cash-flow-snapshot
GitHub生成30/60/90天现金流预测，包含置信区间和风险预警。支持QuickBooks、支付网关或CSV数据，回答发薪、跑道等资金问题。
Trigger Scenarios
Install
npx skills add anthropics/knowledge-work-plugins --skill cash-flow-snapshot -g -y
SKILL.md
Frontmatter
{
"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.\n",
"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 |
Version History
- 6f13415 Current 2026-07-05 18:35
