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 erphq/skills --skill demand-planninggit clone --depth 1 https://github.com/erphq/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/erphq/skills/demand-planning)<a href="https://agentmods.dev/skills/erphq/skills/demand-planning"><img src="https://agentmods.dev/badge/skills/erphq/skills/demand-planning.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.04395 |
| Opus 5 | $0.00017 | $0.02197 |
| Sonnet 5 | $0.00007 | $0.00879 |
| Haiku 4.5 | $0.00003 | $0.00439 |
Grade A, and why
demand-planning 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 7d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Planning
Size-Tier Scope
This variant scales the operating pattern for organizations of 100 to 1,000 people. Use it when the app needs formal ownership, repeatable approvals, role-scoped reporting, and practical automation without the full governance weight of a global enterprise rollout.
What This Process Does
Demand planning is about predicting the future — specifically, how much of each product or material your business will need and when. Get it right and you have the right inventory on hand, your production runs smoothly, your suppliers deliver on time, and your customers get what they want. Get it wrong and you either have too much (wasted money, expired products, clearance sales) or too little (lost sales, unhappy customers, production shutdowns).
It combines historical data (what happened before), market intelligence (what is changing), and business plans (what you are trying to do) into a forecast. That forecast then drives everything downstream — how much to buy, how much to make, how many people to staff, and how much warehouse space you need.
This process also covers safety stock (the buffer you keep just in case), MRP (material requirements planning — translating demand for finished products into demand for components), production scheduling, and S&OP (sales and operations planning — getting your leadership team aligned on one plan).
Start Here: ERP•AI Templates
Before building anything from scratch, check ERP•AI's template library. Look for the Demand Forecasting app, the Safety Stock Calculator, the MRP Planner, and the S&OP Dashboard templates. ERP•AI's catalog of 720+ apps includes planning tools that range from simple moving-average forecasts to multi-variable demand models. Deploy the template that matches your planning maturity and customize the data inputs, forecast horizons, and review cadences.
Build — Setting It Up
With Agents
AI agents transform demand planning from a spreadsheet exercise into a continuous, intelligent process:
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.
- 7d ago First seen · 216 lines · 34 tokens per session scan A 82e40bc28b7f
demand-planning is a skill published in the GitHub repository erphq/skills (2 stars, last pushed 18d ago), licensed MIT. It adds 34 tokens to every session and 4,395 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 2026-08-31.
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