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데이터 및 연구 · SKILL.md

data-preparation

data-preparation

Prepare and validate OpenFold FASTA, mmCIF, MSA, alignment DB, cache, duplicate-chain, and cluster-file inputs.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: data-preparation description: "Prepare and validate OpenFold FASTA, mmCIF, MSA, alignment DB, cache, duplicate-chain, and cluster-file inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OpenFold Data Preparation

Use this sub-skill when the task is about OpenFold input data rather than running prediction, training, installation, or model internals. It covers FASTA records, precomputed MSA/template layouts, alignment_db.index, mmCIF and chain caches, duplicate-chain metadata, cluster files, and safe dry-run validation.

Route First

  • Read references/data-formats.md for concrete FASTA, alignment directory, alignment DB, mmCIF cache, chain cache, duplicate-chain, and cluster-file layouts.
  • Read references/alignment-workflows.md to plan precomputed alignment use, alignment DB conversion, and external binary/database requirements without launching expensive searches.
  • Read references/cache-and-db-workflows.md to plan mmCIF/chain caches, duplicate-chain expansion, alignment DB shards, cluster files, and training data readiness checks.
  • Read references/api-reference.md for parser, data-pipeline, multimer-pipeline, feature-pipeline, and cache-generation API expectations.
  • Read references/troubleshooting.md when OpenFold reports malformed FASTA/MSA/mmCIF data, missing alignment files, stale index byte ranges, cache mismatches, duplicate-chain/cluster problems, missing external binaries, or build-extension import errors.

Bundled Safe Helpers

  • scripts/validate_alignment_layout.py checks local precomputed alignment directories and alignment_db.index files for expected OpenFold filenames, FASTA-to-directory key matching, shard references, byte ranges, duplicate-chain coverage, and multimer uniprot_hits.sto pairing inputs.
  • scripts/inspect_mmcif_cache.py validates mmcif_cache.json, chain_cache.json, or chain_data_cache.json shape and reports missing keys, inconsistent chain/sequence counts, cluster mismatches, duplicate-chain coverage gaps, missing .cif files, and missing release-date/obsolete-entry planning inputs.
  • scripts/plan_alignment_db.py dry-runs an alignment DB sharding plan from a flattened alignment directory and optional duplicate-chain file. It prints a plan only and does not create DB files, indexes, symlinks, clusters, downloads, or OpenFold features.

Boundaries

  • Route prediction command construction and inference execution to ../inference/ after data inputs validate.
  • Route training command construction and training execution to ../training/ after caches, cluster files, and alignment inputs validate.
  • Route database downloads, parameter downloads, binary installation, and environment repair to ../installation-assets/.
  • Route model configs, checkpoint conversion, attention kernels, protein object internals, and feature tensor interpretation to ../model-apis/.

Safety Rules

  • Do not run downloads, HH-suite, HMMER, MMseqs, full cache generation, DB creation, inference, or training as a side effect of validation.
  • Do not tell users to run source-checkout scripts by path. Use the bundled validators/planners here, or describe heavy OpenFold data utilities as explicit, user-approved reference operations.
  • Keep local checkout paths, private environment paths, cache directories, credentials, and machine-specific install details out of public instructions, examples, and reports.
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