Bank Reconciliation Skill

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name: bank-recon

description: Perform bank reconciliation between bank statements and general ledger files. Supports bank statement PDF ingestion, conversion of PDF statements into structured Excel data, custom amount thresholds, ID/key matching, and semantic description matching. Use when the user wants to read a bank statement PDF or Excel file, convert statement activity into a workbook, reconcile bank activity to GL transactions, identify matched and unmatched items, and generate an Excel workbook with reconciliation results, a summary tab, and separate unreconciled-bank and unreconciled-GL tabs.

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# Bank Reconciliation Skill

Reconcile bank statement rows against GL rows and produce an `.xlsx` workbook that is immediately reviewable by an accountant.

Workflow

1. Identify the bank statement path and GL workbook path.

2. Accept either a bank statement `.xlsx` file or a bank statement `.pdf` file.

3. If the bank statement is a PDF, run the workflow so it first extracts the bank statement lines into a structured workbook, then reconciles that extracted workbook to the GL.

4. Confirm the reconciliation threshold. Default to `0.00` unless the user asks for a tolerance.

5. Run `scripts/recon_logic.py` with the bank file, GL file, output file, and threshold.

6. Return the generated workbook and summarize:

- matched bank row count

- matched GL row count

- unreconciled bank row count

- unreconciled GL row count

5. If the user asks for follow-up analysis, use the `Summary`, `Unreconciled Bank`, and `Unreconciled GL` tabs first.

Output Workbook

The generated workbook should contain these tabs:

Command

python3 scripts/recon_logic.py <bank_xlsx_or_pdf> <gl_xlsx> <output_xlsx> [threshold]

When the bank input is a PDF, the script also creates a companion extracted workbook beside the PDF (same basename with `_extracted.xlsx`) before running reconciliation.

Matching Logic

Use a layered approach:

1. Preserve the original signs from both source files in the output.

2. Compare bank and GL amounts using absolute values for matching so bank polarity and accounting debit/credit polarity can reconcile without rewriting displayed source amounts.

3. Match by shared extracted keys such as batch IDs, invoice IDs, vendor IDs, customer IDs, and tax/payment references.

4. Allow one-to-one, one-to-many, many-to-one, and grouped many-to-many matches when totals fall within threshold.

5. For remaining items, use semantic name grouping plus summed-amount comparison.

6. Preserve unmatched rows in dedicated tabs instead of dropping them from the deliverable.

Notes