Cash Flow Snapshot

Produces a 30/60/90-day cash flow forecast with percentage-variance confidence bands and named risk flags. Delivers a two-part output: a concise chat summary and a downloadable XLSX workbook.

Quick start

"Will I make payroll next month?"

Claude pulls AR/AP and fixed costs from connected sources, calculates expected inflows and outflows across 30, 60, and 90-day windows, applies confidence bands based on each customer's historical payment variance, and flags specific risks by name.

Workflow

Step 1 — Identify available data sources

Check which connectors are live. Try in this order:

1. QuickBooks — primary source for AR aging, AP, and fixed costs
2. PayPal — transaction history and settlement timing
3. Stripe — charge and payout history
4. Square — sales and payout history
5. CSV upload — fallback if no connector is connected

If no connector is live and no file is attached, ask the user to either connect a source or upload a CSV (income/expense tabular data, any reasonable format). Note which sources were used in the output — this affects confidence band width.

Step 2 — Pull the data

From QuickBooks:

• AR aging report: customer name, invoice amount, invoice date, due date, days outstanding
• AP: vendor name, amount due, due date
• Recurring fixed costs: rent, payroll, subscriptions (look for recurring transactions)

From PayPal / Stripe / Square:

• Settlement history: transaction date, amount, settlement date
• Use settlement lag (transaction date → payout date) to compute each source's average and variance payment delay

From CSV upload:

• Parse as income/expense tabular data
• Required columns (flexible naming): date, amount, type (income or expense), description
• If columns are ambiguous, show the header row and ask the user to confirm mapping

Step 3 — Compute historical payment timing

For each AR customer (or income source from CSV), calculate:

• Mean payment lag — average days from invoice/transaction date to receipt
• Payment variance — standard deviation of payment lag across last 6–12 payments
• Use variance to set confidence band width (see Step 4)

If fewer than 3 payments exist for a customer, use the population mean as the point estimate and apply a ±30% variance band as the default. When running on CSV data with sufficient history (≥3 payments per source), compute the band from the actual payment variance — do not assume ±30%.

Step 4 — Build the 30/60/90-day forecast

Produce three time windows: 0–30 days, 31–60 days, 61–90 days.

For each window, compute:

Confidence band formula:

band_pct = weighted_avg_stddev_days / avg_payment_lag_days
low  = net_cash × (1 − band_pct)
high = net_cash × (1 + band_pct)

Round band_pct to one decimal place. Cap at ±50% — higher variance means the data is too thin to model; flag it instead (see Step 5).

Step 5 — Flag named risks

Scan for conditions that push the low-band estimate negative or create a liquidity crunch. For each risk found, produce a one-line flag:

• Late-payer risk: "Customer X historically pays 18 days late; that shifts their $8,400 invoice out of the 30-day window into day 48."
• Payroll crunch: "Payroll ($22,000) hits April 15. Low-band cash on hand April 14: $19,200. Shortfall risk: $2,800."
• Thin data warning: "Only 2 payments on record for Customer Y — confidence band set to default ±30%."
• No-connector warning: "Running on CSV data only — no real-time AP or recurring cost data. Confidence bands are wider than normal."

Limit to the top 5 risks by severity (largest dollar impact first).

Step 6 — Deliver outputs

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