Use this when reading, writing, moving, copying, modifying, deleting, or generating data files — including any operation that touches data/raw, data/external, data/interim, data/processed, or outputs directories.
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Use this skill before reading, writing, moving, modifying, deleting, or generating data files.
data/raw/ and data/external/ as immutable.data/interim/, data/processed/, or outputs/.data/raw/ or data/external/.data/interim/, data/processed/, or outputs/).| Directory | Role | Mutable? |
|---|---|---|
data/raw/ | Original source data | No |
data/external/ | Third-party reference data | No |
data/interim/ | Intermediate transforms | Yes |
data/processed/ | Final cleaned/derived data | Yes |
outputs/ | Figures, tables, reports | Yes |
data/raw/ (gitignored) and never commit.data/processed/ or outputs/..env is gitignored. .env.example lists key names only — never real values.python-dotenv.data/raw/ and data/external/ are gitignored, and CI enforces the rules above:
uv run python scripts/check_no_raw_data_commit.py # rawデータのコミットを検知
uv run python scripts/check_no_sensitive_patterns.py # 秘密情報のパターンを検知
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Use this when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.
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因果推論・効果検証（A/B テスト / ABテスト / オンライン実験 / リフト / SRM / CUPED、観察データの処置効果 / ATE / ATT / CATE / 傾向スコア / propensity score / IPW / 二重頑健 / DML / causal forest、準実験 / 差の差 / DiD / イベントスタディ / 操作変数 / IV / 2SLS / 回帰不連続 / RDD / 合成コントロール）を実行したら必ずセットで出す図と値のルーター。 proportions_ztest, chisquare, statsmodels.stats.power, dowhy, econml, causalml, doubleml, propensity, LinearDML, IV2SLS, rdrobust, rddensity, pysyncon, treat*post がコードに現れたとき、またはユーザーが「ABテストの結果を 見て」「施策の効果を推定して」「差の差で」「傾向スコアで揃えて」「閾値前後で比較して」と言ったときに使う。SRM・ バランス・並行トレンド・first-stage に言及がなくても適用する。SKILL.md のルーティング表で設計を特定し、対応する references/<設計>.md を読んでから実行する。実験でない群間比較の検定は statistical-inference-diagnostics、 予測モデルの解釈（SHAP 等）は predictive-modeling-diagnostics を使う。
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Use this when performing DataFrame operations — including loading, filtering, joining, aggregating, transforming, or reshaping tabular data with polars or pandas.
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Use this when creating, editing, executing, or reviewing Jupyter notebooks — including cell structure, kernel management, extracting reusable logic to src/, and ensuring notebooks are restartable.
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Use this when reading from or writing to local files — including constructing file paths with pathlib, creating directories, choosing output locations, and using path utilities from src/analysis_project/paths.py.
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分析を始める前に構造化された分析計画を作成する
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