dynamic-percentage-and-large-file-analysis
根据文件行数动态切换大文件处理策略（Parquet转换），通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量，最终输出结构化Excel报告及可视化图表。
npx skills add OpenSenseNova/SenseNova-Skills --skill percentage-calculation --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 在数据中动态定位关键字段，通过逐行扫描匹配关键词提取数值，并进行条件筛选与占比计算。 ```python key_values = {} target_col = None value_col = 'target_value_col' # 动态查找目标分类列 for col in df_analysis.columns: if 'keyword1' in col.lower() or 'keyword2' in col.lower(): target_col = col break # 通用字段查找逻辑：逐行扫描匹配关键词并提取首个正数 for idx, row in df_analysis.iterrows(): row_str = str(row.values) if '指标A' in row_str and '指标A' not in key_values: for val in row.values: if isinstance(val, (int, float)) and val > 0: key_values['指标A'] = val break if '指标B' in row_str and '指标B' not in key_values: for val in row.values: if isinstance(val, (int, float)) and val > 0: key_values['指标B'] = val break # 条件筛选与统计 if target_col and '特定类别' in df_analysis[target_col].unique(): df_filtered = df_analysis[df_analysis[target_col] == '特定类别'] if value_col in df_filtered.columns: df_filtered[value_col] = pd.to_numeric(df_filtered[value_col], errors='coerce') avg_val = df_filtered[value_col].mean() print(f"特定类别平均值 = {avg_val:.2f}") # 计算占比 if '指标A' in key_values and '指标B' in key_values: percentage = (key_values['指
- Skill Steps
What does the dynamic-percentage-and-large-file-analysis skill do?
根据文件行数动态切换大文件处理策略（Parquet转换），通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量，最终输出结构化Excel报告及可视化图表。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill percentage-calculation --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 4,855 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.