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name: batch-prediction description: "Plan and run ColabFold batch structure prediction workflows, including model choice, MSA-only/prediction splits, templates, AF3 JSON export, outputs, and prediction dependency failures." disable-model-invocation: true metadata: disco-role: operating license: MIT

Batch Prediction

Use this sub-skill when the user needs to run or plan colabfold_batch structure prediction, split MSA generation from GPU prediction, choose model/runtime flags, export AlphaFold3 JSON from ColabFold inputs, interpret prediction outputs, or recover from missing AlphaFold/JAX/GPU/parameter prerequisites.

Route First

• For FASTA, CSV, A3M, AF3 non-protein syntax, query naming, or input validation, route to ../inputs-and-formats/SKILL.md.
• For colabfold_search, local MMseqs2 databases, split/merge MSA directories, or MSA server deployment, route to ../msa-search/SKILL.md.
• For Amber relaxation after prediction, colabfold_relax, output inspection, citations, and visualization details, route to ../relaxation-and-outputs/SKILL.md.

Core References

• Start with references/cli-reference.md for safe colabfold_batch command construction.
• Use references/workflows.md for common one-step, two-step, template, complex, and AF3 JSON workflows.
• Use references/model-and-output-reference.md for model type selection, parameter files, ranking metrics, and expected output files.
• Use references/troubleshooting.md when prediction fails before or during AlphaFold/JAX execution.

Safe Planning Helper

Use the bundled dry-run helper to produce commands without running predictions, querying servers, or downloading model parameters:

python scripts/plan_colabfold_batch_command.py input_sequences.fasta out_dir --complex --two-step --templates --use-pallas

The helper only validates paths syntactically and prints command lines plus prerequisite notes. Treat generated commands as a plan: review hardware, input ownership, server policy, and output paths before execution.

Default Prediction Rules

• Install the prediction extras before structure prediction: pip install colabfold[alphafold] plus a compatible jax build for the target CPU/GPU.
• colabfold_batch input.fasta results/ queries the MSA server when the input lacks embedded A3M/MSA data and then runs prediction.
• --msa-only sets prediction models to zero and writes reusable MSA/template intermediates instead of structures.
• Re-running colabfold_batch input.fasta results/ after --msa-only uses the output directory to continue into prediction.
• --af3-json generates AlphaFold3-compatible JSON and returns without structure prediction.
• --model-type auto resolves to alphafold2_ptm for monomers and alphafold2_multimer_v3 for complexes.

Safety Notes

• Do not run heavyweight model downloads, GPU predictions, public MSA server queries, or local database searches unless the user has approved those resources.
• Do not reference original repository notebooks, tests, or checkout paths at runtime; this sub-skill distills their behavior into the bundled references.
• Prefer planning commands first, then running the smallest representative job before scaling a batch.