Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsnpx agentmods add skills/adaptyvbio/protein-design-skills/proteinmpnnWrote 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/proteinmpnn)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/proteinmpnn"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/proteinmpnn/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/proteinmpnn"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/proteinmpnn.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 | $0.00106 | $0.02245 |
| Opus 5 | $0.00053 | $0.01123 |
| Sonnet 5 | $0.00021 | $0.00449 |
| Haiku 4.5 | $0.00011 | $0.00225 |
Grade A, and why
proteinmpnn 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProteinMPNN Sequence Design
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 11.7+ |
| GPU VRAM | 8GB | 16GB (T4) |
| RAM | 8GB | 16GB |
How to run
First time? See Getting started to set up Modal and biomodals.
Option 1: Local installation (recommended)
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
python protein_mpnn_run.py \
--pdb_path backbone.pdb \
--out_folder output/ \
--num_seq_per_target 16 \
--sampling_temp "0.1"
GPU: T4 (16GB) sufficient | Time: ~50-100 sequences/minute
Option 2: Modal (via LigandMPNN wrapper)
cd biomodals
# modal_ligandmpnn.py takes --input-pdb and forwards run.py args via --params-str
modal run modal_ligandmpnn.py \
--input-pdb backbone.pdb \
--params-str "--model_type protein_mpnn --number_of_batches 16 --temperature 0.1"
GPU (Modal): A10G default | Timeout: 900s default
Note: LigandMPNN includes ProteinMPNN functionality (select with --model_type protein_mpnn).
Config Schema
Core Parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
--pdb_path |
required | path | Single PDB input |
--pdb_path_chains |
all | A,B | Chains to design (comma-sep) |
--out_folder |
required | path | Output directory |
--num_seq_per_target |
1 | 1-1000 | Sequences per structure |
--sampling_temp |
"0.1" | "0.0001-1.0" | Temperature (string!) |
--seed |
0 | int | Random seed |
--batch_size |
1 | 1-32 | Batch size |
Temperature Guide
0.1 -> Low diversity, high recovery (production)
0.2 -> Moderate diversity (default)
0.3 -> Higher diversity (exploration)
0.5+ -> Very diverse, lower quality
IMPORTANT: Temperature must be passed as a string, not float.
Common mistakes
Temperature Parameter
✅ Correct:
--sampling_temp "0.1" # String with quotes
What ships with it
1 file 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.
- 12d ago First seen · 283 lines · 106 tokens per session scan A 848e6f510bef
proteinmpnn is a skill published in the GitHub repository adaptyvbio/protein-design-skills (159 stars, last pushed 3mo ago), licensed MIT. It adds 106 tokens to every session and 2,245 once invoked, about $0.0005 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
proteinmpnn
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design.…
ligandmpnn
Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For…
solublempnn
Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn.…
proteinmpnn
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design.…
ligandmpnn
Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For…
solublempnn
Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn.…