Lifestyle Creep

Lifestyle Creep Detector

Overview

Compares your spending categories over a 6-12 month period to identify gradual, often unnoticed increases in spending — the classic "lifestyle creep" that erodes savings as income grows. Highlights which categories have drifted upward and by how much.

Wilson Tools Used

• spending_summary — pull category-level spending for multiple months to compare periods

Workflow

1. Run spending_summary for the most recent 3 months to get current average spending by category.
2. Run spending_summary for the 3-month period from 6 months ago (e.g., if now is April 2026, pull October-December 2025) to get the baseline.
3. For each category, calculate:

• Dollar change: current average - baseline average
• Percentage change: (current - baseline) / baseline * 100

1. Flag any category where spending increased by more than 15% AND more than $50/month. These are lifestyle creep candidates.
2. Sort flagged categories by dollar increase descending.
3. Present results:

`` LIFESTYLE CREEP ANALYSIS (6-month comparison) ══════════════════════════════════════════════════════ Category 6mo Ago Now Change % ────────────── ──────── ──────── ─────── ──── Dining Out $280 $420 +$140 +50% !! Shopping $350 $480 +$130 +37% !! Groceries $520 $580 +$60 +12% Entertainment $120 $165 +$45 +38% ! Transportation $200 $195 -$5 -3% ══════════════════════════════════════════════════════ Total Creep: +$370/mo | Annual Impact: +$4,440/yr ``

1. Calculate the total annual impact of all flagged increases.
2. For optional deeper analysis, repeat with a 12-month lookback to separate seasonal patterns from true creep.
3. Suggest a target: "If you returned Dining Out and Shopping to 6-month-ago levels, you would save $3,240/year."

Without Wilson

1. Export 12 months of transactions from your bank as CSV.
2. Open in Google Sheets. Add a "Month" column using =TEXT(A2, "YYYY-MM") where A2 is the transaction date.
3. Create a pivot table: Rows = Category, Columns = Month, Values = SUM of Amount.
4. In a new row below each category, calculate the average of the first 3 months and the last 3 months.
5. Add a "Change" column: =AVERAGE(last 3 months) - AVERAGE(first 3 months).
6. Add a "% Change" column: =Change / ABS(AVERAGE(first 3 months)) * 100.
7. Conditional format: highlight any row where Change > $50 AND % Change > 15% in red.
8. Create a line chart for each flagged category to visually confirm the upward trend (select the monthly totals row, Insert > Chart > Line).
9. Common lifestyle creep categories: dining out, coffee shops, clothing, subscription upgrades, grocery store purchases (premium brands replacing store brands), rideshare instead of transit.

Important Notes

• Not all spending increases are lifestyle creep. Inflation, a new family member, or a necessary expense change are legitimate. Review flagged items in context.
• Seasonal effects can look like creep — holiday spending in Q4, summer travel, back-to-school shopping. The 6-month comparison helps smooth some of this.
• Lifestyle creep is most common after a raise, bonus, or debt payoff. Run this skill within 3 months of any income increase.
• The goal is not to eliminate all increases, but to make them intentional. Spending more on something you value is fine; drifting upward without noticing is the problem.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: openaccountant
• Source: openaccountant/skills
• License: MIT

Install and usage instructions live in the source repository linked above.