large-file-kpi-analysis
根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。
npx skills add OpenSenseNova/SenseNova-Skills --skill kpi-metric-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Skill Steps > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 提取关键指标，进行物理量/指标的单位一致性验证计算，并对核心业务指标进行降序排列。 ```python # 1. 物理量/指标单位一致性验证与计算 (保留公式结构示例) col_numerator = 'numerator_col' # 示例：Mx (kN·m) col_denominator = 'denominator_col' # 示例：Wx (cm³) col_target = 'target_col' # 示例：sigma (MPa) if col_numerator in data.columns and col_denominator in data.columns and col_target in data.columns: # 单位换算示例：统一到标准单位后计算 data['den_converted'] = data[col_denominator] * 1e-6 data['num_converted'] = data[col_numerator] * 1e3 data['calc_result_pa'] = data['num_converted'] / data['den_converted'] data['calc_result_mpa'] = data['calc_result_pa'] / 1e6 # 容差验证 tolerance = 1e-6 data['is_valid'] = abs(data['calc_result_mpa'] - data[col_target]) < tolerance print("单位一致性验证通过率:", data['is_valid'].mean() * 100, "%") # 2. 提取关键指标并降序排列 group_col = 'group_col' # 示例：开发区名称 metric_col = 'metric_col' # 示例：实际到帐外资额 result_df = pd.DataFrame() if group_col in data.columns and metric_col in data.columns: result_df = data[[group_col, metric_col]].copy() result_df = result_df.sort_values(metric_col, ascending=False).reset_index(dr
- Skill Steps
What does the large-file-kpi-analysis skill do?
根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill kpi-metric-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,911 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.