本文へ移動
cccskills
無料GitHub で公開

alphafold3

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.

インストール方法を見る

含まれるファイル(25)

  • SKILL.md4.7 KB
  • references/capability-map.md2.6 KB
  • references/repo-provenance.md3.3 KB
  • references/repo-routing-metadata.json333 B
  • references/troubleshooting.md4.0 KB
  • scripts/check_install.py2.7 KB
  • sub-skills/input-preparation/references/input-json.md8.9 KB
  • sub-skills/input-preparation/references/troubleshooting.md8.6 KB
  • sub-skills/input-preparation/scripts/validate_fold_input.py3.4 KB
  • sub-skills/input-preparation/SKILL.md2.4 KB
  • sub-skills/output-interpretation/references/confidence-metrics.md5.8 KB
  • sub-skills/output-interpretation/references/output-files.md4.6 KB
  • sub-skills/output-interpretation/references/troubleshooting.md6.0 KB
  • sub-skills/output-interpretation/scripts/summarize_outputs.py8.6 KB
  • sub-skills/output-interpretation/SKILL.md3.0 KB
  • sub-skills/python-apis/references/api-reference.md12.9 KB
  • sub-skills/python-apis/references/troubleshooting.md8.2 KB
  • sub-skills/python-apis/scripts/inspect_alphafold3_api.py6.4 KB
  • sub-skills/python-apis/SKILL.md2.3 KB
  • sub-skills/running-predictions/references/cli-reference.md7.3 KB
  • sub-skills/running-predictions/references/setup-and-performance.md7.9 KB
  • sub-skills/running-predictions/references/troubleshooting.md8.4 KB
  • sub-skills/running-predictions/scripts/build_run_command.py8.0 KB
  • sub-skills/running-predictions/scripts/check_runtime_requirements.py8.0 KB
  • sub-skills/running-predictions/SKILL.md3.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

AlphaFold 3

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.

Quick Route

  • Use sub-skills/input-preparation/ when constructing, validating, converting, or troubleshooting AlphaFold 3 input JSON.
  • Use 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.
  • Use sub-skills/output-interpretation/ when inspecting result directories, ranking predictions, explaining confidence JSONs, embeddings, distograms, compression, or missing output files.
  • Use 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.

First Checks

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.

Common Task Routing

User asks forRead firstUseful bundled helper
“Create an AF3 JSON for a protein-ligand complex”sub-skills/input-preparation/SKILL.mdsub-skills/input-preparation/scripts/validate_fold_input.py
“Convert/check AlphaFold Server JSON”sub-skills/input-preparation/references/input-json.mdsub-skills/input-preparation/scripts/validate_fold_input.py
“Run only the data pipeline on CPU”sub-skills/running-predictions/SKILL.mdsub-skills/running-predictions/scripts/build_run_command.py
“Debug missing HMMER/database/model paths”sub-skills/running-predictions/references/troubleshooting.mdsub-skills/running-predictions/scripts/check_runtime_requirements.py
“Explain ranking_scores.csv or ipTM/pLDDT/PAE”sub-skills/output-interpretation/SKILL.mdsub-skills/output-interpretation/scripts/summarize_outputs.py
“Use AlphaFold3 APIs in Python”sub-skills/python-apis/SKILL.mdsub-skills/python-apis/scripts/inspect_alphafold3_api.py

Repository Facts

  • Distribution/import name: alphafold3.
  • Verified version for this skill baseline: 3.0.3.
  • Python requirement from package metadata: Python >=3.12.
  • Main runner: run_alphafold.py in source distributions or containers.
  • Console entry point: build_data, used to generate internal CCD pickle resources from packaged components.cif data when needed.
  • Package dependencies include JAX, Haiku, RDKit, Tokamax, NumPy, zstandard, and compiled pybind extensions.

Safety Boundaries

  • Do not download full genetic databases automatically; documented full database setup is hundreds of GB.
  • Do not assume model parameters are present or licensed; they must be obtained directly from Google DeepMind under the model parameter terms.
  • Do not run GPU inference as a smoke test unless the user requests it and supplies weights, databases or precomputed features, and output space.
  • Treat fetch_databases.sh, SSD mount/copy helpers, Docker build, and full run_alphafold.py runs as user-approved operational actions, not default validation.
  • Prefer command generation, import checks, JSON validation, and output-tree inspection for safe agent assistance.

References

  • Read references/capability-map.md for the coverage map and ownership boundaries.
  • Read references/troubleshooting.md for cross-cutting install/import/resource/runtime problems.
  • Read references/repo-provenance.md to decide whether this skill may be stale against a changed AlphaFold 3 checkout.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

3ddfa

無料

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

3ddfa-v2

無料

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

ab3dmot

無料

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

acme

無料

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

日本語の概要は準備中です。原文の説明を表示しています。

VectorSpaceLab/AREX-Skill3322026年9月3日 更新

VectorSpaceLab のスキルをすべて見る

このスキルの問題を報告する