Finance Analysis Skill

---
name: finance-analysis-skill
description: "Personal finance memory layer. Parse statements, store transactions, query spending."
category: "Finance & Crypto"
author: community
version: "0.1.2"
icon: coins
---

# Finance Skill

Personal finance memory layer. Parse statements, store transactions, query spending.

## Data Location
- Transactions: `~/.openclaw/workspace/finance/transactions.json`
- Raw statements: `~/.openclaw/workspace/finance/statements/`

*Storage convention: OpenClaw workspace (`~/.openclaw/workspace/`) is the standard location for persistent user data. This matches where session-memory and other hooks store agent data. Credentials/config would go in `~/.config/finance/` if needed.*

## Tools

### 1. Parse Statement
When user shares a statement (image or PDF):

**⚠️ IMPORTANT: Telegram/channel previews truncate PDFs!**
Always extract with pypdf first to get ALL pages:

```bash
python3 -c "
import pypdf
reader = pypdf.PdfReader('/path/to/statement.pdf')
for i, page in enumerate(reader.pages):
    print(f'=== PAGE {i+1} ===')
    print(page.extract_text())
"
```

Then parse the full text output:
1. Extract transactions from ALL pages
2. Return JSON array: `[{date, merchant, amount, category}, ...]`
3. Run `scripts/add-transactions.sh` to append to store
4. **Verify total matches statement** (sum of expenses should equal "Total purchases")

**Extraction format:**
```
Each transaction: {"date": "YYYY-MM-DD", "merchant": "name", "amount": -XX.XX, "category": "food|transport|shopping|bills|entertainment|health|travel|other"}
Negative = expense, positive = income/refund.
```

**Categories:**
- food: restaurants, groceries, coffee, fast food
- transport: Waymo, Uber, gas, public transit
- shopping: retail, online purchases
- bills: utilities, subscriptions
- entertainment: movies, concerts, theme parks
- health: pharmacy, doctors
- travel: hotels, flights

### 2. Query Transactions
User asks about spending → read transactions.json → filter/aggregate → answer

**Example queries:**
- "How much did I spend last month?" → sum all negative amounts in date range
- "What did I spend on food?" → filter by category
- "Show my biggest expenses" → sort by amount

### 3. Add Manual Transaction
User says "I spent $X at Y" → append to transactions.json

## File Format

```json
{
  "transactions": [
    {
      "id": "uuid",
      "date": "2026-02-01",
      "merchant": "Whole Foods",
      "amount": -87.32,
      "category": "food",
      "source": "statement-2026-01.pdf",
      "added": "2026-02-09T19:48:00Z"
    }
  ],
  "accounts": [
    {
      "id": "uuid",
      "name": "Coinbase Card",
      "type": "credit",
      "lastUpdated": "2026-02-09T19:48:00Z"
    }
  ]
}
```

## Usage Flow

1. User: *shares statement image*
2. Agent: extracts transactions via vision, confirms count
3. Agent: runs add script to store
4. User: "how much did I spend on food?"
5. Agent: reads store, filters, answers

## Dependencies
- `jq` — for JSON transaction storage and querying (`apt install jq` / `brew install jq`)
- `pypdf` — for full PDF text extraction (`pip3 install pypdf`)

## Lessons Learned
- **Telegram truncates PDF previews** — always use pypdf to get all pages
- **Verify totals** — sum extracted expenses and compare to statement total before importing
- **Coinbase Card** — no Plaid support, statement upload only

## Future: Plaid Integration
- Add `finance_connect` tool for Plaid OAuth flow
- Auto-sync transactions from connected banks
- Same query interface, different data source