Made by@ledgerlyExpense categorizer
@ledgerly/expense-categorizer
Categorize a bank or card CSV export with a rules file you can edit. Writes a categorized CSV, spend by category and by month, and a review list of unknown merchants with suggested rules. Offline and repeatable.
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- 1{
- 2 "name": "expense-categorizer",
- 3 "owner": "ledgerly",
- 4 "version": "1.0.0",
- 5 "type": "skill",
- 6 "description": "Categorize a bank or card CSV export with a rules file you can edit. Writes a categorized CSV, spend by category and by month, and a review list of unknown merchants with suggested rules. Offline and repeatable.",
- 7 "category": "finance",
- 8 "license": "MIT",
- 9 "compat": ["claude-code"],
- 10 "permissions": {
- 11 "shell": ["node scripts/categorize.mjs"],
- 12 "files": { "read": ["./data/**"], "write": ["./data/expense-rules.csv", "./out/expenses*"] }
- 13 },
- 14 "tests": {
- 15 "cases": [
- 16 {
- 17 "name": "categorizes the example bank export and lists unknowns",
- 18 "inputs": {},
- 19 "checks": [
- 20 { "type": "command_exits_zero", "command": "node scripts/categorize.mjs examples/transactions.csv --rules examples/rules.csv --out out/expenses" },
- 21 { "type": "file_exists", "path": "out/expenses-categorized.csv" },
- 22 { "type": "file_contains", "path": "out/expenses-summary.md", "text": "| Payroll | $246,701 | 85.5% |" },
- 23 { "type": "file_contains", "path": "out/expenses-unknowns.csv", "text": "BLUE BOTTLE COFFEE" },
- 24 {
- 25 "type": "output_matches_schema",
- 26 "path": "out/expenses-summary.json",
- 27 "schema": {
- 28 "type": "object",
- 29 "required": ["transactions", "by_category", "unknown_merchants"],
- 30 "properties": {
- 31 "transactions": { "const": 38 },
- 32 "spend_sign": { "const": "negative" },
- 33 "outflow_total": { "const": 288702.99 },
- 34 "unknown_merchants": { "const": 4 },
- 35 "by_category": { "type": "object", "properties": { "Payroll": { "const": 246701.16 }, "Travel": { "const": 722.5 } } }
- 36 }
- 37 }
- 38 }
- 39 ]
- 40 },
- 41 {
- 42 "name": "handles debit and credit columns, regex rules, and first match wins",
- 43 "inputs": {
- 44 "data/card.csv": "Posted Date,Payee,Debit,Credit\n10/02/2026,FIGMA ANNUAL,900.00,\n10/03/2026,Payment received,,1500.00\n10/04/2026,FIGJAM WORKSHOP,50.00,\n",
- 45 "data/rules.csv": "pattern,category,match\nfigma annual,Software (annual),contains\n^fig,Design,regex\n"
- 46 },
- 47 "checks": [
- 48 { "type": "command_exits_zero", "command": "node scripts/categorize.mjs data/card.csv --rules data/rules.csv --out out/expenses" },
- 49 {
- 50 "type": "output_matches_schema",
- 51 "path": "out/expenses-summary.json",
- 52 "schema": {
- 53 "type": "object",
- 54 "properties": {
- 55 "outflow_total": { "const": 950 },
- 56 "inflow_total": { "const": 1500 },
- 57 "by_category": { "type": "object", "properties": { "Software (annual)": { "const": 900 }, "Design": { "const": 50 } } },
- 58 "unknown_merchants": { "const": 1 }
- 59 }
- 60 }
- 61 }
- 62 ]
- 63 }
- 64 ]
- 65 }
- 66}