Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Analyze bank transactions, categorize spending, track monthly budgets, detect overspending and anomalies. Outputs interactive HTML report.
Analyze bank transactions, categorize spending, track monthly budgets, detect overspending and anomalies. Outputs interactive HTML report.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Analyze transactions, categorize spending, track budgets, flag overspending.
Ask user for bank/card CSV export OR pasted text. Common sources: Download CSV from your bank's online portal Export from budgeting apps Copy/paste transactions from statements Supported formats: Any CSV with date, description, amount columns Pasted text: "2026-01-03 Starbucks -5.40 CHF"
Read input, normalize to standard format: Auto-detect delimiter (comma, semicolon, tab) Parse dates (YYYY-MM-DD, DD/MM/YYYY, MM/DD/YYYY) Normalize amounts (expenses negative, income positive) Extract merchant from description Detect recurring transactions (subscriptions)
For each transaction, assign category: Categories: rent, utilities, subscriptions, groceries, eating_out transport, travel, shopping, health income, transfers, other Categorization order: Check saved merchant overrides Apply deterministic keyword rules (see common-merchants.md) Pattern matching (subscriptions, utilities) Heuristic fallback For ambiguous merchants (batch of 5-10), ask user to confirm. Save overrides for future runs.
Compare spending against user-defined budgets. Alert thresholds: 80% - approaching limit (yellow) 100% - at limit (red) 120% - over budget (red, urgent) See budget-templates.md for suggested budgets.
Flag unusual spending: Category spike: spend > 1.5x baseline AND delta > 50 Subscription growth: subscriptions up > 20% New expensive merchant: first appearance AND spend > 30 Potential subscriptions: recurring same-amount charges Baseline = previous 3 months average (or current month if no history).
Create local HTML file with: Month summary (income, expenses, net) Category breakdown with budget status Top merchants Alerts section Recurring transactions detected Privacy toggle (blur amounts/merchants) Copy template.html and inject data.
Persist to ~/.watch_my_money/: state.json - budgets, merchant overrides, history reports/YYYY-MM.json - machine-readable monthly data reports/YYYY-MM.html - interactive report
# Analyze CSV python -m watch_my_money analyze --csv path/to/file.csv --month 2026-01 # Analyze from stdin cat transactions.txt | python -m watch_my_money analyze --stdin --month 2026-01 --default-currency CHF # Compare months python -m watch_my_money compare --months 2026-01 2025-12 # Set budget python -m watch_my_money set-budget --category groceries --amount 500 --currency CHF # View budgets python -m watch_my_money budgets # Export month data python -m watch_my_money export --month 2026-01 --out summary.json # Reset all state python -m watch_my_money reset-state
Console shows: Month summary with income/expenses/net Category table with spend vs budget Recurring transactions detected Top 5 merchants Alerts as bullet points Files written: ~/.watch_my_money/state.json ~/.watch_my_money/reports/2026-01.json ~/.watch_my_money/reports/2026-01.html
Collapsible category sections Budget progress bars Recurring transaction list Month-over-month comparison Privacy toggle (blur sensitive data) Dark mode (respects system preference) Floating action button Screenshot-friendly layout Auto-hide empty sections
All data stays local. No network calls. No external APIs. Transaction data is analyzed locally and stored only in ~/.watch_my_money/.
Data access, storage, extraction, analysis, reporting, and insight generation.
Largest current source with strong distribution and engagement signals.