEND") # '_' represents masked positions
# Generate completion
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
print(protein.sequence)
For remote/cloud usage via Forge API:
from esm.sdk.forge import ESM3ForgeInferenceClient
from esm.sdk.api import ESMProtein, GenerationConfig
# Connect to Forge
model = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", url="https://forge.evolutionaryscale.ai", token="<token>")
# Generate
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
See references/esm3-api.md for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.
2. Structure Prediction and Inverse Folding
Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).
Structure prediction:
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
# Predict structure from sequence
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_with_structure = model.generate(
protein,
GenerationConfig(track="structure", num_steps=protein.sequence.count("_"))
)
# Access predicted structure
coordinates = protein_with_structure.coordinates # 3D coordinates
pdb_string = protein_with_structure.to_pdb()
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 316 lines · 86 tokens per session scan A c9d08ee1a9fd
esm is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed today), licensed Apache-2.0. It adds 86 tokens to every session and 2,491 once invoked, about $0.0004 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.
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