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name: rfdiffusion description: "Use RFdiffusion for protein backbone generation workflows, including unconditional design, motif scaffolding, partial diffusion, binder design, symmetric oligomers, guided potentials, scaffold-guided design, and RFpeptides macrocycles." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

RFdiffusion

Use this skill when a user wants practical RFdiffusion command construction, workflow routing, input validation, or troubleshooting for protein structure generation. RFdiffusion generates protein backbones with Hydra-configured inference workflows, model checkpoints, contigs, optional targets, optional potentials, and workflow-specific inputs.

First Checks

Before building a production command, confirm:

• RFdiffusion imports in the active Python environment: python -c "import rfdiffusion; import rfdiffusion.inference.utils".
• The user has downloaded model weights and knows their runtime model directory, or will pass inference.model_directory_path=/path/to/models.
• The intended runtime has a compatible PyTorch backend; GPU is strongly preferred for real designs.
• Every input PDB, scaffold tensor, checkpoint, or output prefix is a user runtime path, not a path from any source checkout.
• Hydra list overrides are shell-quoted, for example 'contigmap.contigs=[100-200]'.

Use scripts/check_rfdiffusion_environment.py for a safe import/model-weight preflight and scripts/build_inference_command.py for reusable command assembly.

Route By Workflow

• Use sub-skills/unconditional-generation/SKILL.md for de novo monomer or backbone generation from only a length/range and output prefix.
• Use sub-skills/motif-scaffolding/SKILL.md for fixed motifs, active sites, inpaint sequence/structure masks, target-aware motif contigs, and motif checkpoint choices.
• Use sub-skills/partial-diffusion/SKILL.md for diversifying an existing backbone or complex with diffuser.partial_T and optional contigmap.provide_seq.
• Use sub-skills/binder-design/SKILL.md for PPI binders, hotspots, target/binder contigs, flexible peptide targets, scaffolded PPI, and downstream assessment boundaries.
• Use sub-skills/symmetric-oligomers/SKILL.md for cyclic, dihedral, tetrahedral, octahedral, or icosahedral oligomers and symmetric motif scaffolding.
• Use sub-skills/guided-potentials/SKILL.md for potentials.guiding_potentials, compactness/contact/substrate/oligomer potentials, guide scale, and guide decay.
• Use sub-skills/scaffold-guided-design/SKILL.md for fold conditioning with scaffold directories, secondary-structure tensors, block adjacency tensors, target tensors, and sampled insertions.
• Use sub-skills/macrocycle-design/SKILL.md for RFpeptides macrocyclic monomers and macrocyclic binders using inference.cyclic=True and inference.cyc_chains.

Shared References

• Read references/configuration-reference.md when translating a natural-language request into Hydra overrides.
• Read references/model-weights.md when the task depends on checkpoint filenames, model directories, or download expectations.
• Read references/troubleshooting.md for install/import, SE(3)-Transformer, DGL/e3nn, CUDA, model-weight, quoting, and output failures.
• Read references/repo-provenance.md before deciding whether this skill matches a current RFdiffusion checkout.

Common Command Shape

RFdiffusion’s central entrypoint is the installed run_inference.py script from the package distribution. Do not require a future agent to reopen the original repository examples; build commands from the bundled sub-skill references.

run_inference.py \
  inference.output_prefix=outputs/design \
  inference.num_designs=10 \
  inference.model_directory_path=/path/to/models \
  'contigmap.contigs=[100-200]'

For symmetric designs, add --config-name=symmetry. For macrocycles, do not use symmetry config unless the task is a symmetric oligomer; use the macrocycle sub-skill’s inference.cyclic=True route instead.

Output Expectations

A design prefix such as outputs/run/design normally yields numbered files such as outputs/run/design_0.pdb and outputs/run/design_0.trb. With trajectories enabled, RFdiffusion also writes multi-model trajectory PDBs below a sibling traj/ directory. The .trb file stores resolved config, timing/device information, pLDDT arrays, and contig mappings.

Boundaries

This skill helps future agents assemble and debug RFdiffusion workflows. It does not provide model weights, run network downloads by default, validate scientific binding success, replace downstream ProteinMPNN/structure-prediction pipelines, or promise CPU production performance. Treat native RFdiffusion examples as evidence; use the bundled references and scripts as the self-contained runtime guidance.