القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.