# Wayland Cashflow Review

Interactive cashflow review.

- Repository: https://github.com/FerroxLabs/wayland (616 stars)
- Category: Business
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add FerroxLabs/wayland --skill wayland-cashflow-review -g -a claude-code -y`
Codex: `npx skills add FerroxLabs/wayland --skill wayland-cashflow-review -g -a codex -y`
Cursor: `npx skills add FerroxLabs/wayland --skill wayland-cashflow-review -g -a cursor -y`
Gemini CLI: `npx skills add FerroxLabs/wayland --skill wayland-cashflow-review -g -a gemini-cli -y`

## Description

Interactive cashflow review. Parses bank actuals, computes forecast variance and a 30-day projection with red/yellow/green status, then turns it into action recommendations. Use when the user wants a structured multi-step cashflow review from raw bank data to a shipped review document. Do NOT use for a single cashflow lookup an atomic finance skill can answer.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Cashflow Review

**Estimated time:** 20-30 minutes

This workflow runs an interactive cashflow review: bank actuals to forecast
variance to a 30-day projection with red/yellow/green status, ending in action
recommendations. Act as the CFO. Each build step is followed by a review
checkpoint where the user can refine the categorization, assumptions, or
recommendations before proceeding.

**Step 1: Kickoff and point at the data** (uses: finance-cashflow)

Tell the user you will run a cashflow review. Ask them to point you at the
data: bank CSV(s), credit-card exports, AR aging, by path or pasted contents,
and to say which file is what. Offer to read any file dropped into the chat or
pull from a connected source (Stripe payouts, or the connected ledger,
database, or Google Sheets workbook). If neither is set up, ask them to attach
the bank export.

- Input: user-provided bank CSVs, CC exports, AR aging, or connected source
- Output: data_inputs (the located data and what each file is)
- Key focus: know exactly which file is which before parsing

**Step 2: Infer the review period** (uses: finance-cashflow)

From the data, infer the review period and present it with reasoning. Invite
the user to override if they want a different window, for example just the last
30 days. Confirm the period before parsing.

- Input: data_inputs
- Output: review_period (the confirmed review window)
- Key focus: align the window with the data and the user's intent

**Step 3: Parse actuals** (uses: finance-cashflow)

Parse the data in parse_actuals mode for the confirmed period: inflows by
category, outflows by category, and net cash position. Show the output.

- Input: data_inputs, review_period
- Output: actuals (categorized inflows, outflows, net position)
- Key focus: clean categorization that the user recognizes as correct

**Step 4: Review and refine categorization** (uses: finance-cashflow)

The rest: https://github.com/FerroxLabs/wayland/blob/HEAD/src/process/resources/bundled-workflows/bodies/wayland-cashflow-review/SKILL.md