---

name: openfold description: "Route OpenFold protein-structure prediction, data preparation, training, installation, and programmatic API tasks to focused repo skill guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OpenFold Repo Skill

Use this repo skill when a task is about OpenFold: installing or validating its runtime, planning protein-structure inference, preparing FASTA/mmCIF/MSA inputs, training or fine-tuning models, using OpenFold Python APIs, debugging optional CUDA/TensorRT/DeepSpeed dependencies, or deciding which OpenFold workflow fits a protein modeling request.

First Checks

1. Read references/repo-provenance.md when deciding whether this skill matches a current OpenFold checkout or should be refreshed.
2. Read references/capability-map.md for a compact route map from user tasks to sub-skills and bundled helpers.
3. Run scripts/check_openfold_imports.py --json in the target environment for a package import/config/extension check before deeper workflows.
4. Use references/troubleshooting.md for cross-cutting install/import/backend/data-asset triage, then route to the nearest sub-skill for workflow-specific recovery.

Route by Task

User task	Read next	Why
Install OpenFold, plan parameters/databases, diagnose import/build/backend failures, or validate runtime readiness	sub-skills/installation-assets/	Owns environment setup, assets, optional dependencies, external binaries, and safe environment checks.
Build or validate monomer, multimer, SoloSeq, precomputed-alignment, custom-template, long-sequence, or threading inference commands	sub-skills/inference/	Owns run_pretrained_openfold.py, thread_sequence.py, inference flags, outputs, relaxation, and inference troubleshooting.
Prepare FASTA, MSA, mmCIF, precomputed alignment, alignment DB, duplicate-chain, cluster-file, or cache inputs	sub-skills/data-preparation/	Owns input file layouts, parser facts, alignment/cache validators, and data-prep troubleshooting.
Train or fine-tune OpenFold, configure validation/distillation, resume checkpoints, plan DeepSpeed, or construct distributed training commands	sub-skills/training/	Owns train_openfold.py, training prerequisites, DeepSpeed/distributed options, checkpoints, and training command builders.
Use OpenFold from Python, inspect config presets, instantiate model APIs, import/convert weights, or reason about acceleration internals	sub-skills/model-apis/	Owns verified/source-backed API signatures, config presets, weights/checkpoints, output objects, metrics, and optional acceleration APIs.

Installation Baseline

• Public package/distribution name: openfold.
• Source metadata version used for this skill: 2.2.0.
• Documented production target: Linux, Python 3.10, PyTorch 2, CUDA-class GPU runtime for realistic inference/training.
• For a local checkout, install only after selecting a compatible PyTorch/CUDA runtime, then use python -m pip install --no-build-isolation -e . so setup.py can import the already-installed torch while building the OpenFold extension.
• OpenFold setup imports torch at build time and builds attn_core_inplace_cuda as a CUDA extension when CUDA/NVCC are available, otherwise as a CPU stub.
• Full inference/training usually requires large external parameters, sequence databases, template mmCIFs, HMMER/HHSuite/Kalign binaries, and GPU resources; do not run downloads, database searches, model inference, or training without explicit user approval.

Minimal package check:

python - <<'PY'
from openfold.config import model_config
from openfold.data.parsers import parse_fasta
cfg = model_config('model_1')
tags, seqs = parse_fasta('>query\nACD\n')
print('openfold config/parser ok', cfg.model.template.enabled, tags, seqs)
PY

For fuller diagnostics, use:

python scripts/check_openfold_imports.py --json

Shared Rules

• Keep inference and training commands as dry-run plans until parameters, databases, hardware, external binaries, and output paths are confirmed.
• If run_pretrained_openfold.py, train_openfold.py, or model imports fail with ModuleNotFoundError: attn_core_inplace_cuda, repair/rebuild the OpenFold install before claiming model or CLI readiness.
• Use precomputed alignments to avoid database searches only after validating the alignment directory or alignment DB layout in sub-skills/data-preparation/.
• Route missing TensorRT, cuda.cudart, DeepSpeed, cuEquivariance, FlashAttention, OpenMM, PDBFixer, HMMER, HHSuite, or Kalign errors through sub-skills/installation-assets/ first.
• Do not assume CPU-only execution is enough for full OpenFold training or production inference; CPU-only is mainly useful for import/config/data validation.

Bundled Root Files

• references/capability-map.md maps capabilities to sub-skill owners and safe helpers.
• references/troubleshooting.md covers cross-cutting install/import/runtime failures.
• references/repo-routing-metadata.json is structured metadata consumed by DisCo import tooling.
• scripts/check_openfold_imports.py performs lightweight import/config/module availability checks without model execution.