---

name: colabfold description: "Use ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies." disable-model-invocation: true metadata: disco-role: operating license: MIT

ColabFold

Use this skill when a task involves ColabFold local or notebook-derived workflows: preparing sequence inputs, generating MSAs, planning or running colabfold_batch, exporting AlphaFold3 JSON, inspecting prediction outputs, or diagnosing ColabFold dependency and backend failures.

Start Here

1. Check whether the task is about the current source checkout or general ColabFold usage. If freshness matters, read references/repo-provenance.md.
2. Choose the narrowest install route from references/installation-and-backends.md.
3. Use the sub-skill that owns the user-facing workflow; do not send future agents to original repository notebooks, tests, scripts, or docs.
4. Run a bundled dry-run or read-only diagnostic before expensive network, database, GPU, prediction, or relaxation work.

Install Routes

• Search/input inspection only: install base ColabFold, which provides input helpers plus colabfold_search and colabfold_split_msas.
• Structure prediction: install prediction extras such as colabfold[alphafold] and a JAX build compatible with the target CPU/GPU.
• Amber relaxation: install OpenMM/PDBFixer support such as colabfold[openmm] and verify the CPU/GPU OpenMM platform before using --use-gpu.
• Local database search: install MMseqs2 separately and prepare the ColabFold databases; database setup is large and should be explicitly approved.

Minimal import check:

python - <<'PY'
import importlib.metadata as md
print(md.version("colabfold"))
PY

Optional environment diagnostic:

python scripts/check_colabfold_environment.py --check-entry-points --json

Route by Task

• Inputs and formats: use sub-skills/inputs-and-formats/SKILL.md for FASTA, CSV, A3M, AF3 molecule syntax, parser behavior, PDB/mmCIF input extraction, and validation with validate_colabfold_input.py.
• MSA search: use sub-skills/msa-search/SKILL.md for public MSA server use, colabfold_search, local MMseqs2 databases, GPU/gpuserver search, split/merge helpers, AF3 JSON from MSAs, and local MSA server planning.
• Batch prediction: use sub-skills/batch-prediction/SKILL.md for colabfold_batch, MSA-only then prediction staging, templates, model flags, parameter downloads, GPU/JAX planning, and AF3 JSON-only export.
• Relaxation and outputs: use sub-skills/relaxation-and-outputs/SKILL.md for colabfold_relax, OpenMM/PDBFixer choices, output directory inspection, score/PAE/pLDDT interpretation, plots, citations, and extra pTM/interface metrics.

Shared References and Scripts

• references/installation-and-backends.md explains package extras, external binaries, database storage, GPU/JAX/OpenMM choices, and safe verification.
• references/troubleshooting.md covers cross-cutting install/import, network/server, database, GPU, and optional dependency failures; sub-skills add workflow-specific details.
• scripts/check_colabfold_environment.py is a read-only diagnostic for package metadata, imports, console entry points, optional dependencies, MMseqs2, and GPU signals.

Safety and Resource Rules

• Do not run public MSA server queries, large local database setup, model parameter downloads, GPU predictions, or relaxation unless the user has approved the resource use.
• Treat original notebooks and tests as evidence only. Runtime instructions in this skill rely on bundled references and scripts.
• Prefer dry-run command planning and read-only validators before mutating output directories or launching long computations.
• If a requested workflow requires credentials, private sequences, large databases, or unavailable hardware, explain the missing prerequisite and provide a safe fallback or skip decision.