zhukang
1c7abd81d1
- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
170 lines
6.4 KiB
Markdown
170 lines
6.4 KiB
Markdown
---
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name: cash-flow-snapshot
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description: >
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Reads AR/AP, historical cash timing, and known fixed costs from QuickBooks,
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PayPal, Stripe, or Square — or a CSV upload — and produces a 30/60/90-day
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cash flow forecast with percentage-variance confidence bands and named risk
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flags. Delivers a chat summary and a downloadable XLSX. Use when the user
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asks "forecast my cash flow," "will I make payroll," mentions "runway," or
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says "cash crunch." Falls back to CSV upload when no connector is live.
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compatibility: "Requires one or more of: QuickBooks MCP, PayPal MCP, Stripe MCP, Square MCP, file upload (CSV fallback). Output uses xlsx skill."
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---
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# Cash Flow Snapshot
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Produces a 30/60/90-day cash flow forecast with percentage-variance confidence
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bands and named risk flags. Delivers a two-part output: a concise chat summary
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and a downloadable XLSX workbook.
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**Quick start**
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> "Will I make payroll next month?"
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Claude pulls AR/AP and fixed costs from connected sources, calculates expected
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inflows and outflows across 30, 60, and 90-day windows, applies confidence
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bands based on each customer's historical payment variance, and flags specific
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risks by name.
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---
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## Workflow
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### Step 1 — Identify available data sources
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Check which connectors are live. Try in this order:
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1. QuickBooks — primary source for AR aging, AP, and fixed costs
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2. PayPal — transaction history and settlement timing
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3. Stripe — charge and payout history
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4. Square — sales and payout history
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5. CSV upload — fallback if no connector is connected
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If no connector is live and no file is attached, ask the user to either connect
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a source or upload a CSV (income/expense tabular data, any reasonable format).
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Note which sources were used in the output — this affects confidence band width.
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### Step 2 — Pull the data
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**From QuickBooks:**
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- AR aging report: customer name, invoice amount, invoice date, due date, days outstanding
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- AP: vendor name, amount due, due date
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- Recurring fixed costs: rent, payroll, subscriptions (look for recurring transactions)
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**From PayPal / Stripe / Square:**
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- Settlement history: transaction date, amount, settlement date
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- Use settlement lag (transaction date → payout date) to compute each source's
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average and variance payment delay
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**From CSV upload:**
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- Parse as income/expense tabular data
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- Required columns (flexible naming): date, amount, type (income or expense), description
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- If columns are ambiguous, show the header row and ask the user to confirm mapping
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### Step 3 — Compute historical payment timing
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For each AR customer (or income source from CSV), calculate:
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- **Mean payment lag** — average days from invoice/transaction date to receipt
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- **Payment variance** — standard deviation of payment lag across last 6–12 payments
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- Use variance to set confidence band width (see Step 4)
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If fewer than 3 payments exist for a customer, use the population mean as the
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point estimate and apply a ±30% variance band as the default. When running on
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CSV data with sufficient history (≥3 payments per source), compute the band
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from the actual payment variance — do not assume ±30%.
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### Step 4 — Build the 30/60/90-day forecast
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Produce three time windows: 0–30 days, 31–60 days, 61–90 days.
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For each window, compute:
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| Line | Method |
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|---|---|
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| Expected inflows | AR due in window, adjusted for mean payment lag |
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| Expected outflows | AP due in window + fixed costs falling in window |
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| Net cash position | Inflows − Outflows |
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| Confidence band | ± weighted average payment variance as a % of expected inflows |
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Confidence band formula:
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```
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band_pct = weighted_avg_stddev_days / avg_payment_lag_days
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low = net_cash × (1 − band_pct)
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high = net_cash × (1 + band_pct)
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```
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Round band_pct to one decimal place. Cap at ±50% — higher variance means the
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data is too thin to model; flag it instead (see Step 5).
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### Step 5 — Flag named risks
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Scan for conditions that push the low-band estimate negative or create a
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liquidity crunch. For each risk found, produce a one-line flag:
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- **Late-payer risk:** "Customer X historically pays 18 days late; that shifts
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their $8,400 invoice out of the 30-day window into day 48."
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- **Payroll crunch:** "Payroll ($22,000) hits April 15. Low-band cash on hand
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April 14: $19,200. Shortfall risk: $2,800."
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- **Thin data warning:** "Only 2 payments on record for Customer Y — confidence
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band set to default ±30%."
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- **No-connector warning:** "Running on CSV data only — no real-time AP or
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recurring cost data. Confidence bands are wider than normal."
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Limit to the top 5 risks by severity (largest dollar impact first).
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### Step 6 — Deliver outputs
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**Chat summary** (always):
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```
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Cash Flow Snapshot — [date range]
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Source(s): [connectors used]
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Expected Low High
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30-day net: $X,XXX $X,XXX $X,XXX
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60-day net: $X,XXX $X,XXX $X,XXX
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90-day net: $X,XXX $X,XXX $X,XXX
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⚠ Risks flagged: [count]
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• [risk 1]
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• [risk 2]
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...
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```
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**XLSX workbook** (always):
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Read `xlsx/SKILL.md` before generating. Produce a workbook with three sheets:
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1. **Summary** — the 30/60/90 forecast table with confidence bands. Beneath
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each window row, expand inline sub-rows showing the individual transactions
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that make up its inflows (green) and outflows (red). This makes the estimates
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auditable without leaving the Summary sheet.
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2. **Detail** — all transactions grouped by window, sorted by date within each
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group. Include a running net column (cumulative inflows minus outflows within
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the window) and a subtotal row at the bottom of each window showing total
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inflows, total outflows, and net. Grey out past transactions in a separate
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section at the bottom for reference. Ensure all three windows have rows even
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if one is empty — show a "No transactions in this window" placeholder row.
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3. **Risks** — the flagged risks with dollar impact and affected window.
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Save as `cash-flow-snapshot-[YYYY-MM-DD].xlsx`.
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---
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## Approval gates
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No destructive actions — this skill is read-only. No approval gate required
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before generating the forecast.
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Remind the user after delivery:
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> "This forecast is based on [sources listed]. It is not a substitute for
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> accounting advice — verify with your bookkeeper before making financing decisions."
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---
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## Reference files
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| File | Load when |
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|---|---|
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| `reference/gotchas.md` | When a connector returns unexpected data or variance is extreme |
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| `reference/examples/worked-example.md` | When modeling the output format for a new data shape |
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