Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add finsilabs/awesome-ecommerce-skills --skill cash-flow-forecastinggit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/cash-flow-forecasting)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/cash-flow-forecasting"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/cash-flow-forecasting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/cash-flow-forecasting"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/cash-flow-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.02566 |
| Opus 5 | $0.00015 | $0.01283 |
| Sonnet 5 | $0.00006 | $0.00513 |
| Haiku 4.5 | $0.00003 | $0.00257 |
Grade A, and why
cash-flow-forecasting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cash Flow Forecasting
Overview
Cash flow forecasting predicts when money actually enters and leaves your bank account — not when revenue is recognized. For ecommerce businesses, cash and revenue timing diverge significantly: payment processors hold funds for days or weeks, inventory must be purchased and paid for weeks before it sells, and marketplace disbursements are bi-weekly or delayed by account reviews.
This skill guides you through building a 13-week rolling cash flow forecast using your platform's data, covering the key cash timing differences by sales channel, and setting up a simple model in a spreadsheet or BI tool that you update weekly.
When to Use This Skill
- When managing cash position and needing to know your runway
- When planning inventory buys and needing to confirm you have the cash to fund them
- When preparing for a fundraise and needing to show investors a 12–18 month cash model
- When stress-testing the business against a revenue shortfall scenario
- When selling on Amazon and managing bi-weekly disbursement timing
- When planning for seasonal cash flow gaps (Q1 trough after Q4 peak)
Core Instructions
Step 1: Pull your historical revenue and expense data from your platform
Before building a forecast, gather 12 months of actuals. Here is where to find the data by platform:
Shopify
- Revenue data: Go to Analytics → Reports → Sales over time — export to CSV; this gives you daily/weekly/monthly gross sales, discounts, returns, and net sales
- Order-level data: Go to Analytics → Reports → Orders over time — shows order count and AOV trends useful for projecting future orders
- Finance summary: Go to Analytics → Finances summary — shows gross sales, discounts, returns, shipping charged, and net sales for any date range
- Payouts: Go to Finances → Payouts — shows exactly when Shopify Payments transferred funds to your bank and the amount; this is your actual cash inflow history, not just revenue recognition
- Export orders: Go to Orders → Export — download all orders with financial data; use this to build a detailed spreadsheet model
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/13-week-cash-model-with-inventory-and-ou/criteria.json 2.8 KB
- evals/13-week-cash-model-with-inventory-and-ou/task.md 2.4 KB
- evals/multi-channel-revenue-to-cash-conversion/criteria.json 2.5 KB
- evals/multi-channel-revenue-to-cash-conversion/task.md 2.5 KB
- evals/seasonal-revenue-forecast-and-cash-runwa/criteria.json 2.6 KB
- evals/seasonal-revenue-forecast-and-cash-runwa/task.md 2.2 KB
- tile.json 315 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 193 lines · 31 tokens per session scan A 86980fda2d57
cash-flow-forecasting is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 2,566 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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