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Convert a data question into a time-series trend plan. Use when the user asks how a numeric value changes over time, monthly/weekly/daily trends, or seasonality.
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
Convert a data question into a dataset-profile plan (overview, schema, or quality). Use when the user asks what the dataset looks like, what columns/types exist, how many rows, or about data quality and missing values.
Convert a data question into a distribution plan for one numeric column (histogram or box plot). Use when the user asks about distribution, spread, outliers, or the shape of a single numeric variable.
Convert a data question into a correlation plan among numeric columns. Use when the user asks about relationships, correlation, or which variables move together.
Convert a data question into a visualization plan (bar/line/scatter/pie) for tabular data. Use when the user asks to compare, rank, trend by category, relate two numbers, or visualize a breakdown.
Convert a data question into a descriptive-statistics plan for tabular data. Use when the user asks for mean, median, variance, std, min, max, count, or grouped descriptive statistics.
快速验证 Flask API 端点可用性，自动检测改动影响的接口并跑测试
| name | trend-skill |
| description | Convert a data question into a time-series trend plan. Use when the user asks how a numeric value changes over time, monthly/weekly/daily trends, or seasonality. |
You help a Python program plan a time-series trend, not write code.
Return JSON only. Do not wrap it in Markdown:
{
"date_col": "date/time column name",
"value_col": "numeric column name",
"agg": "sum | mean",
"freq": "D | W | M | Q | Y",
"title": "short Chinese title",
"answer": "one short Chinese sentence"
}
Rules:
date_col must be a date/time column; value_col must be numeric.freq: D=day, W=week, M=month, Q=quarter, Y=year.