excel

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>