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evaluation

evaluation

Evaluate and summarize Boltz prediction outputs, benchmark result tables, and legacy OpenStructure-based evaluation workflows.

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Documentation de la Skill

GitHub

name: evaluation description: "Evaluate and summarize Boltz prediction outputs, benchmark result tables, and legacy OpenStructure-based evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Evaluation

Use this sub-skill when the task is to interpret Boltz output metrics, summarize prediction result files, diagnose benchmark/evaluation folder layout, or reason about legacy CASP/PDB evaluation workflows.

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Routing Boundaries

  • Use this sub-skill for: evaluating Boltz outputs, aggregating evaluation CSVs, interpreting affinity/confidence metrics, CASP/PDB benchmark layout, OpenStructure requirements, and top-1 versus oracle summaries.
  • Route raw boltz predict setup, YAML/FASTA input authoring, MSA server use, model checkpoints, and inference options to the prediction sub-skill.
  • Route training/evaluation jobs launched through PyTorch Lightning or Hydra training configs to the training sub-skill.
  • Route CCD, MSA, mmCIF preprocessing, dataset split construction, and benchmark input layout preparation to the data-preparation sub-skill.

Safe Defaults

  • Treat Boltz-2 benchmark evaluation files, setup, and scripts as not yet published when using the documented repository state.
  • Treat the available evaluation scripts as legacy Boltz-1 benchmark references unless newer Boltz-2 assets are supplied by the user.
  • Do not claim a local confidence or affinity JSON summary is a reproduced structural benchmark; structural benchmark metrics need targets, aligned benchmark folders, and OpenStructure.
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