Cash Flow Snapshot

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.

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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

Chat summary (always):

Cash Flow Snapshot — [date range]
Source(s): [connectors used]

            Expected    Low       High
30-day net: $X,XXX     $X,XXX    $X,XXX
60-day net: $X,XXX     $X,XXX    $X,XXX
90-day net: $X,XXX     $X,XXX    $X,XXX

⚠ Risks flagged: [count]
  • [risk 1]
  • [risk 2]
  ...

XLSX workbook (always): Read xlsx/SKILL.md before generating. Produce a workbook with three sheets:

1. Summary — the 30/60/90 forecast table with confidence bands. Beneath each window row, expand inline sub-rows showing the individual transactions that make up its inflows (green) and outflows (red). This makes the estimates auditable without leaving the Summary sheet.

2. Detail — all transactions grouped by window, sorted by date within each group. Include a running net column (cumulative inflows minus outflows within the window) and a subtotal row at the bottom of each window showing total inflows, total outflows, and net. Grey out past transactions in a separate section at the bottom for reference. Ensure all three windows have rows even if one is empty — show a "No transactions in this window" placeholder row.

3. Risks — the flagged risks with dollar impact and affected window.

Save as cash-flow-snapshot-[YYYY-MM-DD].xlsx.

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Approval gates

No destructive actions — this skill is read-only. No approval gate required before generating the forecast.

Remind the user after delivery:

"This forecast is based on [sources listed]. It is not a substitute for

accounting advice — verify with your bookkeeper before making financing decisions."

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Reference files

Worked example — cash-flow-snapshot

Scenario: Small services business. QuickBooks + PayPal connected. Three active customers, monthly payroll, office rent.

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Input data (pulled from connectors)

AR aging (QuickBooks):

Historical payment lag (from PayPal settlements):

Fixed costs (QuickBooks recurring AP):

• Payroll: $22,000 — hits April 15
• Rent: $3,200 — hits May 1
• Software subscriptions: $480 — hits May 1

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Step 3 output — adjusted inflow dates

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Step 4 output — 30/60/90 forecast

Confidence band calculation:

• Weighted avg stddev: 3.6 days
• Weighted avg mean lag: 12.7 days
• band_pct = 3.6 / 12.7 = 28.3%

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Step 5 output — risks flagged

1. Payroll crunch: Payroll ($22,000) hits April 15. Low-band inflows through April 14: $0 (both AR receipts fall April 28–29). Shortfall risk: up to $22,000. Recommend: confirm receivables timing with Acme and BlueSky before April 14.

2. Late-payer risk: Acme Corp historically pays 18 days late. Their $8,400 invoice (due Apr 10) shifts to April 28 — after payroll.

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Step 6 output — chat summary

Cash Flow Snapshot — Apr 23 → Jul 21, 2026
Sources: QuickBooks, PayPal

              Expected    Low        High
30-day net:   +$600      −$5,928    +$7,128
60-day net:   +$2,320    +$1,670    +$2,970
90-day net:   $0         —          —

⚠ 2 risks flagged:
  • Payroll crunch: $22K payroll hits Apr 15; AR receipts don't clear until
    Apr 28–29. Low-band shortfall risk: up to $22,000.
  • Late-payer: Acme Corp (mean 18-day lag) shifts $8,400 past payroll date.

Confidence band: ±28% (based on historical payment variance across 3 customers).

This forecast is based on QuickBooks AR/AP and PayPal settlement history.
It is not a substitute for accounting advice — verify with your bookkeeper
before making financing decisions.

XLSX: cash-flow-snapshot-2026-04-23.xlsx — Summary / Detail / Risks sheets.

Gotchas — cash-flow-snapshot

Known edge cases and connector failure modes. 2–5 entries, Good/Bad format.

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1. QuickBooks AR aging includes invoices already collected

Bad: Including fully-paid invoices from the AR aging report inflates inflow projections. QuickBooks sometimes shows $0-balance invoices in aging exports.

Good: Filter AR rows to balance_due > 0 before computing inflows. If the connector doesn't expose balance_due, subtract known PayPal/Stripe settlements from the invoice total before including it.

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2. PayPal settlement lag varies by transaction type

Bad: Assuming all PayPal receipts settle in 1–2 business days. PayPal holds funds differently for disputes, new sellers, and high-value transactions — using a flat settlement assumption produces overconfident inflow timing.

Good: Compute settlement lag from actual transaction_date → completed_date pairs in the PayPal transaction history. Use the computed mean and stddev per customer or transaction type.

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3. CSV column names are inconsistent across accounting exports

Bad: Requiring exact column names like "Date", "Amount", "Type". QuickBooks CSV exports use "Transaction Date", "Amount", "Transaction Type". Wave uses "Date", "Amount", "Account Type". Rigid parsing fails silently.

Good: Fuzzy-match column headers (date → transaction date → txn date; amount → debit/credit; type → category → account type). Show the header row to the user and confirm mapping before computing — one question beats a silent wrong forecast.

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4. Fixed costs hidden in one-off AP entries

Bad: Only pulling recurring line items labeled as "recurring" in QuickBooks. Many SMBs don't tag fixed costs consistently — rent may appear as a one-off vendor bill each month.

Good: Look for AP entries that appear in 3+ consecutive months with the same vendor and similar amount (±10%). Treat these as recurring fixed costs in the forecast. Surface the list to the user: "I'm treating these as fixed monthly costs — does that look right?"

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5. Confidence band formula breaks when mean payment lag is zero

Bad: Dividing stddev by a mean lag of 0 (e.g. immediate payment customers like Square POS) produces a divide-by-zero error or an infinite band.

Good: If mean lag ≤ 1 day, set band_pct to 5% (low variance, near-immediate settlement). Don't attempt the division.