Foundry Claude Skills
Claude Code skills for running Foundry protein design tools on Vast.ai GPU instances.
Uses a pre-built Docker image (see Dockerfile in the Foundry repo) with all tools, checkpoints, and dependencies pre-installed — including Enhanced MPNN weights. Instances are ready to use immediately after launch.
Skills
| Skill | Command | Description |
|---|
/rfd3 | rfd3 design | Run RFdiffusion3 all-atom protein structure design |
/rf3 | rf3 fold | Run RosettaFold3 structure prediction |
/mpnn | mpnn | Run ProteinMPNN/LigandMPNN/SolubleMPNN/Enhanced MPNN sequence design |
/run-foundry-job | Multi-step | Run complete design pipelines (e.g., RFD3 + Enhanced MPNN + RF3) |
Each skill launches a Vast.ai GPU instance with the Foundry Docker image, uploads input files, runs the job, downloads results, and destroys the instance.
Prerequisites
- Claude Code installed (docs)
- Vast.ai CLI installed and authenticated:
pip install vastai
vastai set api-key <YOUR_API_KEY>
- SSH keys configured on Vast.ai:
vastai show ssh-keys
# If empty, upload your public key:
vastai create ssh-key "$(cat ~/.ssh/id_ed25519.pub)"
- Foundry Docker image built and pushed to a registry (see
Dockerfile in the Foundry repo)
Installation
Option 1: Install from GitHub (recommended)
Step 1 — Add the marketplace.
From within Claude Code, run:
/plugin marketplace add LiorZ/foundry-claude-skill
Step 2 — Install the plugin.
/plugin install foundry@foundry-skills
This permanently registers the skills. They will be available in all future Claude Code sessions.
Step 3 — (Optional) Set project scope for team sharing.
To make the plugin available for everyone working in a specific project:
/plugin install foundry@foundry-skills --scope project
This writes the configuration to .claude/settings.json which can be committed to your repo.
Option 2: Install from a local path (for development)
Launch Claude Code with the --plugin-dir flag pointing to a local clone:
git clone [email protected]:LiorZ/foundry-claude-skill.git
claude --plugin-dir ./foundry-claude-skill
This loads all skills for that session. Useful for testing and development.
Alternatively, add a local clone as a marketplace:
/plugin marketplace add /path/to/foundry-claude-skill
/plugin install foundry@foundry-skills
Option 3: Manual configuration
Add the following to your Claude Code settings file (~/.claude/settings.json for user-wide, or .claude/settings.json in your project):
{
"extraKnownMarketplaces": {
"foundry-skills": {
"source": {
"source": "directory",
"path": "/path/to/foundry-claude-skill"
}
}
},
"enabledPlugins": {
"foundry@foundry-skills": true
}
}
Verifying installation
After installation, you should be able to invoke skills directly:
/rfd3 Design binders for my target protein
If the skills are loaded correctly, Claude will launch a forked agent that handles the full Vast.ai workflow.
Usage Examples
# Design protein binders targeting PD-L1
/rfd3 Design binders for PD-L1 using target.pdb with hotspots at A56, A115, A123
# Predict structure of a designed sequence
/rf3 Fold this sequence: MTSENPLLALREK...
# Design sequences for a backbone with Enhanced MPNN (best designability)
/mpnn Design 8 sequences for backbone.cif using Enhanced MPNN at temperature 0.1
# Design sequences with standard ProteinMPNN
/mpnn Design 10 sequences for backbone.cif using ProteinMPNN at temperature 0.1
# Run a full binder design pipeline
/run-foundry-job Design binders for target.pdb, then design sequences with Enhanced MPNN, then validate with RF3
Skills Reference
/rfd3 — RFdiffusion3 Design
Designs protein structures under complex constraints. Supports protein binders, enzyme active sites, nucleic acid binders, small molecule binders, and symmetric assemblies.
/rf3 — RosettaFold3 Prediction
Predicts biomolecular structures for proteins, nucleic acids, small molecules, and their complexes. Supports templating, covalent modifications, and MSAs.
/mpnn — Sequence Design
Fixed-backbone inverse folding with four model variants:
- ProteinMPNN — standard sequence design
- LigandMPNN — ligand-aware design (small molecules, DNA/RNA, ions)
- SolubleMPNN — solubility-optimized design for E. coli expression
- Enhanced MPNN — designability-optimized (recommended for de novo design). Fine-tuned LigandMPNN using ResiDPO that achieves ~2.7x better enzyme design success and ~2.3x better binder design success by optimizing directly for structural validation confidence rather than native sequence recovery.
