sn-da-excel-workflow

npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-excel-workflow --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Excel Data Analysis Workflow End-to-end workflow for structured Excel analysis. Each step maps to a capability sub-skill that can be loaded for detailed patterns. ## Workflow ### Step 1 — Count rows across all sheets (lightweight, no full load) Count rows per sheet **without loading data into memory**. Use openpyxl `read_only` mode — this works for any file size. ```python import openpyxl, gc wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True) total_rows = 0 sheet_info = {} for name in wb.sheetnames: ws = wb[name] row_count = sum(1 for _ in ws.iter_rows(min_row=2, values_only=True)) total_rows += row_count sheet_info[name] = row_count print(f"Sheet '{name}': {row_count} rows") wb.close() print(f"总行数={total_rows}") ``` ⚠️ **Do NOT use `pd.read_excel()` to count rows** — it loads all data into memory, which will OOM on large files. → capability: `excel-reading/multi-sheet-reading` ### Step 2 — Large file gate (CRITICAL — choose strategy by row count) | total_rows | Strategy | What to do | |-----------|----------|------------| | < 10k | Direct read | `df = pd.read_excel(file_path, sheet_name=target_sheet)` | | 10k – 100k | Parquet cache | `pd.read_excel()` once →