Spending Review

1. Time Period Selection

Ask the user what period they want to review:

• "Daily": Review today's spending only
• "Weekly": Review the past 7 days
• "Monthly": Review the current month (or specify: "this month" vs "last month")
• "Custom": Allow user to specify start and end dates

Store the selected period for queries below.

Example conversation:

Bot: "Let's review your spending. What time period would you like to look at?"
User: "Last month"
Bot: "Got it, I'll show you January 1–31 spending."

2. Spending Summary by Category

Use spending_summary(start_date=<period_start>, end_date=<period_end>, group_by="category") to aggregate spending.

Present the results in this format:

# Spending Summary — [Period]

Total Spent: $[amount]

By Category:
- [Category 1]: $[amount] ([% of total]) — [count] transactions
- [Category 2]: $[amount] ([% of total]) — [count] transactions
- [Category 3]: $[amount] ([% of total]) — [count] transactions
  ...

Top Merchant in [Top Category]: [Merchant name] ($[amount])

Analysis questions to ask yourself (do not push on user unless they ask):

• Is the top category expected for this period?
• Are there any new categories that didn't appear before?
• Does the total spending feel aligned with what the user might have guessed?

If the user asks "Is this normal?" or "Did I spend a lot?", try to provide context:

• Use memory_recall(topic="spending patterns") to retrieve baseline expectations
• Compare current period to previous period if available
• Flag unusually high or low totals

3. Top Merchants and Transaction Breakdown

Use list_transactions(start_date=<period_start>, end_date=<period_end>, limit=20) to get top transactions.

Present top merchants by transaction count:

# Top Merchants

1. [Merchant 1] — [count] transactions totaling $[amount]
   - Most recent: $[amount] on [date]
2. [Merchant 2] — [count] transactions totaling $[amount]
   - Most recent: $[amount] on [date]
...

Follow-up questions (if appropriate):

• For recurring merchants (same merchant, similar amounts): "Would you like me to create a subscription record for [Merchant]?" (if not already tracked)
• For unusual merchants: "What is [Merchant]? Is this something new?"
• For high-frequency merchants: "You shopped at [Merchant] 5 times this week. Would you like to set a spending limit or goal for this category?"

4. Anomaly Detection and Notable Patterns

Run spending_summary() for two consecutive periods (current and previous) to detect anomalies:

Anomaly types to flag:

1. Unusually High Single Transaction: If any single transaction is > 1.5× the category average or > $500 (configurable), flag it:

   ⚠️ Large transaction detected: $[amount] at [Merchant] on [date]
   Is this planned? Should I note this as an unusual expense?
   

2. Category Spending Spike: If a category's spending increased > 50% vs previous period, flag it:

   📈 Spending spike: [Category] increased from $[previous_amount] to $[current_amount]
   What's behind the increase? New subscription? One-time purchase?
   

3. Unusual Merchant: If a transaction is from a merchant not seen before, note it:

   New merchant detected: [Merchant name] ($[amount])
   Is this a new subscription, recurring payment, or one-time?
   

4. Subscriptions Not Tracked: If the same merchant appears multiple times at similar amounts/intervals, suggest creating a subscription record:

   I noticed [Merchant] charged you $[amount] on [dates]. This looks like a recurring subscription.
   Would you like me to track this as "[Merchant] subscription"?
   

Positive patterns to acknowledge:

• "Your spending this week was $[amount], down from $[previous_amount] last week — great job!"
• "You had no transactions in [Category] this period. Keeping up with your goals?"

5. Time-Based Trends (Optional)

If reviewing a longer period (weekly or monthly), offer to show spending by week or day:

Bot: "Would you like to see how your spending breaks down by week?"
User: "Sure"
Bot: [Show spending by week, highlighting any week that stands out]

Use spending_summary(group_by="week") to break down by week, or group_by="day" for daily detail.

6. Summary and Action Items

After presenting all findings, generate a summary:

# Spending Review Summary — [Period]

**Total Spent**: $[amount]
**Top Category**: [Category] ($[amount])
**Transaction Count**: [N]
**Highest Transaction**: $[amount] at [Merchant]

## Highlights
- [Key finding 1]
- [Key finding 2]
- [Key finding 3]

## Action Items
- [Suggested action 1, if applicable]
- [Suggested action 2, if applicable]

## Next Steps
- Would you like to set a spending limit for any category?
- Would you like to create subscription records for recurring charges?
- Should I send you weekly spending summaries?

Suggested actions (only if appropriate):

• "Consider setting a budget for [Category]" (if unusually high)
• "Create subscription record for [Merchant]" (if recurring)
• "Review subscription list and cancel unused services" (if subscriptions are high)
• "Track spending limit for [Category]" (if user expresses concern)

7. Memory Storage

After the user acknowledges or completes the review, optionally store insights:

Bot: "Would you like me to remember any spending patterns or goals from this review?"

