AlphaFold Repo Skill

Use this skill for AlphaFold 2.3.2 repository/package tasks involving protein structure prediction setup, input validation, local inference command planning, model configuration, output confidence interpretation, AlphaFold DB or Server JSON formats, and Amber relaxation.

Start Here

• Read references/package-overview.md for the capability map, public prerequisites, and safe/unsafe operation boundaries.
• Read references/troubleshooting.md for cross-cutting install, import, backend, data, Docker, and hardware failures.
• Read references/repo-provenance.md before deciding whether this skill matches a current AlphaFold checkout or should be refreshed.
• Use scripts/check_install.py to inspect an installed AlphaFold environment without running predictions, downloading data, or starting Docker.

Route by Task

User task	Go to
Build Docker/image commands, plan GPU/container requirements, or plan database/model-parameter downloads and updates	sub-skills/docker-and-data-setup/SKILL.md
Construct or diagnose direct run_alphafold commands, presets, database flags, MSA reuse, relaxation flags, random seeds, and output locations	sub-skills/prediction-cli/SKILL.md
Validate FASTA/MSA/template inputs, adapt notebook input validation, or reason about monomer/multimer data-pipeline APIs	sub-skills/input-data-and-formats/SKILL.md
Inspect model presets/configs, parameter-loading APIs, feature processing, JAX/Haiku/TensorFlow dependency constraints, or backend import errors	sub-skills/model-config-and-api/SKILL.md
Interpret prediction folders, ranked structures, confidence JSON, PAE/pTM/ipTM, AFDB formats, or AlphaFold Server JSON	sub-skills/outputs-and-confidence/SKILL.md
Decide whether/how to run Amber relaxation, switch GPU/CPU relax, inspect PDB relaxability, or debug OpenMM/PDBFixer failures	sub-skills/relaxation/SKILL.md

Safe Defaults

• Treat full AlphaFold prediction, Docker builds/runs, database downloads, model-parameter downloads, and AFDB/GCS/BigQuery operations as user-supervised external operations.
• Prefer bundled dry-run helpers first: validate paths, FASTA files, database layout, model presets, confidence files, and relaxation inputs before proposing expensive commands.
• Use --db_preset=reduced_dbs only with the small BFD database path; use --db_preset=full_dbs only with BFD and UniRef30 paths.
• Use --model_preset=multimer only with multimer FASTA inputs and the UniProt plus PDB SeqRes database paths.
• Keep the AlphaFold data directory outside the project/build context to avoid huge Docker builds.

Minimal Environment Check

Run the bundled diagnostic in the environment where AlphaFold is installed:

python scripts/check_install.py --check run_alphafold --check docker --check openmm --json

A passing import check does not prove that prediction is runnable. Full runs also need model parameters, genetic/template databases, external alignment binaries, writable output storage, and suitable CPU/GPU resources.

Common Decisions

• Choose docker-and-data-setup when the user asks how to install, download, update, mount, or run the documented Docker path.
• Choose prediction-cli when the user already has an installed package/environment and wants direct run_alphafold command construction or flag diagnosis.
• Choose input-data-and-formats when the failure or task is about FASTA contents, multimer chain count, MSA formats, templates, or notebook-style sequence validation.
• Choose outputs-and-confidence when the task starts after a prediction folder exists or involves AFDB/Server JSON files.
• Choose relaxation when the task mentions Amber, OpenMM, PDBFixer, models_to_relax, use_gpu_relax, or structural violations.

Do Not Do Automatically

• Do not start terabyte database downloads or model-weight downloads without explicit user approval.
• Do not run full prediction, benchmark, Docker build, Docker run, or relaxation minimization as a routine verification step.
• Do not assume cached MSAs are valid after the sequence, database, template cutoff, or model preset changes.
• Do not treat AlphaFold Server JSON as input to the local run_alphafold CLI; route Server JSON tasks to outputs-and-confidence.