Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use this skill for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection. Covers the local AlphaFold 3 inference package, its JSON dialect, run_alphafold workflow, outputs, troubleshooting, and safe helper scripts.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this repo skill when a user asks an agent to work with AlphaFold 3 inference workflows: preparing fold-input JSON, planning or debugging prediction runs, interpreting output directories, or writing safe Python tooling around the package.
AlphaFold 3 full prediction is operationally heavy. It requires Linux, model parameters obtained under the AlphaFold 3 terms, genetic databases, HMMER tools, compatible JAX/CUDA runtime, and an NVIDIA GPU for inference. Do not run full inference, download databases, mount disks, or fetch model weights unless the user explicitly asks and the environment is ready.
sub-skills/input-preparation/ when constructing, validating, converting, or troubleshooting AlphaFold 3 input JSON.sub-skills/running-predictions/ when planning Docker/local commands, split data-pipeline/inference runs, database/model paths, GPU flags, HMMER tools, or runtime preflight checks.sub-skills/output-interpretation/ when inspecting result directories, ranking predictions, explaining confidence JSONs, embeddings, distograms, compression, or missing output files.sub-skills/python-apis/ when coding against AlphaFold 3 internals such as folding_input.Input, DataPipelineConfig, make_model_config, process_fold_input, ModelRunner, or structure/mmCIF utilities.For a local Python installation, start with a safe import/resource check rather than a prediction run:
python scripts/check_install.py
If the package imports fail, read references/troubleshooting.md. If the input JSON is the suspected problem, use sub-skills/input-preparation/scripts/validate_fold_input.py. If runtime paths or binaries are suspected, use sub-skills/running-predictions/scripts/check_runtime_requirements.py.
| User asks for | Read first | Useful bundled helper |
|---|---|---|
| “Create an AF3 JSON for a protein-ligand complex” | sub-skills/input-preparation/SKILL.md | sub-skills/input-preparation/scripts/validate_fold_input.py |
| “Convert/check AlphaFold Server JSON” | sub-skills/input-preparation/references/input-json.md | sub-skills/input-preparation/scripts/validate_fold_input.py |
| “Run only the data pipeline on CPU” | sub-skills/running-predictions/SKILL.md | sub-skills/running-predictions/scripts/build_run_command.py |
| “Debug missing HMMER/database/model paths” | sub-skills/running-predictions/references/troubleshooting.md | sub-skills/running-predictions/scripts/check_runtime_requirements.py |
| “Explain ranking_scores.csv or ipTM/pLDDT/PAE” | sub-skills/output-interpretation/SKILL.md | sub-skills/output-interpretation/scripts/summarize_outputs.py |
| “Use AlphaFold3 APIs in Python” | sub-skills/python-apis/SKILL.md | sub-skills/python-apis/scripts/inspect_alphafold3_api.py |
alphafold3.3.0.3.>=3.12.run_alphafold.py in source distributions or containers.build_data, used to generate internal CCD pickle resources from packaged components.cif data when needed.fetch_databases.sh, SSD mount/copy helpers, Docker build, and full run_alphafold.py runs as user-approved operational actions, not default validation.references/capability-map.md for the coverage map and ownership boundaries.references/troubleshooting.md for cross-cutting install/import/resource/runtime problems.references/repo-provenance.md to decide whether this skill may be stale against a changed AlphaFold 3 checkout.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.