Skill: chipmunkrpa/bank-recon-skill (version 1.0.2)

--- 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. --- # 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: - `Summary`: threshold, matched counts, unreconciled counts, and basic totals - `Recon Results`: matched groupings with match basis and variance notes - `Unreconciled Bank`: bank rows not matched to the GL - `Unreconciled GL`: GL rows not matched to the bank ## Command ```bash 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 - Read the first worksheet from each input workbook. - Expect simple three-column inputs: date, amount, description/memo. - For text-based bank statement PDFs, the script extracts transaction rows by reading the PDF content streams and reconstructing the transaction table into a workbook. - The PDF path is best for digital statements with selectable text; scanned-image PDFs would still need OCR or a multimodal extraction path. - Keep the workbook generation dependency-light so it can run in minimal Python environments.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)