openfold3
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.
Profile →npx skills add xuzhougeng/wisp-science --skill openfold3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# OpenFold3 Structure Prediction ## Prerequisites | Requirement | Minimum | Recommended | | ----------- | ------- | ----------- | | Python | 3.10+ | 3.11 | | CUDA | 12.1+ | 12.4+ | | GPU VRAM | 24GB | 80GB (H100) | | RAM | 32GB | 64GB | | Disk (weights) | 3GB | - | ## How to run ### Installation ```bash 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. ### Weights 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: ```bash 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
- Prerequisites
- How to run
- Installation
- Weights
- Running
- Query JSON format
- Key parameters
- Wisp execution
- Output format
- What good output looks like
- Verify
- Troubleshooting
pip install 'openfold3[cuequivariance]==0.4.1' export OPENFOLD_CACHE=~/.openfold3 huggingface-cli download OpenFold/OpenFold3 checkpoints/of3-p2-155k.pt \ export OPENFOLD_CACHE=/path/to/cache run_openfold predict \ grep -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
What does the openfold3 skill do?
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.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill openfold3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.