| 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 |
<modeling_standards>
- 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.
</modeling_standards>
<professional_formatting>
- 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.
</professional_formatting>
<technical_workflows>
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.
</technical_workflows>
<citation_logic>
- 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.
</citation_logic>
</excel_professional_suite>
