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name: model-config-and-api description: "Inspect and modify AlphaFold model presets, RunModel configuration, feature processing, parameter loading, and backend dependency issues." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AlphaFold Model Config and API

Use this sub-skill when a task is about AlphaFold's programmatic model layer: choosing model presets, inspecting or editing ml_collections configs, constructing RunModel objects, processing model features, loading Haiku parameters, diagnosing JAX/Haiku/TensorFlow imports, or using model-side geometry and lDDT helper APIs.

Start Here

• Run scripts/inspect_model_presets.py to list installed model presets and selected configuration fields without loading weights or running inference.
• Read references/model-overview.md for preset selection, config-editing patterns, RunModel lifecycle, dependency roles, and weight compatibility rules.
• Read references/api-reference.md for model config APIs, RunModel, parameter loading, feature processing, and numerical helper contracts.
• Read references/troubleshooting.md for import failures, JAX/JAXLIB mismatch, parameter path mismatches, TensorFlow/NumPy pin conflicts, GPU memory caveats, and preset/weight confusion.

Routing Boundaries

• Use this sub-skill for alphafold.model.config, alphafold.model.model.RunModel, alphafold.model.data.get_model_haiku_params, alphafold.model.features, model geometry helpers, lDDT/FAPE helpers, and dependency/backend diagnostics.
• Use ../prediction-cli/ for building run_alphafold commands, CLI flags, database path flags, MSA reuse, random seeds, and output-directory orchestration.
• Use ../outputs-and-confidence/ for interpreting ranking_debug.json, confidence_*.json, pae_*.json, pLDDT, PAE, pTM, ipTM, PDB, mmCIF, AFDB, or AlphaFold Server JSON outputs.
• Use ../relaxation/ for Amber/OpenMM relaxation configuration, stereochemical cleanup, GPU relaxation, and relaxation failure modes.
• Use ../input-data-and-formats/ for FASTA/MSA/template parsing and data-pipeline input formats before model feature processing.

Safe Operating Rules

• Do not load model weights, instantiate expensive model runners, call RunModel.predict, or run JAX compilation as a routine validation step.
• Treat model_config() inspection as safe; treat RunModel construction and feature processing as dependency-sensitive because JAX, Haiku, and TensorFlow may import or initialize runtime backends.
• Keep model names and parameter files aligned: model_1_ptm expects params_model_1_ptm.npz, while multimer v3 preset names expect params_model_1_multimer_v3.npz through params_model_5_multimer_v3.npz.
• Prefer explicit preset selection over manual ad hoc config edits, then make small config changes with with cfg.unlocked() or with dataclass_cfg.unfreeze() only when the task truly needs them.

Bundled Helper

python sub-skills/model-config-and-api/scripts/inspect_model_presets.py --json

The helper imports only alphafold.model.config, reads installed package metadata when available, prints preset/config summaries, and never opens parameter files or runs inference.