skills/setup/SKILL.md
First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.
npx skillsauth add adaptyvbio/protein-design-skills setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Help users get their environment ready to run protein design tools.
Run through this checklist when a user encounters setup issues:
| Step | Check | Fix |
|------|-------|-----|
| 1. Modal CLI | modal --version | pip install modal |
| 2. Modal auth | modal token show | modal setup |
| 3. biomodals | ls biomodals/modal_*.py | git clone https://github.com/hgbrian/biomodals |
| 4. Test | cd biomodals && modal run modal_boltzgen.py --help | See troubleshooting |
Cause: Modal CLI not installed.
Fix:
pip install modal
Then restart the terminal or run hash -r.
Cause: Modal not authenticated.
Fix:
modal setup
This opens a browser. Click "Authorize" to complete authentication.
Cause: biomodals repository not cloned or not in correct directory.
Fix:
git clone https://github.com/hgbrian/biomodals
cd biomodals
Cause: uvx is an optional wrapper from the uv package. It's not required.
Fix: Run modal directly (recommended):
modal run modal_boltzgen.py --help
Or install uv if you prefer using uvx:
pip install uv
pip install modal
Verify: modal --version
modal setup
This opens a browser. Click "Authorize".
Verify: modal token show
git clone https://github.com/hgbrian/biomodals
cd biomodals
Verify: ls modal_*.py should show files like modal_boltzgen.py
cd biomodals
modal run modal_boltzgen.py --help
Expected: Usage instructions appear showing --input-yaml, --protocol, --num-designs options.
Once setup is complete, users can:
cd biomodals
# Design binders with BoltzGen (requires YAML config)
modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 50
# Generate backbones with RFdiffusion
modal run modal_rfdiffusion.py --pdb target.pdb --contigs "A1-150/0 70-100" --num-designs 100
# Validate with Chai
modal run modal_chai1.py --input-faa designs.fasta
Set GPU with environment variable:
GPU=A10G modal run modal_rfdiffusion.py --pdb target.pdb --contigs "A1-100/0 50-80" --num-designs 10
GPU=L40S modal run modal_boltzgen.py --input-yaml config.yaml --num-designs 50
GPU=A100 modal run modal_chai1.py --input-faa complex.fasta
| GPU | VRAM | Best For | |-----|------|----------| | T4 | 16GB | ProteinMPNN, ESM | | A10G | 24GB | RFdiffusion, Chai | | L40S | 48GB | BoltzGen, BindCraft | | A100 | 40-80GB | Large complexes |
Modal offers $30/month in free credits - enough for:
Full documentation: See Installation Guide
testing
Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.
development
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. For ligand-aware design, use ligandmpnn.
development
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
testing
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. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.