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data-pipeline

data-pipeline

Prepare and validate AlphaFold 3 mmCIF/PDB, MSA, template, crop, sampling, and large-dataset curation inputs without performing unsafe acquisition or mutation.

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name: data-pipeline description: "Prepare and validate AlphaFold 3 mmCIF/PDB, MSA, template, crop, sampling, and large-dataset curation inputs without performing unsafe acquisition or mutation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data pipeline

Use this sub-skill when the task involves a local structure file, PDB dataset layout, MSA or template features, crop selection, weighted PDB sampling, or a plan for PDB/AFDB/CCD curation. Start with the safe preflight helper, then use the smallest applicable reference:

Run scripts/validate_data_layout.py --help before using it. The helper only checks explicitly supplied local paths, extensions, naming/layout conventions, CSV mapping headers/IDs, dates, and crop/config consistency. It never imports the package, downloads, parses-and-rewrites, filters, clusters, launches workers, starts a server, runs Kalign, or trains a model.

Keep this boundary explicit: model forward and sampling belong to model-inference; generic Alphafold3Input construction and atom conversion belong to input-representation; Trainer/YAML ownership belongs to training-configuration; CLI or interactive app operation belongs to cli-serving. This sub-skill can produce the PDB-derived features those areas consume, but does not execute their work.

Safe operating sequence

  1. Preflight the intended structure, MSA, template, CCD, and cluster paths with the bundled validator.
  2. Confirm whether the input is curated PDB, arbitrary mmCIF, or AFDB distillation data; do not infer this from a filename alone.
  3. Choose training/validation/inference/distillation semantics and an explicit crop, length, MSA, template, and cutoff policy.
  4. Only after a human-approved acquisition and resource plan, load PDBInput/a dataset and convert to atom inputs. Treat the curation and distillation acquisition interfaces as reference-only because they are networked, mutating, multiprocessing, or dataset-scale.
  5. Record missing optional features as intentional fallbacks, not as silently verified biological coverage.
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