安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：可疑

OpenClaw 评估

The skill's stated purpose (local transaction analysis and local HTML reports) is plausible and largely self-consistent, but there are mismatches — notably CLI examples that reference a python module that is not included and no declared runtime/binary requirements — so you should verify where the actual code runs before trusting it with real bank data.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Watch My Money」。简介：Analyze bank transactions, categorize spending, track monthly budgets, detect o…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/andreolf/watch-my-money/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: watch-my-money
description: Analyze bank transactions, categorize spending, track monthly budgets, detect overspending and anomalies. Outputs interactive HTML report.
triggers:
  - "track spending"
  - "check my budget"
  - "analyze transactions"
  - "what did I spend on"
  - "am I overspending"
  - "budget tracker"
  - "spending analysis"
  - "monthly expenses"
formats:
  - CSV bank exports
  - Text transaction lists
outputs:
  - Interactive HTML report
  - JSON data export
  - Console summary
privacy: local-only
---

# watch-my-money

Analyze transactions, categorize spending, track budgets, flag overspending.

## Workflow

### 1. Get Transactions

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"

### 2. Parse & Normalize

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)

### 3. Categorize Transactions

For each transaction, assign category:

**Categories:**
- rent, utilities, subscriptions, groceries, eating_out
- transport, travel, shopping, health
- income, transfers, other

Categorization order:
1. Check saved merchant overrides
2. Apply deterministic keyword rules (see [common-merchants.md](references/common-merchants.md))
3. Pattern matching (subscriptions, utilities)
4. Heuristic fallback

For ambiguous merchants (batch of 5-10), ask user to confirm.
Save overrides for future runs.

### 4. Check Budgets

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](references/budget-templates.md) for suggested budgets.

### 5. Detect Anomalies

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).

### 6. Generate HTML Report

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](assets/template.html) and inject data.

### 7. Save State

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

## CLI Commands

```bash
# 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
```

## Output Structure

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`

## HTML Report Features

- 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

## Privacy

All data stays local. No network calls. No external APIs.
Transaction data is analyzed locally and stored only in `~/.watch_my_money/`.