cash-flow-snapshot

name cash-flow-snapshot description Reads AR/AP, historical cash timing, and known fixed costs from QuickBooks, PayPal, Stripe, or Square — or a CSV upload — and produces a 30/60/90-day cash flow forecast with percentage-variance confidence bands and named risk flags. Delivers a chat summary and a downloadable XLSX. Use when the user asks "forecast my cash flow," "will I make payroll," mentions "runway," or says "cash crunch." Falls back to CSV upload when no connector is live. compatibility Requires one or more of: QuickBooks MCP, PayPal MCP, Stripe MCP, Square MCP, file upload (CSV fallback). Output uses xlsx skill. 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: QuickBooks — primary source for AR aging, AP, and fixed costs PayPal — transaction history and settlement timing Stripe — charge and payout history Square — sales and payout history 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: Line Method Expected inflows AR due in window, adjusted for mean payment lag Expected outflows AP due in window + fixed costs falling in window Net cash position Inflows − Outflows Confidence band ± weighted average payment variance as a % of expected inflows 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: 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. 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. Risks — the flagged risks with dollar impact and affected window. Save as cash-flow-snapshot-[YYYY-MM-DD].xlsx . 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." Reference files File Load when reference/gotchas.md When a connector returns unexpected data or variance is extreme reference/examples/worked-example.md When modeling the output format for a new data shape