Financial Ops

Financial Spreadsheet Operations

description: Two capabilities in one skill — audit spreadsheets for formula accuracy and model integrity, and clean messy data for analysis. Covers formula-level checks (errors, hardcodes, inconsistencies), full financial model audits (BS balance, cash tie-out, logic sanity), and data cleanup (whitespace, casing, duplicates, type coercion, date normalization). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "model check", "model won't balance", "clean this data", "normalize this data", "fix formatting", "dedupe", "this data is messy".

Environment

• Standalone .xlsx file: Use Python/openpyxl. Read cell values and formulas programmatically.
• If asked to generate a cleaned/audited copy: Write to a new file, never overwrite the original without explicit permission.

No paid MCP dependencies. All operations use openpyxl or pandas — nothing that requires a subscription or external API.

Part A: Spreadsheet Audit

Audit formulas and data for accuracy and mistakes. Scope determines depth.

Step 1: Determine Scope

If the user already gave a scope, use it. Otherwise ask:

What scope do you want me to audit?

• selection — just a specific range (e.g. A1:F200)
• sheet — one sheet only
• model — the whole workbook, including financial-model integrity checks (BS balance, cash tie-out, roll-forwards, logic sanity)

The model scope is the deepest — use it for DCF, LBO, 3-statement, merger, comps, or any integrated financial model.

Step 2: Formula-Level Checks (ALL scopes)

Step 3: Model-Integrity Checks (MODEL scope only)

If scope is model, identify the model type (DCF / LBO / 3-statement / merger / comps / custom) and run the appropriate integrity checks.

3a. Structural Review

3b. Balance Sheet

If BS doesn't balance, quantify the gap per period and trace where it breaks — nothing else matters until this is fixed.

3c. Cash Flow Statement

3d. Income Statement

3e. Circular References

• Interest → debt balance → cash → interest is a common intentional circ in LBO/3-stmt models
• If intentional: verify iteration toggle exists and works
• If unintentional: trace the loop and flag how to break it

3f. Logic & Reasonableness

3g. Model-Type-Specific Bugs

DCF:

• Discount rate applied to wrong period (mid-year vs end-of-year)
• Terminal value not discounted back
• WACC uses book values instead of market values
• FCF includes interest expense (should be unlevered)
• Tax shield double-counted

LBO:

• Debt paydown doesn't match cash sweep mechanics
• PIK interest not accruing to principal
• Exit multiple applied to wrong EBITDA (LTM vs NTM)
• Fees/expenses not deducted from Day 1 equity

Merger:

• Accretion/dilution uses wrong share count (pre- vs post-deal)
• Synergies not phased in
• Purchase price allocation doesn't balance
• Transaction fees not in sources & uses

3-Statement:

• Working capital changes have wrong sign
• Depreciation doesn't match PP&E schedule
• Debt maturity schedule doesn't match principal payments
• Dividends exceed net income without explanation

Step 4: Audit Report

Output a findings table:

Severity:

• Critical — wrong output (BS doesn't balance, formula broken, cash doesn't tie)
• Warning — risky (hardcodes, inconsistent formulas, edge-case failures)
• Info — style/best-practice (color coding, layout, naming)

For model scope, prepend a summary line:

Model type: [DCF/LBO/3-stmt/...] — Overall: [Clean / Minor Issues / Major Issues] — [N] critical, [N] warnings, [N] info

Don't change anything without asking — report first, fix on request.

Part B: Data Cleanup

Clean messy data in a sheet or specified range.

Step 1: Scope

• If a range is given (e.g. A1:F200), use it
• Otherwise use the full used range of the active sheet
• Profile each column: detect its dominant type (text / number / date) and identify outliers

Step 2: Detect Issues

Step 3: Propose Fixes

Show a summary table before changing anything:

Step 4: Apply

• Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.
• Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
• For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
• After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
• Report a before/after summary of what changed

AU-Specific Data Patterns

When working with Australian financial data, watch for:

Notes

• BS balance first — if it doesn't balance, everything downstream is suspect
• Hardcoded overrides are the #1 source of silent bugs — search aggressively
• Sign convention errors (positive vs negative for cash outflows) are extremely common
• If the model uses VBA macros, note any macro-driven calculations that can't be audited from formulas alone
• Always create a backup before applying any data cleanup changes