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name: cli-and-inference description: "Build safe Protenix prediction commands and debug CLI inference setup, model selection, output layout, cache/checkpoint expectations, and user-facing kernel choices." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Protenix CLI and Inference

Use this sub-skill when a user needs help with protenix pred, root CLI help, prediction model selection, inference flags, cache/checkpoint side effects, output locations, or command-shape diagnosis. It is designed for planning and debugging without launching expensive inference.

Fast Triage

1. Confirm the installed command shape first: protenix --help and protenix pred --help. The verified console commands are pred, json, msa, mt, and prep.
2. For command planning, use the bundled no-run builder: python sub-skills/cli-and-inference/scripts/build_protenix_pred_command.py --input input.json --out-dir output --print-warnings. It prints a shell-quoted protenix pred command and never imports or runs Protenix.
3. Prefer explicit boolean values for protenix pred: use --use_template true, --use_rna_msa false, and similar value pairs rather than bare flags.
4. Start routine prediction commands from --model_name protenix_base_default_v1.0.0 --use_default_params true unless the user explicitly needs Protenix-v2, a 2025 cutoff model, or a mini/tiny smoke-test shape.
5. Set only user-provided data/cache roots at runtime. Do not invent checkpoint paths; Protenix derives the checkpoint directory from the runtime PROTENIX_ROOT_DIR environment variable and auto-downloads needed cache/checkpoint files when missing.
6. For CUDA/kernel failures, first build a no-run fallback command with --trimul-kernel torch --triatt-kernel torch; route deeper kernel installation, CUTLASS, cuEquivariance, or config surgery to ../advanced-model-configuration/SKILL.md.

References

• references/cli-reference.md covers root command registry facts, pred flag names, boolean handling, and no-run command construction.
• references/model-selection.md covers model capabilities, default parameters, seed behavior, dtype, TFG routing, cache/checkpoint expectations, and user-facing kernel choices.
• references/output-layout.md covers prediction output directories, sample/confidence file naming, atom-confidence output, and ERR behavior.
• references/troubleshooting.md covers missing CLI/version mismatches, accidental launches, missing caches/checkpoints, preprocessing dependencies, kernel errors, output surprises, and feature/model mismatches.

Route Elsewhere

• Input JSON schemas, entity fields, ligands, constraints, modelSeeds, templatesPath, rna_msa fields, and example JSON interpretation belong to ../input-data-and-features/SKILL.md.
• Protein MSA, template search, RNA MSA search, external databases, FASTA inputs, and preprocessing workflows belong to ../msa-template-and-prep/SKILL.md.
• Training, finetuning, training data preparation, torchrun training, and dataset configs belong to ../training-and-data-pipeline/SKILL.md.
• Deep kernel installation/debugging, model config internals, TFG internals, and backend performance tuning belong to ../advanced-model-configuration/SKILL.md.