--- name: excel description: |- Handle spreadsheet operations (Excel/CSV) with high-fidelity modeling, financial analysis, and visual verification. Use for budget models, data dashboards, and complex formula-heavy sheets. Use proactively when zero formula errors and professional standards are required. Examples: - user: "Build an LBO model" -> create Excel with banking-standard formatting - user: "Analyze this data and create a dashboard" -> use openpyxl + artifact_tool - user: "Verify formulas in this spreadsheet" -> run recalc.py to check for errors --- - **Zero Formula Errors**: Models MUST have zero #REF!, #DIV/0!, or #VALUE! errors. - **Dynamic Logic**: You MUST NOT hardcode derived values. You MUST use Excel formulas for all calculations. - **Assumptions**: You MUST place all inputs in dedicated assumption cells. - **Standards**: Specify units in headers ("Revenue ($mm)"). Format zeros as "-". - **Color Coding**: The agent SHOULD follow the project's `branding` skill for color choices. If not defined, the agent SHOULD default to professional standards (e.g., Blue for hardcoded inputs, Black for formulas). - **Visuals**: You SHOULD use `artifact_tool` to render sheets and verify layout. **Reference**: `references/artifact_tool_spreadsheets_api.md`. ### 1. Data Analysis (Pandas) - You SHOULD use **Pandas** for heavy lifting and aggregation. - You SHOULD convert to **Openpyxl** for final professional formatting and formula insertion. ### 2. Verification Loop (MANDATORY) Before delivery, you MUST run the audit script: - `python scripts/recalc.py output.xlsx` - You MUST fix all errors identified in the resulting JSON summary. - **Citations**: You SHOULD cite sources for hardcoded data in cell comments. - **Best Practices**: See `references/spreadsheet.md` for guidance on cross-sheet references and complex formula construction.