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:

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:

| 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 `reference/xlsx-helper.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`.

---

## 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 |