Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or EvolutionaryScale Forge API. Use AlphaFold for traditional folding; RDKit for small molecules.
SkillScore100Maintained
Automatic
◆AI Engineer pack
Last verified 8/23/2026
What it changes
Passed 22 of 22 test cases with the skill, and 11 of 22 without it.
Without skill50%
With skill100%
Checks it passed that the model alone missed
Uses ESMC rather than ESM3
Instantiates ESM3ForgeInferenceClient
Sets num_steps=32 in GenerationConfig
and 17 more in the full report
11 fixed11 passed either way0 still failing0 broke
Pass-rate lift
+50 pp
Tokens
+185%
Agent turns
0%
Response time
-46%
Model gemini-3.6-flashMeasured 8/23/2026v122 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 11
Prompt
Write Python code initializing EvolutionaryScale Forge client relying on standard environment variable authentication rather than passing token parameter.
What was checked, without and with the skill
Checked
Without skill
With skill
Instantiates ESM3ForgeInferenceClient without explicit token argument
Missed
Passed
Relies on FORGE_API_TOKEN environment variable
Missed
Passed
case-14 · gemini-3.6-flash · 8/23/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md390 linessingle file
ESM — Protein Language Models
Overview
ESM (Evolutionary Scale Modeling) provides pretrained protein language models for generative protein design and representation learning. ESM3 is a multimodal generative model conditioned on sequence, structure, and function simultaneously. ESM C is an efficient embedding model optimized for extracting protein representations for downstream ML tasks.
When to Use
Generating novel protein sequences conditioned on desired structure or function
Extracting fixed-length embeddings from protein sequences for classification, clustering, or regression
Predicting 3D structure from amino acid sequence
Inverse folding: designing sequences that fold into a target structure
Annotating proteins with functional keywords (GO terms, EC numbers)
Comparing protein similarity via embedding distance instead of sequence alignment
Chain-of-thought protein design: iterative refinement of sequence/structure/function
For traditional physics-based structure prediction, use AlphaFold instead
For sequence alignment and homology search, use BLAST/HMMER via BioPython instead
Prerequisites
Python packages: esm (EvolutionaryScale package)
Hardware: GPU recommended for local inference (ESM3: 8GB+ VRAM; ESM C: 4GB+ VRAM). CPU works for small batches
Cloud alternative: EvolutionaryScale Forge API (requires API token from forge.evolutionaryscale.ai)
Model weights: Downloaded automatically on first use (~1-4 GB depending on model)
pip install esm
# For Forge cloud API
pip install esm[forge]
Quick Start
from esm.models.esmc import ESMC
from esm.sdk.api import ESMProtein
# Load ESM C model for embeddings
model = ESMC.from_pretrained("esmc_600m")