>
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add aipoch/open-science --skill openfold3 -a cursor -y
>
name: openfold3 description: > Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation. license: Apache-2.0 category: biomodels requirements: [gpu] metadata: display-name: OpenFold3
--use-msa-server DEFAULTS to true (MSA server is
# api.colabfold.com), so the sequence leaves the machine unless opted out.
# No published ToS — wiki is the closest data-use reference.
- kind: service
name: ColabFold MSA server (api.colabfold.com)
provider: Steinegger Lab
info_url: | Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.1+ | 12.4+ |
| GPU VRAM | 24GB | 80GB (H100) |
| RAM | 32GB | 64GB |
| Disk (weights) | 3GB | - |
pip install 'openfold3[cuequivariance]==0.4.1'
The default attention kernel is DeepSpeed DS4Sci_EvoformerAttention. If
DeepSpeed is unavailable, switch to the cuEquivariance triangle kernels (no
build-from-source) by overriding the eval memory settings in
model_config.py (use_deepspeed_evo_attention: False,
use_cueq_triangle_kernels: True). Some pre-built environments already ship
this override; check before re-patching.
Apache-2.0, ~2.3 GB from HF OpenFold/OpenFold3. The repo is gated (auto-approval) — accept the access form on the HF model page and authenticate (huggingface-cli login or HF_TOKEN) before downloading:
export OPENFOLD_CACHE=~/.openfold3
huggingface-cli download OpenFold/OpenFold3 checkpoints/of3-p2-155k.pt \
--local-dir "$OPENFOLD_CACHE"
run_openfold will also auto-download to $OPENFOLD_CACHE on first run if
egress is open and HF credentials are available (either HF_TOKEN or a prior
huggingface-cli login) with repo access granted. The interactive
setup_openfold helper exists but prompts on stdin; prefer the explicit
download above for non-interactive runs.
export OPENFOLD_CACHE=/path/to/cache
run_openfold predict \
--query_json=queries.json \
--output-dir out/ \
--use-msa-server false \
--use-templates false
run_openfold discovers the checkpoint under $OPENFOLD_CACHE automatically.
Only pass --inference-ckpt-path <file.pt> if you have a non-standard layout
or multiple checkpoints and need to pin one explicitly.
For MSA + templates (slower, higher accuracy), drop the two false flags. The
MSA server is api.colabfold.com; template chain-ID remap hits
data.rcsb.org (GraphQL) — both must be reachable.
OpenFold3 does not read FASTA. Queries are a JSON object validated by
InferenceQuerySet (pydantic, extra: forbid — unknown keys reject):
{
"queries": {
"my_complex": {
"chains": [
{ "molecule_type": "protein", "chain_ids": ["A"], "sequence": "MQIFVK…" },
{ "molecule_type": "protein", "chain_ids": ["B", "C"], "sequence": "MVLSPA…" },
{ "molecule_type": "ligand", "chain_ids": ["L"], "smiles": "CC(=O)Oc1ccccc1C(=O)O" }
],
"use_msas": true
}
},
"seeds": [42]
}
molecule_type | required field |
|---|---|
protein / dna / rna | sequence |
ligand | smiles or ccd_codes: ["HEM"] |
chain_ids is a list — repeat the same sequence across multiple chain IDs
for homo-oligomers. Per-chain paired_msa_file_paths / main_msa_file_paths
let you supply your own a3m instead of the server.
| Flag | Default | Description |
|---|---|---|
--num-diffusion-samples | 5 | Structures per (query, seed) |
--num-model-seeds | 1 | Number of model seeds per query (multiplies output count alongside JSON seeds and diffusion samples) |
--use-msa-server | true | ColabFold MMseqs2 server for MSA |
--use-templates | true | ColabFold template search + RCSB remap |
--inference-ckpt-path | auto-discovered under $OPENFOLD_CACHE | Override only — for non-standard layouts or to pin a specific checkpoint file |
out/
├── summary.txt
├── model_config.json / experiment_config.json
├── inference_query_set.json
└── <query_name>/seed_<N>/
├── <query>_seed_<N>_sample_<k>_model.cif
├── <query>_seed_<N>_sample_<k>_confidences.json # full PAE/pLDDT
├── <query>_seed_<N>_sample_<k>_confidences_aggregated.json
└── timing.json
*_confidences_aggregated.json is the small one to read first:
{
"avg_plddt": 78.96,
"ptm": 0.667,
"iptm": 0.0,
"gpde": 0.73,
"has_clash": 0.0,
"sample_ranking_score": 0.133,
"chain_ptm": { "A": 0.667 },
"chain_pair_iptm": {}
}
summary.txt shows Successful Queries: N matching your input counthas_clash: 0.0.cif ~50-150 KB per sample for a small proteingrep -E 'Successful|Failed' out/summary.txt
find out -name '*_model.cif' | wc -l # = queries x json_seeds x num-model-seeds x num-diffusion-samples
| Error | Cause | Fix |
|---|---|---|
_deepspeed_evo_attn requires that DeepSpeed be installed | default eval kernel is DS4Sci on CUDA | install deepspeed (needs nvcc + CUTLASS), or in model_config.py eval block set use_deepspeed_evo_attention: False + use_cueq_triangle_kernels: True (cuEq path; no build) |
CUTLASS_PATH ... not set ... cutlass_library is not installed | cuEq path still needs the python cutlass_library shim | pip install nvidia-cutlass |
libXrender.so.1: cannot open shared object file | rdkit (via pdbeccdutils) needs X11 render libs | apt-get install libxrender1 libxext6 libsm6 |
ModuleNotFoundError: boto3 (or awscrt) | openfold3.core.data.io.s3 is eager-imported even when weights are local | pip install boto3 awscrt |
ValidationError: queries / Field required or Input should be an object | wrong JSON shape | top-level is {"queries": {"<name>": {...}}} (a dict, not a list) |
ValidationError ... settings / Extra inputs are not permitted | tried to override model config via --runner-yaml | --runner-yaml is InferenceExperimentConfig only; kernel/memory settings live in model_config.py |
Failed to fetch chain ID mappings from RCSB for N entries | data.rcsb.org unreachable (allowlist/offline) | run with --use-templates false, or open egress to data.rcsb.org |
CUDA out of memory | large complex / many samples | reduce --num-diffusion-samples; the low_mem preset (model_setting_presets.yml) offloads more aggressively |