---

name: boltz description: "Route Boltz package tasks for biomolecular structure and affinity prediction, data preparation, training, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Boltz Repo Skill

Use this repo skill when a user asks about Boltz, the boltz Python package, or workflows for biomolecular structure prediction, binding-affinity prediction, training data preparation, retraining, or evaluation.

Quick Triage

• Run or debug boltz predict: use sub-skills/prediction/SKILL.md for CLI options, YAML/FASTA inputs, MSA server/authentication, affinity prediction, output files, cache behavior, and prediction-time troubleshooting.
• Prepare raw training/evaluation data: use sub-skills/data-preparation/SKILL.md for CCD, sequence clustering, MSA processing, RCSB/mmCIF processing, Redis, mmseqs, processed file layouts, and safe preflight checks.
• Train or retrain models: use sub-skills/training/SKILL.md for Hydra config edits, debug launches, checkpoint intent, resource knobs, wandb/DDP issues, and training-data readiness checks.
• Evaluate or summarize outputs: use sub-skills/evaluation/SKILL.md for confidence/affinity metrics, benchmark folder layouts, legacy OpenStructure evaluation scripts, CSV/JSON summaries, and top-1 versus oracle distinctions.

Install And Smoke Checks

Boltz is a Python package named boltz, with a public CLI entry point named boltz and a predict command. Public installation guidance recommends a fresh Python environment:

pip install boltz -U

For CUDA-enabled inference, public docs use the CUDA extra:

pip install 'boltz[cuda]' -U

Use CPU-only installs for inspection, input validation, and documentation tasks. Full prediction, training, and benchmark workflows may require GPU hardware, large model/data downloads, external tools, or long runtimes.

Run safe checks before expensive work:

python scripts/boltz_environment_check.py
boltz --help
boltz predict --help

Shared References

• references/package-overview.md — package purpose, installed facts, dependency/back-end expectations, and workflow map.
• references/troubleshooting.md — cross-cutting install/import, cache, GPU, data/config, CLI, and external dependency failures.
• references/repo-provenance.md — source repository snapshot used to generate this skill.
• references/repo-routing-metadata.json — structured routing metadata used by DisCo's managed repo-skills router.
• scripts/boltz_environment_check.py — safe local environment and CLI preflight helper.

Cross-Workflow Guardrails

• Prefer prediction YAML for new inference inputs; FASTA remains supported but is deprecated and lacks several YAML-only features.
• Do not run model downloads, MSA-server calls, full training, raw-data preprocessing, or benchmark evaluation until the user has confirmed hardware, storage, credentials, network, and runtime expectations.
• Treat original Boltz examples, docs, tests, and scripts as evidence. This skill bundles portable references and helpers so future agents do not need the original source checkout for routine guidance.
• Keep credentials out of prompts and logs. For MSA server secrets, prefer environment variables over inline CLI values.
• Be explicit about Boltz-2 documentation gaps: inspected docs mark updated Boltz-2 training and evaluation assets as coming soon.