executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
input-representationBuild, validate, batch, serialize, and export AlphaFold 3 molecule and atom inputs without running model inference.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when a task starts from protein, RNA, DNA, ligand, or metal entities and needs a valid AlphaFold 3 input graph, atom-level features, collation, serialization, or structure-output conversion.
scripts/inspect_input.py --help before using the bundled, no-inference
validator. It accepts a small JSON specification, emits deterministic
summaries, and can validate a temporary serialization round trip.The normal conversion is:
Alphafold3Input → molecule-length input → AtomInput →
BatchedAtomInput → model-facing keyword arguments.
Prefer Alphafold3Input for sequence/SMILES/ion descriptions and AtomInput
when atom-level features already exist. Keep MSA, template, PDB/mmCIF loading,
cropping, and dataset preparation in data-pipeline.
Keep forward, loss, sampling, checkpoint, and confidence behavior in
model-inference. Keep trainer and dataloader
configuration in training-configuration.
Alphafold3Input must contain at least one supported entity. Use
explicit AtomInput data for precomputed or unusual atom-level cases.[batch, atoms, 3], while standalone alphafold3_input_to_biomolecule
expects token-major [tokens, 47, 3] coordinates. Do not interchange them.