メニュー
Use when an ML result must be reproducible — fixing seeds, pinning environments, versioning data, and structuring projects so runs can be exactly recreated. Covers determinism gotchas across NumPy, PyTorch, and CUDA.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when preparing raw data for modeling — handling missing values, duplicates, inconsistent types, outliers, and bad categorical values. Emphasizes fitting all imputation on train-only to avoid leakage.
Use when running ML experiments that need to be compared, reproduced, or shared. Covers MLflow/Weights & Biases logging, what to track, run organization, and model registry basics.
Use when starting on a new dataset, before modeling, or when asked to "explore", "profile", "understand", or "summarize" data. Covers structured EDA, distributions, correlations, target leakage checks, and visualization.
Use when creating, encoding, scaling, or selecting features for ML models. Covers categorical encoding, numeric transforms, datetime/text/aggregation features, and leakage-safe target encoding.
Use when optimizing model hyperparameters. Covers search strategy (random vs Bayesian/Optuna), leakage-safe tuning inside CV, search-space design, early stopping, and budget management.
Use when the target is rare (fraud, churn, disease, anomalies). Covers correct metrics, resampling (SMOTE/undersampling), class weights, threshold tuning, and avoiding the accuracy trap and resampling leakage.
| name | reproducible-ml |
| description | Use when an ML result must be reproducible — fixing seeds, pinning environments, versioning data, and structuring projects so runs can be exactly recreated. Covers determinism gotchas across NumPy, PyTorch, and CUDA. |
Reproducibility means: same code + same data + same config → same result. It is a prerequisite for trusting comparisons, debugging regressions, and shipping. Three pillars: seed everything, pin everything, version the data.
import os, random, numpy as np, torch
def seed_everything(seed: int = 42):
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False # disables nondeterministic autotuner
For DataLoaders, also set worker_init_fn and a generator so workers are deterministic.
requirements.txt with ==, or uv.lock / poetry.lock / conda env export.sha256) and log the hash with every run.data.csv in place.data_v="2026-06-01").project/
├── data/ # raw (immutable) + processed, both versioned
├── src/ # importable code, no notebooks doing real work
├── configs/ # YAML/Hydra configs, one per experiment
├── scripts/ # entrypoints: train.py, evaluate.py
├── requirements.txt # or uv.lock / poetry.lock (pinned)
└── README.md # exact commands to reproduce
torch.use_deterministic_algorithms(True) and set CUBLAS_WORKSPACE_CONFIG=:4096:8.groupby/apply ordering can vary — sort before reducing.set/dict ordering across processes — set PYTHONHASHSEED.src/.A repo where pip install -r requirements.txt && python scripts/train.py --config configs/exp.yaml recreates the exact result that experiment-tracking logged.