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name: unconditional-generation description: "Build safe RFdiffusion unconditional monomer/backbone generation commands, including Hydra contig overrides, output prefixes, deterministic/cautious smoke runs, model-weight prerequisites, and output-file interpretation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Unconditional Generation

Use this sub-skill when a user wants RFdiffusion to generate a de novo monomer backbone without a motif, target, binder, scaffold guide, macrocycle, or symmetry constraint.

Route Here

• The request is for an unconditional monomer, de novo backbone, or random protein of a requested length or length range.
• The main user inputs are protein length, output location, number of designs, and optional cautious/deterministic/debug settings.
• The command can be expressed with contigmap.contigs, inference.output_prefix, and inference.num_designs against the base inference config.
• The user asks about .pdb, .trb, or traj/ outputs from a base RFdiffusion generation run.
• The user asks for a smoke-check command that runs one deterministic design without overwriting existing outputs.

Route Elsewhere

• Fixed motif residues, input PDB motif contigs, sequence/structure inpainting, or active-site checkpoints belong in ../motif-scaffolding/SKILL.md.
• Auxiliary potentials such as monomer_ROG or monomer_contacts may start here only for the unconditional base command; route detailed potential selection and tuning to ../guided-potentials/SKILL.md.
• Cyclic, dihedral, tetrahedral, or other symmetry requests belong in ../symmetric-oligomers/SKILL.md.
• Partial diffusion from an existing structure belongs in ../partial-diffusion/SKILL.md.
• Binder, hotspot, receptor/target, fold-conditioned scaffold-guided, or macrocycle workflows belong in their sibling sub-skills.

Required Context

Collect these before forming a command:

• run_inference.py entry point: prefer the user's installed/package-provided RFdiffusion launcher; do not assume an original checkout path.
• Model weights: know either the default model directory used by the installation or an explicit inference.model_directory_path=/path/to/models.
• Output prefix: a path prefix, not a directory. RFdiffusion appends design indices such as _0.pdb and _0.trb.
• Length intent: exact length like 150-150 or sampled range like 100-200.
• Number of designs: inference.num_designs=N; use 1 for smoke checks.
• Overwrite policy: default inference.cautious=True skips existing indexed .pdb files.

Command Pattern

Use the workflow reference for concrete templates: references/workflows.md.

Core unconditional command:

python /path/to/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=outputs/unconditional/design inference.num_designs=10

For shell safety, keep Hydra list-like values in single quotes. The contig must be a single-item Hydra list, so use 'contigmap.contigs=[150-150]', not contigmap.contigs=150-150.

If the integrated root skill includes helper scripts, prefer them for command assembly or environment checks:

python ../../scripts/check_rfdiffusion_environment.py --models /path/to/models
python ../../scripts/build_inference_command.py unconditional --contig 150-150 --output-prefix outputs/unconditional/design --num-designs 10

If those helpers are not present in the delivered skill copy, write the command directly from references/workflows.md.

Base Overrides To Prefer

• contigmap.contigs=[L-L]: exact unconditional backbone length.
• contigmap.contigs=[MIN-MAX]: sample a length in the range for each design.
• inference.output_prefix=outputs/name/prefix: creates prefix_0.pdb, prefix_0.trb, and optionally traj/prefix_0_*_traj.pdb.
• inference.num_designs=1: safest initial run; increase only after the environment and weights work.
• inference.deterministic=True: seeds each design index deterministically.
• inference.final_step=48: shortens inference in the repository's smoke-test pattern; use for checks, not final production quality by default.
• inference.cautious=True: default skip-on-existing-output behavior; keep enabled unless the user explicitly wants replacement.
• inference.write_trajectory=False: reduces output volume when trajectories are not needed.
• inference.model_directory_path=/path/to/models: points RFdiffusion at downloaded model weights.
• inference.ckpt_override_path=/path/to/checkpoint.pt: use only when a documented workflow needs a non-default checkpoint.

Avoid changing model, preprocess, or low-level diffuser settings for ordinary unconditional generation unless the user is deliberately experimenting and understands the training/inference coupling.

Output Interpretation

A design with inference.output_prefix=outputs/unconditional/design and default design_startnum=0 produces:

• outputs/unconditional/design_0.pdb: final generated backbone; unconditional residues are written as glycine with backbone atoms.
• outputs/unconditional/design_0.trb: pickled run metadata, resolved config, timing/device information, pLDDT stack, and contig mappings.
• outputs/unconditional/traj/design_0_Xt-1_traj.pdb: reverse diffusion trajectory input states when write_trajectory=True.
• outputs/unconditional/traj/design_0_pX0_traj.pdb: predicted denoised states when write_trajectory=True.

The trajectory PDBs are multi-model files ordered for visualization, with the final timestep first.

Deterministic And Cautious Runs

For a minimal reproducible check, combine:

python /path/to/run_inference.py 'contigmap.contigs=[60-60]' inference.output_prefix=outputs/smoke/design inference.num_designs=1 inference.deterministic=True inference.final_step=48 inference.cautious=True inference.write_trajectory=False

RFdiffusion seeds deterministic runs from the design index. If design_startnum=0, _0 is reproducible for the same environment, checkpoint, and hardware backend. If inference.cautious=True and outputs/smoke/design_0.pdb already exists, RFdiffusion skips that design instead of overwriting it.

Use inference.design_startnum=-1 when the user wants RFdiffusion to scan existing output_prefix_*.pdb files and continue at the next numeric index.

Model And Environment Checks

Before blaming contigs, verify:

• rfdiffusion, rfdiffusion.contigs, rfdiffusion.diffusion, and rfdiffusion.inference.utils import in the active Python environment.
• PyTorch is installed and the intended CPU/GPU backend is visible; GPU is strongly preferred for real RFdiffusion runs.
• Model weights are downloaded and discoverable by RFdiffusion, either through installation defaults or inference.model_directory_path.
• Optional explicit checkpoints passed with inference.ckpt_override_path exist and match the requested workflow.

The first inference run may pause while calculating and caching IGSO3 schedules. That is expected; later runs reuse the cache when the cache directory is writable.

Common Misuse To Correct

• Unquoted contigs: replace contigmap.contigs=[100-200] with 'contigmap.contigs=[100-200]' in shell commands.
• Missing list brackets: replace 'contigmap.contigs=100-200' with 'contigmap.contigs=[100-200]'.
• Treating output_prefix as a directory: explain that RFdiffusion appends _0, _1, and file extensions.
• Expecting sidechains: unconditional .pdb outputs are backbone-focused and write designed residues as glycine.
• Using potential examples as base unconditional advice: route potential tuning to ../guided-potentials/SKILL.md after the base command is sound.
• Overwriting surprises: keep inference.cautious=True or choose a fresh prefix.

Evidence Base

This sub-skill is backed by RFdiffusion repository evidence from the README unconditional execution/output sections, the base inference config, the inference launcher behavior, unconditional example scripts, and the native test harness pattern that rewrites examples to deterministic one-design smoke runs.

Use references/troubleshooting.md for error triage and references/workflows.md for command templates.