Agent skill · business finance · anthropics
datapack-builder
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.
Why this skill is useful
Provides domain-specific formatting rules and data normalization procedures for financial data that the AI wouldn't reliably generate on its own.
What it needs
About 11k tokens when loaded. Last updated 2026-08-04. 34,059 stars on the source repository.
What this skill does
Financial Data Pack Builder Build professional, standardized financial data packs for private equity, investment banking, and asset management. Transform financial data from CIMs, offering memorandums, SEC filings, web search, or MCP server access into polished Excel workbooks ready for investment committee review. Important: Use the xlsx skill for all Excel file creation and manipulation throughout this workflow. CRITICAL SUCCESS FACTORS Every data pack must achieve these standards. Failure on any point makes the deliverable unusable. 1. Data Accuracy (Zero Tolerance for Errors) Trace every number to source document with page reference Use formula-based calculations exclusively (no hardcoded values) Cross-check subtotals and totals for internal consistency Verify balance sheet balances: Assets = Liabilities + Equity Confirm cash flow ties to balance sheet changes 2. ESSENTIAL RULES RULE 1: Financial data (measuring money) → Currency format with $ Triggers: Revenue, Sales, Income, EBITDA, Profit, Loss, Cost, Expense, Cash, Debt, Assets, Liabilities, Equity, Capex Format: $#,##0.0 for millions, $#,##0 for thousands Negatives: $(123.0) NOT -$123 RULE 2: Operational data (counting things) → Number format, NO $ Triggers: Units, Stores, Locations, Employees, Customers, Square Feet, Properties, Headcount Format: #,##0 with commas Negatives: (123) consistent with rest of table RULE 3: Percentages (rates and ratios) → Percentage format Triggers: Margin, Growth, Rate, Percentage, Yield, Return, Utilization, Occupancy Format: 0.0% for one decimal place Display: 15.0% NOT 0.15 RULE 4: Years → Text format to prevent comma insertion Format: Text or custom to prevent 2,024 Display: 2020, 2021, 2022, 2023A, 2024E RULE 5: When context is mixed, each metric gets its own appropriate format Example: Revenue and per-store metrics use $, Store count uses number format. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/datapack-builder