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; sources and opening balance sit in a small header panel.
• docx / md / notion / canva preference: deliver the same content in that form — a DOCX or markdown file, a Notion page created via the connector (named destination, never overwriting), or a Canva Doc created via the Canva connector (a new design each run, named with the date; tables become lists); fall back to the visual artifact if Notion or Canva is not connected — and say that is why. The XLSX still ships alongside.
• Best for skill: use the visual artifact — a forecast is read at a glance and re-run often.

After the run

One line on what just happened: the forecast is built and the risks are named. Then offer the single most relevant next step, plus at most two others nearby:

• If a payroll crunch was flagged: "can I make payroll" runs /plan-payroll.
• "Who owes me money?" runs invoice-chase to pull collections forward.
• "Close the month" runs /close-month so next forecast runs on clean books.

Max three offers, and never repeat an offer the owner declined this session.

Approval gates

This skill is read-only — no approval gate 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."

More sources, and a schedule

Read reference/v2_sources.md for the full mapping:

• Shopify — payments inflow timing, which is often the largest single inflow for a commerce business and settles on a delay worth modeling
• NetSuite — a ledger of record, common at the larger end of the segment: AR, AP, fixed costs, and the cash balance via reports and SuiteQL
• Xero — a ledger of record: aged receivables, bills, bank balances, and the organisation's currency and financial year
• Ramp — card spend that hasn't hit the books yet, which is the most common reason a forecast is quietly optimistic
• Ramp balances — real business and treasury account balances with history. When the owner banks with Ramp this is a sourced opening balance rather than a number they had to remember
• MYOB — AR and AP legs for MYOB shops. Read-only, and it holds no bank or cash balances at all — never source an opening balance from it

Nothing about the forecast logic changes. These are additional legs into the same model.

Running on a schedule

This skill is schedulable. The monthly preset is a cash heads-up before the month turns.

Offer it once, after a forecast the owner found useful:

Want this monthly, a few days before month end? That's when it's most useful,
and it's the same forecast you just got.

Scheduling is a property of this skill. There is no separate command for it.

QuickBooks summary fields lie. Read ../../shared/quickbooks-report-traps.md before quoting any total from a QuickBooks summary object: the AP aging summary nets vendor credits into its buckets and can show a negative overdue figure, and the P&L summary can report expenses as zero against real rows. Total the rows, or use the detail call.

Reference files