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

Workflow

Step 1 — Identify available data sources

Check which connectors are live. Pull from every one that is, in one batch:

1. The ledger — MYOB, NetSuite, QuickBooks, Xero, or Zoho Books, whichever is connected — for AR aging, AP, fixed costs, and the cash balance. Ledgers are peers (../../shared/connector-neutrality.md); if two are connected, ask which is the source of record and take totals from that one only
2. PayPal — transaction history and settlement timing
3. Square — sales and payout history
4. Stripe — charge and payout history
5. Shopify — orders (list-orders) as the inflow, plus payout timing. Shopify on its own is enough to run: for a commerce business it is often the largest inflow. The payout read may fail because the connector's scopes exclude Shopify Payments — then ask the owner for their payout schedule and model from that; never infer a lag (reference/v2_sources.md)
6. CSV upload — when 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.

Always establish the starting cash balance — "will I make payroll" is a question about the balance, not the net. Pull it in Step 2, or ask: "What's in the business account right now, and as of what date?" Never assume one. If nobody knows, head the output "no opening balance — net change only" and drop cash-on-hand from the risk flags.

Step 2 — Pull the data

From the ledger:

• Balance sheet: bank and cash balances with the as-of date — the opening balance (MYOB holds none; see reference/v2_sources.md)
• 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 Gusto, when connected:

• The next payroll run's date and expected amount, and the regular pay-schedule cadence — the real numbers for the biggest fixed cost, instead of inferring payroll from recurring transactions. When Gusto and the ledger disagree on payroll, trust Gusto for timing and amount and say so in the output

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 USD 8,400 invoice out of the 30-day window into day 48."
• Payroll crunch: "Payroll (USD 22,000) hits April 15. Opening balance USD 31,000 on April 1, plus inflows, minus outflows, puts low-band cash on hand April 14 at USD 19,200. Shortfall risk: USD 2,800." Needs a sourced balance.
• 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). Use a markdown table for the forecast, not a fenced code block — the chat surface renders markdown tables; a fenced block shows up as raw monospace text. Shape:

• Title line: Cash Flow Snapshot — <date range>
• Source(s): <connectors used>
• Opening balance: AUD X,XXX as of <date> (or: none on file — net change only)

Then the forecast as a markdown table, amounts carrying the business's currency code (../../shared/currency-and-locale.md), never a bare symbol:

Then risks as a short bulleted list under "⚠ Risks flagged: <count>".

XLSX workbook (always): read xlsx/SKILL.md first, then produce 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.

1. 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.

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

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

The forecast page (always, alongside the chat summary — never instead of it), delivered per the owner's stored output preference — never default to a markdown file. Check the ## Business context block's Output preference (shared style guide rule, ../../shared/artifact-style.md):

• Visual artifact (the default): render the full forecast as an HTML page in the house style. The 30/60/90-day nets are stat tiles with the confidence range as each tile's context line; the transaction detail is a table with right-aligned tabular-nums amounts; each risk flag carries a status pill — critical for a projected shortfall, warn for late-payer and thin-data flags;