About seasonal-patterns
seasonal-patterns is an analysis skill for finding recurring monthly changes in business revenue and expenses. It builds monthly indexes and highlights peaks and troughs to inform cash, staffing, and marketing plans.
Key features
- 01Analyze monthly income and expenses
- 02Calculate revenue indexes by month
- 03Identify seasonal peaks and troughs
- 04Measure the seasonal range
- 05Suggest timing for cash and spending decisions
Use cases
- 01Finding months with historically high or low revenue
- 02Planning cash reserves around slow periods
- 03Scheduling major expenses around revenue peaks
- 04Adding seasonal trends to cash-flow forecasts
MCP support
The skill describes using Wilson’s `spending_summary` tool to retrieve monthly income and expense totals over a 12-month or longer period.
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ openaccountant/ skills --skill seasonal-patternsAfter installing, type/seasonal-patternsin Claude Code to confirm it loaded.
Details
From the project READMESeasonal Pattern Detection
Overview
Identify cyclical trends in your revenue and expenses over a 12-month or multi-year window. Reveals which months are historically strong or weak, enabling better cash management, staffing, and marketing timing.
Wilson Tools Used
spending_summary— pull monthly income and expense totals across 12+ months to build a seasonal profile
Workflow
- Ask for the analysis window (minimum 12 months, 24+ months preferred for statistical confidence).
- Use
spending_summaryfor each month in the window to get monthly revenue and expense totals. - Calculate the monthly index: Month Index = Month Revenue / Average Monthly Revenue * 100.
- Index > 100 = above-average month
- Index < 100 = below-average month
- Identify peaks and troughs.
- Generate the seasonal profile:
SEASONAL PATTERN ANALYSIS — [Period]
══════════════════════════════════════════════════════════
Month Avg Revenue Index Avg Expenses Trend
──────────────────────────────────────────────────────────
Jan $8,200 82 $6,100 Slow start
Feb $7,800 78 $5,900 ▼ Trough
Mar $9,500 95 $6,500 Recovering
Apr $11,200 112 $7,200 ▲ Above avg
May $12,500 125 $7,800 ▲ Strong
Jun $13,000 130 $8,200 ▲▲ Peak
Jul $11,800 118 $7,500 ▲ Above avg
Aug $10,200 102 $7,000 Average
Sep $10,500 105 $7,200 Average
Oct $11,000 110 $7,500 ▲ Above avg
Nov $9,500 95 $8,500 Expense spike
Dec $6,800 68 $9,000 ▼▼ Low + high cost
──────────────────────────────────────────────────────────
Annual $122,000 100 $88,400
Peak Month: June (130) Trough Month: December (68)
Seasonal Range: 62 points
══════════════════════════════════════════════════════════
- Calculate the seasonal range (peak index - trough index). Over 50 points indicates significant seasonality.
- Provide actionable recommendations:
- Build cash reserves during peak months for trough months
- Time major expenses (equipment, marketing campaigns) for peak-revenue months
- Consider seasonal pricing or promotions during slow months
Without Wilson
- Export 12-24 months of transactions from your bank as CSV.
- In a spreadsheet, add a
Monthcolumn:=TEXT(Date,"YYYY-MM")and aMonthNumcolumn:=MONTH(Date). - Create a pivot table: Rows = MonthNum, Values = Average of Income, Average of Expenses (use separate columns for multi-year averaging).
- Overall Average:
=AVERAGE(AllMonthlyRevenues). - Seasonal Index per month:
=MonthAvgRevenue/OverallAvg*100. - In Google Sheets, create a line chart of monthly revenue with a trendline to visualize the pattern.
- For statistical detection, use Excel's
=FORECAST.ETS.SEASONALITY(values, timeline)to auto-detect the seasonal period length. - Google Sheets alternative: chart the data and add a polynomial trendline (order 4-6) to see the seasonal curve.
Important Notes
- You need at least 12 months of data for meaningful seasonal analysis. With only one year, you cannot distinguish seasonal patterns from one-time events. Two or more years dramatically improves confidence.
- Not every business is seasonal. If your seasonal range is under 20 points, your revenue is relatively stable month-to-month.
- Expense seasonality matters too. Insurance renewals, annual software subscriptions, and quarterly tax payments create predictable expense spikes.
- Overlay seasonal patterns onto your cash flow forecast for more accurate projections.
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