If yes, use memory_store_fact() to capture:

# Example: Store spending pattern
memory_store_fact(
    subject="user",
    predicate="spending_pattern",
    content="tends to spend $300-400/week on groceries at Whole Foods and local markets",
    permanence="standard",
    importance=6.0,
    tags=["spending", "groceries", "recurring"]
)

# Example: Store spending concern
memory_store_fact(
    subject="dining",
    predicate="spending_spike",
    content="dining expenses increased from $200 to $350 in [month] — user investigating",
    permanence="volatile",
    importance=5.0,
    tags=["spending", "dining", "anomaly"]
)

# Example: Store spending goal
memory_store_fact(
    subject="user",
    predicate="spending_goal",
    content="wants to keep subscriptions under $50/month total",
    permanence="standard",
    importance=7.0,
    tags=["spending", "goal", "subscriptions"]
)

Adaptive Tips

For daily reviews:

• Keep it light: show top 3 transactions and one anomaly if present
• Focus on "did I spend more today than typical?"
• 2–3 minutes

For weekly reviews:

• Show category breakdown and top merchants
• Highlight any notable patterns (spike, new merchant, subscription suggestion)
• Check subscription trend (are recurring charges increasing?)
• 5–7 minutes

For monthly reviews:

• Full category breakdown with week-by-week trends
• Compare to previous month (if available)
• Review all detected subscriptions and flag any that are new or changed
• Surface top 3 merchants and high-transaction anomalies
• 10–15 minutes

For custom date range reviews:

• Allow any start/end date pair
• Adapt feedback based on range length (e.g., "You spent $X over Y days, averaging $Z/day")

Error Handling

• If no transactions exist for the period: "You don't have any recorded spending for [period]. Would you like to review a different time range?"
• If date range is invalid: "I need a valid date range. Could you clarify the dates?"
• If category lookup fails: Use generic category or merchant name
• If spending_summary times out: Fall back to displaying raw transaction list with manual category grouping

Example Review Flows

Example 1: Quick Daily Review

Bot: "Daily spending review! Today you had 3 transactions totaling $47.32."
- Coffee at Blue Bottle: $5.50
- Lunch at Panera: $14.99
- Groceries at Whole Foods: $26.83

Nothing out of the ordinary. Have a good day!

Example 2: Weekly Review with Spike Detection

Bot: "Here's your weekly spending summary (Jan 15–21):"

Total Spent: $684.50

By Category:
- Groceries: $185.40 (27%) — 6 transactions
- Dining: $156.20 (23%) — 8 transactions
- Transport: $120.00 (18%) — 2 transactions
- Entertainment: $98.50 (14%) — 3 transactions
- Subscriptions: $44.40 (6%) — 4 transactions

Top Merchants:
1. Whole Foods — 4 transactions, $126.30
2. DoorDash — 5 transactions, $89.40
3. Uber — 2 transactions, $120.00

📈 Alert: Your dining spending jumped to $156 this week, up from $89 last week. Special occasion or new routine?

Example 3: Monthly Review with Memory Integration

Bot: "January Spending Summary:"

Total Spent: $2,847.60

By Category:
- Rent: $1,800 (63%)
- Groceries: $520 (18%)
- Dining: $287 (10%)
- Subscriptions: $148 (5%)
- Transport: $92 (3%)

⚠️ Subscription Alert: Netflix ($15.99), Spotify ($12.99), Adobe Cloud ($54.99), and 2 others.
Total subscriptions: $148/month. Same as last month—no new ones.

🎯 Reminder: You mentioned wanting to keep subscriptions under $150. You're right at that level!

Would you like me to continue tracking this goal and alert you if subscriptions exceed $150?

Example 4: Custom Range with Comparison

User: "Show me spending from Feb 1–10"

Bot: "February 1–10 Spending:"

Total: $547.30 (averaging $54.73/day)

By Category:
- Groceries: $185 (34%)
- Dining: $198 (36%)
- Transport: $98 (18%)
- Entertainment: $66 (12%)

Compared to Jan 1–10:
- Groceries: similar ($192)
- Dining: ↑ 23% ($161 → $198)
- Transport: ↓ 15% ($115 → $98)

💡 Your dining spending is trending upward. Anything new going on?

Important Reminders

1. Always have data ready: Confirm transactions exist before starting review
2. Progress gradually: Don't dump all data at once — use progressive disclosure
3. Personalize patterns: Use memory to recall user preferences and previous concerns
4. Proactive anomaly detection: Surface unusual spending without judgment
5. Respect privacy: Keep spending data private; never assume the "why" behind spending
6. Storage discipline: Use memory facts only for patterns the user approves, not every observation

Version History

• v1.0.0 (2026-02-23): Initial skill creation with daily/weekly/monthly review flows, anomaly detection, and memory integration