Category Trend Analyzer

Category Trend Analyzer

Table of Contents

• Overview
• Input contract
• Workflow
• Outlier rules
• Output contract
• Guardrails

Overview

The point of categorizing transactions is to see, every month, what changed. This skill computes the comparative numbers — current vs. rolling, current vs. budget, current vs. same-month-last-year — for every category, identifies outliers, and surfaces the transactions that drive them. It refuses to flag noise: small categories below a materiality floor are excluded.

Input contract

The caller provides:

• transactions — at least 13 months of categorized transactions.
• budget — budget.json with target per category.
• period_start, period_end — the current period (typically a calendar month).
• materiality_floor_cents — default 5000 ($50). Categories with current spend below this are summarized but not flagged.
• outlier_thresholds — defaults: { rolling_multiple: 1.5, budget_pct: 130 }.

Workflow

Trend Analysis Progress:
- [ ] Step 1: Roll up transactions by category for the current period
- [ ] Step 2: Roll up the prior 6 calendar months by category
- [ ] Step 3: Roll up the same-month-last-year by category
- [ ] Step 4: Compute deltas and percent changes
- [ ] Step 5: Apply outlier rules
- [ ] Step 6: Identify driver transactions for each outlier
- [ ] Step 7: Emit ranked list and totals

Step 1 — Current period totals

For each category and category.subcategory, sum spend (negative amount_cents) over [period_start, period_end]. Exclude:

• income.*
• savings_investment.*
• financial.transfers_internal

These are not spending. They're tracked separately for the cash-flow and savings views.

Step 2 — Rolling 6-month average

For each category, sum spend in each of the prior 6 calendar months, then take the mean. Use calendar month anchoring, not trailing 180 days, so a "monthly" comparison matches user intuition.

If fewer than 3 prior months exist, mark rolling_avg_confidence: low and only flag outliers above the budget threshold (skip the rolling-multiple test).

Step 3 — Year-over-year

Sum the same calendar month, one year prior. If absent (less than a year of data), set yoy_delta_cents: null.

Step 4 — Compute deltas

Step 5 — Outlier rules

A category is an outlier when any of:

• vs_rolling_pct ≥ (rolling_multiple − 1) × 100 (default ≥ 50% above 6-month average) AND current_cents ≥ materiality_floor_cents.
• vs_budget_pct ≥ budget_pct (default ≥ 130% of budget) AND budget exists.
• current_cents ≥ 2 × rolling_avg_cents AND current_cents ≥ materiality_floor_cents → severity high (real-time alert per the spending-analyst spec).

Each outlier carries:

• severity — high (≥ 2× rolling), medium (≥ 1.5× rolling or ≥ 130% budget), low (above materiality but inside thresholds — surfaced for context only).
• reason — which rule fired.

Step 6 — Drivers

For each outlier, identify the top 3–5 transactions ranked by absolute amount, descending. These are the "what drove the outlier" lines for the briefing.

If a single transaction explains > 60% of the outlier, mark it single_driver: true so the analyst can phrase the explanation correctly ("you spent $850 at IKEA — one trip — vs. average of $120 in home maintenance").

Step 7 — Ranking

Rank outliers by dollar_impact_cents = |current_cents − rolling_avg_cents|. Largest dollar movers first; small-percentage-large-dollar matters more than large-percentage-small-dollar.

Outlier rules

Concrete examples:

Output contract

{
  "period": { "start": "2026-04-01", "end": "2026-04-30" },
  "totals": {
    "spend_cents": 624800,
    "spend_cents_vs_rolling": 78400,
    "spend_cents_vs_budget": -25200,
    "categories_above_floor": 18,
    "outliers_count": 3
  },
  "categories": [
    {
      "category": "food",
      "subcategory": "groceries",
      "current_cents": 102000,
      "rolling_avg_cents": 85000,
      "yoy_cents": 92000,
      "budget_cents": 90000,
      "vs_rolling_pct": 20.0,
      "vs_budget_pct": 113.3,
      "vs_yoy_pct": 10.9,
      "severity": "low",
      "outlier": false
    },
    {
      "category": "travel",
      "subcategory": null,
      "current_cents": 340000,
      "rolling_avg_cents": 30000,
      "yoy_cents": 18000,
      "budget_cents": 40000,
      "vs_rolling_pct": 1033.3,
      "vs_budget_pct": 850.0,
      "vs_yoy_pct": 1788.9,
      "severity": "high",
      "outlier": true,
      "reasons": ["above_rolling_multiple_2x", "above_budget_threshold"],
      "drivers": [
        { "tx_id": "tx_20260408_002", "amount_cents": -312000,
          "merchant": "Delta Air Lines", "single_driver": true,
          "share_of_outlier_pct": 91.8 }
      ]
    }
  ],
  "rankings": {
    "biggest_growers": ["travel", "home_maintenance", "restaurants"],
    "biggest_shrinkers": ["entertainment", "transportation.gas"]
  }
}

Guardrails

• Materiality first. Don't flag a $20 hobbies overage. The user's attention is the scarcest resource.
• Don't double-count subcategories and parents. Choose a level — top-level for the headline, subcategory for the drill-down. The spending-analyst agent decides which to show.
• Annual lumpy categories (auto_insurance, property_tax, home_insurance) routinely trigger 10×+ outliers in their billing month. Skip flagging when the spike matches the merchant's annual cadence in recurring.json. Use the seasonal-calendar sub-signal from the spending-analyst agent.
• Negative outliers are not always bad. A category that drops to zero may signal a missed bill (utility autopay broken). Flag both directions, but with different framing.
• Do not classify the cause in this skill ("she got a raise" / "kid started camp"). Surface the data; the analyst agent provides the narrative.