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 naity/FM4Life --skill esm3git clone --depth 1 https://github.com/naity/FM4LifeWrote 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/naity/fm4life/esm3)<a href="https://agentmods.dev/skills/naity/fm4life/esm3"><img src="https://agentmods.dev/badge/skills/naity/fm4life/esm3.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00113 | $0.02028 |
| Opus 5 | $0.00056 | $0.01014 |
| Sonnet 5 | $0.00023 | $0.00406 |
| Haiku 4.5 | $0.00011 | $0.00203 |
Grade A, and why
esm3 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 7d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ESM3: Multimodal Generative Protein Design
Overview
ESM3 is a generative protein language model from EvolutionaryScale that reasons simultaneously over sequence, structure, and function. Unlike ESM2 (discriminative, embeddings only), ESM3 generates proteins using iterative masked language modeling — you provide partial information in any combination of modalities and ESM3 fills in the rest.
Core use cases:
- Complete partial/masked sequences
- Structure prediction from sequence (fast alternative for design iterations)
- Inverse folding — design sequences that fold to a target structure
- Function-conditioned generation — proteins with specific functional annotations
- Chain-of-thought design — iterate across sequence → structure → function tracks
Installation
pip install esm
# Optional: Flash Attention for 2–4× faster inference on GPU
pip install flash-attn --no-build-isolation
Model Selection
| Model | Params | Access | Best for |
|---|---|---|---|
esm3-sm-open-v1 |
1.4B | Local (open weights) | Development, experimentation, fine-tuning |
esm3-medium-2024-08 |
7B | Forge API | Production quality |
esm3-large-2024-03 |
98B | Forge API | Maximum accuracy |
esm3-medium-multimer-2024-09 |
7B | Forge API | Protein complexes (experimental) |
Only esm3-sm-open-v1 runs locally. All other models require a Forge API token from https://forge.evolutionaryscale.ai.
Core Concepts
ESMProtein — the data container
from esm.sdk.api import ESMProtein
protein = ESMProtein(
sequence="MPRT____KEND", # use '_' to mark positions to generate
coordinates=coords, # optional: (L, 37, 3) numpy array of atom coords
secondary_structure="HHHCCC", # optional: per-residue H/E/C annotation
function_annotations=[...], # optional: FunctionAnnotation objects
sasa=sasa_array, # optional: per-residue solvent accessibility
)
# Load from / export to PDB
protein = ESMProtein.from_pdb("structure.pdb")
pdb_str = protein.to_pdb()
What ships with it
2 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.
- 7d ago First seen · 226 lines · 113 tokens per session scan A 1ae9c5a2d9e3
esm3 is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 5mo ago), licensed MIT. It adds 113 tokens to every session and 2,028 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 2026-08-31.
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